U.S. patent application number 14/593262 was filed with the patent office on 2016-06-23 for method and apparatus for interference suppression in orthogonal frequency division multiplexed (ofdm) wireless communication systems.
The applicant listed for this patent is Tellabs Operations, Inc.. Invention is credited to Samir Kapoor, Daniel J. Marchok.
Application Number | 20160181693 14/593262 |
Document ID | / |
Family ID | 22313783 |
Filed Date | 2016-06-23 |
United States Patent
Application |
20160181693 |
Kind Code |
A1 |
Kapoor; Samir ; et
al. |
June 23, 2016 |
Method And Apparatus For Interference Suppression In Orthogonal
Frequency Division Multiplexed (OFDM) Wireless Communication
Systems
Abstract
A method and apparatus for interference suppression in wireless
communication systems, especially Orthogonal Frequency Division
Multiplexed (OFDM) systems, is presented. The array apparatus
includes a two-tier adaptive array system, which provides for both
spatial diversity and beamforming at the uplink and includes
sub-arrays spaced at a distance sufficient to provide spatial
diversity and support beamforming or scanning A Direction of
Arrival (DOA) of signals impinging upon the array can be calculated
by comparing signals from sub-array elements. Each sub-array can be
filtered or beamformed to provide high gain to desired signals
received from the DOA (which may be a multipath signal) while
simultaneously dampening-out undesired signals, such as co-channel
interference (CCI) in the frequency band of operation. The DOA is
also used for allocating frequency bins for data signals, such as
in an OFDM system, to provide weighted guidelines for bin
allocation to maximize received signal power.
Inventors: |
Kapoor; Samir; (Voorhees,
NJ) ; Marchok; Daniel J.; (Princeton, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Tellabs Operations, Inc. |
Naperville |
IL |
US |
|
|
Family ID: |
22313783 |
Appl. No.: |
14/593262 |
Filed: |
January 9, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13269336 |
Oct 7, 2011 |
8934457 |
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14593262 |
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09975518 |
Oct 11, 2001 |
8050288 |
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13269336 |
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09106884 |
Jun 30, 1998 |
6795424 |
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09975518 |
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Current U.S.
Class: |
455/562.1 |
Current CPC
Class: |
H01Q 1/246 20130101;
H04W 88/08 20130101; H04W 16/24 20130101; H04L 27/2626 20130101;
H01Q 3/2611 20130101; H04B 7/086 20130101 |
International
Class: |
H01Q 3/26 20060101
H01Q003/26; H01Q 1/24 20060101 H01Q001/24; H04B 7/08 20060101
H04B007/08 |
Claims
1. (canceled)
2. An apparatus for processing signals received from a plurality of
remote transceivers, the apparatus comprising: at least two
beamformers, each beamformer being associated with a corresponding
antenna sub-array having at least two antenna elements, and being
configured to: receive at least two base signals associated with at
least two carrier signals corresponding to a frequency bin
allocated to a user, the at least two carrier signals being
associated with the at least two antenna elements of the
corresponding antenna sub-array; and generate an output signal
based on the at least two base signals and directions of arrival of
the at least two carrier signals; and a spatial diversity combiner
configured to selectively combine at least two output signals of
the at least two beamformers.
3. The apparatus as recited in claim 2 further comprising a
direction of arrival estimator configured to estimate directions of
arrival of the at least two carrier signals.
4. The apparatus as recited in claim 2, wherein the spatial
diversity combiner is selected from a group consisting of: a
maximal ratio combiner, a maximum signal to interference and noise
ratio (SINR) optimum combiner, and a switched diversity
combiner.
5. The apparatus as recited in claim 2, wherein the at least two
beamformers and the spatial diversity combiner are implemented via
a general purpose processor, an application-specific processor, an
application-specific integrated circuit, general purpose processor
executing application specific software, or a combination
thereof.
6. An apparatus for processing signals received from a plurality of
remote transceivers, the apparatus comprising: at least two
beamformers, each beamformer being configured to adjust gains
applied to at least two base signals, associated with at least two
carrier signals, based on directions of arrival of the at least two
carrier signals, and to produce at least one output signal; a
spatial diversity combiner configured to selectively combine at
least two output signals of the at least two beamformers; and a
processor configured to: monitor information related to a plurality
of frequency bins; and assign signals associated with a remote
transceiver to frequency bins based on at least one of association
with a remote transceiver and direction of arrival.
7. The apparatus as recited in claim 6, wherein the processor is
further configured to assign the signals to neighboring frequency
bins in a manner that reduces differences in power between the
assigned signals:
8. The apparatus as recited in claim 6, wherein the processor is
further configured to assign the signals in a manner to avoid
having a co-channel interfering signal in a neighboring frequency
bin.
9. The apparatus as recited in claim 6, wherein the processor is
further configured to assign the signals based on an indication of
neighboring frequency bins including at least one of the following:
an adjacent frequency bin and an approximately adjacent frequency
bin.
10. A method for processing signals received from a plurality of
remote transceivers, the method comprising, in a receiver system
including a spatial diversity combiner and at least two
beamformers: receiving a plurality of base signals from at least
two antenna sub-arrays each having at least two antenna elements,
the plurality of base signals associated with at least two carrier
signals corresponding to a frequency bin allocated to a user, the
at least two carrier signals being associated with the at least two
antenna elements of the corresponding antenna sub-array; receiving
the plurality of base signals to one of the at least two antenna
sub-arrays by an associated one of the at least two beamformers;
receiving the plurality of base signals to another of the at least
two antenna sub-arrays by an associated one of the at least two
beamformers; generating, by each beamformer of the at least two
beamformers, an output signal based on the at least two of the
plurality of base signals and directions of arrival of the at least
two corresponding carrier signals; and spatial diversity combining
the output signals of the at least two beamformers.
11. The method as recited in claim 10, further comprising
estimating directions of arrival of the at least two carrier
signals.
12. The method as recited in claim 10, wherein spatial diversity
combining is selected from a group consisting of: maximal ratio
combining, maximum signal to interference and noise ratio (SINR)
optimum combining, and switched diversity combining.
13. A method for processing signals received from a plurality of
remote transceivers, the method comprising: forming signal beams
using base signals, each signal beam being formed using at least
two base signals, associated with at least two carrier signals,
based on directions of arrival of the at least two carrier signals;
spatial diversity combining at least two of the formed signal
beams; monitoring information related to a plurality of frequency
bins; and assigning signals associated with a remote transceiver to
frequency bins based on at least one of association with a remote
transceiver and direction of arrival.
14. The method as recited in claim 11, wherein assigning the
signals further includes: assignment of signals to neighboring
frequency bins is performed in a manner that reduces differences in
power between the assigned signals.
15. The method as recited in claim 11, wherein assigning the
signals further includes: assignment of signals to frequency bins
is performed in a manner that avoids having a co-channel
interfering signal in a neighboring frequency bin.
16. The method as recited in claim 11, wherein assigning the
signals is based on an indication of neighboring frequency bins
being at least of the following: an adjacent frequency bin and an
approximately adjacent frequency bin.
17. A non-transitory computer readable medium including computer
software for processing signals received from a plurality of remote
transceivers, the computer software when executed by a processor
causes an apparatus to: receive at least two base signals
associated with at least two carrier signals corresponding to a
frequency bin allocated to a user, the at least two carrier signals
being associated with at least two antenna elements of a
corresponding antenna sub-array; and generate an output signal
based on the at least two base signals and based on directions of
arrival of the at least two carrier signals; and spatial diversity
combine the output signals of at least two beamformers.
18. The non-transitory computer readable medium as recited in claim
17, wherein the computer software when executed by the processor
further causes the apparatus to estimate directions of arrival of
the at least two carrier signals.
19. The non-transitory computer readable medium as recited in claim
17, wherein the computer software when executed by the processor
further causes the apparatus to spatial diversity combine the
output signals based on maximal ratio combining, maximum signal to
interference and noise ratio (SINR) optimum combining, or switched
diversity combining.
20. The non-transitory computer readable medium as recited in claim
17, wherein the computer software, when executed by the processor,
further causes the apparatus to: monitor information related to a
plurality of frequency bins; and assign signals associated with a
remote transceiver to frequency bins according to at least the
following criteria: (1) signals associated with a remote
transceiver are assigned frequency bins that are separated apart in
the frequency domain and (2) signals assigned to neighboring
frequency bins that have substantially different directions of
arrival.
21. The non-transitory computer readable medium as recited in claim
20, wherein the criteria further include: assignment of signals to
neighboring frequency bins is performed in a manner that reduces
differences in power between the assigned signals.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 13/269,336, filed Oct. 7, 2011, which is a is a continuation of
U.S. application Ser. No. 09/975,518, filed Oct. 11, 2001, now U.S.
Pat. No. 8,050,288, issued Nov. 1, 2011, which is a continuation of
Ser. No. 09/106,884, filed Jun. 30, 1998, now U.S. Pat. No.
6,795,424, issued Sep. 21, 2004. The entire teachings of the above
applications are incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0002] In the last few years, the number of commercial cellular
telephone users has risen dramatically, but the bandwidth allocated
to cellular telephony has remained nearly constant. Because of the
limited nature of cellular telephony bandwidth as a resource, the
cost of obtaining bandwidth has risen dramatically. This
necessitates the efficient utilization of available bandwidth
resources to maintain commercial viability.
[0003] Many intelligent schemes for optimizing the use of available
bandwidth resources have been proposed. These methods include such
means as signal compression or elimination of non-essential
frequency artifacts to reduce the overall bandwidth. Other systems
include Time Division Multiple Access (TDMA) where multiple users
utilize the same frequency band by transmitting bursts of data in
specified periodic time slots or Code Division Multiple Access
(CDMA) systems.
[0004] The use of Orthogonal Frequency Division Multiplexing (OFDM)
as a modulation and multiple access method for commercial wireless
communication systems is not widely practiced and is expected to
grow in the future. Potential applications include wireless local
loop, wireless local area networks and cellular and PCS systems.
Possessing many of the benefits of well known time and code
division multiple access systems, OFDM based multiple access
systems are also referred to as Orthogonal Frequency Division
Multiple Access (OFDMA) systems in the literature. Recently, OFDM
was chosen as the modulation scheme for the European Digital Audio
Broadcast (DAB) standard and the European Terrestrial Digital Video
Broadcast (DVB-T) standard. OFDM based hybrid multiple access
systems such as OFDM-TDMA and Multicarrier-CDMA are also being
actively researched.
[0005] Such communication systems consist of a downlink and an
uplink. The downlink is the unidirectional communication link from
a single base-station (BS) to multiple remote (possibly mobile)
transceivers. The uplink is the unidirectional communication link
from these transceivers to the BS. Typically, the downlink and
uplink occupy distinct non-overlapping frequency bands--also called
frequency division duplex (FDD) operation. It is also possible to
operate in time division duplex (TDD) ("ping-pong" or half-duplex
mode) where the uplink and downlink occupy the same frequency band
but alternate in time. This is generally preferred only for indoor
systems. The uplink is a multiple access channel since the
plurality of remote transceivers access or share the uplink channel
resources. The downlink can be thought of as a broadcast or
multicast link. In general, the problem of interference suppression
is more difficult and important for the uplink since typically it
represents the capacity bottleneck (compared to the downlink).
[0006] One of the major problems faced by wireless communication
systems is that of interference. In particular, in OFDM systems,
two main categories of interference are Inter-Bin Interference
(IBI) and Co-channel Interference (CCI). IBI is the manifestation
of loss of orthogonality between different bins of a OFDM system.
Each data carrying bin acts as a source of interference (or noise)
for every other data carrying bin. CCI refers to any other
undesired signal whose spectrum overlaps with the spectrum of the
particular OFDM system under consideration and causes interference.
For example, sources of CCI may be other analog or digital
communication/broadcast systems (which may or may not be using
OFDM) operating in the same (or adjacent) frequency band in the
same/nearby geographic areas. IBI and CCI can increase the
bit-error-rate of the particular frequency bins that are
experiencing the interference. As a result, the OFDM system
performance may be degraded. Thus, interference suppression
techniques are desirable for high-performance systems. A number of
different techniques have been prepared to either avoid or suppress
interference.
[0007] A factor which must be considered in multiple access
wireless systems is that of power control or automatic gain control
(AGC). Essentially, the receiver must be able to ensure that the
received power of each bin is within a certain target range. This
problem is made difficult by the presence of fading which can
easily cause fluctuations in the received power in the range of
20-40 dB in a matter of seconds. Thus, in wireless systems, some
basic power control mechanisms may be used. However, these power
control mechanisms may not be perfect. Imperfect power control may
exacerbate the effect of IBI.
[0008] If CCI is localized in frequency (i.e., narrowband CCI), the
particular bin (or bins) that are affected such that the average
signal-to interference-plus-noise ratio (SINR) is reduced below a
certain threshold can be left unused. If the interference is
temporary, the bin can be reused when the SNR improves. The basic
procedure is well established in digital subscriber line (DSL)
modems which use DMT as the modulation scheme. This procedure may
be implemented by the BS in a wireless OFDM system by measuring any
CCI across the frequency band of interest. However, the problem is
more difficult in wireless systems because of the presence of
fading which can also greatly reduce the SNR. Thus, the average SNR
must be tracked. Fading results in fluctuations in the channel
frequency response with time.
[0009] One measure of the rate of change of the channel response
with time is given by the so-called Doppler spread (units of
Hertz). When there is little relative movement between the receiver
and transmitter (or when the propagation environment is relatively
static), the multipath fading can be considered to be slow fading
and the Doppler spread is around 5 Hz or less (this is not to be
confused with the attenuation due to distance which also changes
slowly, typically according to the log-normal distribution). In
such cases (e.g. wireless local loop and indoor systems), the
receiver can track and estimate the channel frequency response for
each bin with good accuracy. This is typically accomplished via the
use of periodic pilot sub-symbols inserted in the sub-symbol
streams of each bin of interest. F or example, for a given data
carrying bin, every pth (say p=8 or 16) sub-symbol can be pilot
(training) sub-symbol to estimate the channel periodically. For
in-between sub-symbols, the receiver can estimate the channel by
operating in decision directed mode or by interpolation. For fast
fading channels (Doppler spread 10-200 Hz), estimating the channel
is more difficult and sophisticated time-frequency interpolation
techniques must be used (this is a drawback of OFDM).
[0010] Several techniques have been proposed in the literature for
combating IBI and/or CCI. One method of addressing IBI is to space
data carrying bins apart in frequency and leave bins unused there
between. This is effective because: (a) the effect of IBI decreases
with increase in frequency separation between bins and, (b) for a
given total bandwidth, there are fewer active bins. However, this
is wasteful of bandwidth and not a preferable solution. Another
approach for addressing IBI and CCI is forward error correction
(FEC) codes, mostly implemented in conjunction with interleaving.
FEC codes may afford some protection against noise and
interference. A related method is the use of Trellis coded
modulation (TCM) to address IBI and CCI. However, the methods
proposed heretofore have met with limited success. A need remains
for an improved method and apparatus to overcome the problems
associated with IBI and CCI.
[0011] One aspect of the present invention is targeted at
interference suppression in the uplink of a FDD OFDMA system using
spatial signal processing via antenna arrays deployed at the BS
receiver. The present invention is not limited to FDD OFDMA, but
may be to carry out interference suppression in other scenarios as
well such as for hybrid OFDM-TDMA systems: Multicarrier-CDMA
systems, TDD systems and in the downlink of the above systems.
SUMMARY OF THE INVENTION
[0012] The present invention affords a method and apparatus for
suppression of CCI via the use of receive antenna arrays at the BS
for the uplink channel. In addition, those skilled in the art will
recognize that the application of this invention is not limited to
the BS uplink channel, but is also applicable elsewhere including
the BS downlink and at the remote transmitter/receivers. The
intelligent use of antenna arrays for mitigating fading and
interference is also referred to as "smart" or "intelligent
antennas". Smart antenna systems may be carried out through the use
of switched beam antennas or adaptive arrays (AA). Switched beam
antennas use a fixed beamforming network to provide several output
ports corresponding to beams in fixed directions. Signal levels in
each beam are monitored and analyzed to switch the beams
appropriately among different time or frequency channels depending
on the air-interface scheme. Adaptive arrays, on the other hand,
electronically steer a phased array by weighting the amplitude and
phase of signal at each element in response to changes in the
propagation environment. Adaptive arrays provide greater steering
flexibility in response to the propagation environment. The
preferred embodiment focuses on adaptive arrays. However, switched
beam antennas may be used.
[0013] A first inventive aspect of the present invention involves
an adaptive array (AA) architecture and methods for combating the
effect of IBI and CCI over multipath fading channels. As described
above, the solution is presented for the uplink of an OFDMA system
with synchronous uplink, however, the present invention is not
limited to OFDMA. A second inventive aspect involves a method for
allocation of frequency bins (i.e., determining the spectral
locations or bin numbers) to different users by taking spatial and
other information (such as automatic gain control (AGC)
information) into account. Each user may require one or more bins
to meet a certain quality of service requirement. This aspect of
the invention is most appropriate in the context of the adaptive
array architecture described above but, in general, is not limited
to such a receiver configuration as will be apparent to those
skilled in the art.
[0014] For multi-element AA receivers, each element may have its
own RF-to-baseband conversion and baseband demodulator. Of course,
hardware optimizations may be possible. All beamforming and
diversity combining algorithms operate on digital complex baseband
signals, for instance via general purpose or application specific
DSP's, ASIC's, in software (such as in software radios) or
combinations thereof.
[0015] Consider slowly time-varying fading channels (SFC) first.
This implies that the channel attenuation coefficients in each
frequency bin can be taken to be constant over each symbol (a
single complex number). Also, these coefficients may change from
symbol to symbol but at a slow rate relative to the symbol rate.
Some systems predominantly encounter SFC (e.g. indoor systems)
while others encounter FFC's (e.g. cellular systems along
highways). However, most wireless communication systems of interest
experience a mix of SFC and FFC conditions.
[0016] Generally, wireless communications systems experience two
primary types of signal fading within channels, slow time varying
fading and fast time varying fading. The IBI due to the
time-varying fading nature of a channel is negligible. IBI due to
frequency offsets (imperfect synchronization) is still possible. A
preferred embodiment of the present invention includes a AA with
the elements spaced far apart (5 to 15 wavelengths) to obtain
spatial diversity, i.e., independent fading at different antenna
elements. The combining method of the preferred embodiment uses
maximal ratio combining (MRC) to correct for IBI and Additive white
Gaussian noise (AWGN). The MRC is merely a spatial matched filter.
If an M element array is used, each bin has a separate M
dimensional combining weight vector. To implement MRC, the channel
frequency response for each bin may be estimated via periodic pilot
sub-symbols. Note that the MRC also subsumes the role of the
standard frequency equalization (FEQ) operation.
[0017] If CCI is also present, an optimal method (i.e., according
to one specific criterion) is the so-called maximum SINR optimum
combining (MSOC). This method also uses channel estimation. In
addition, some statistics of the signal and interference must also
be estimated. Periodic pilot sub-symbols can be used for both these
tasks. MSOC has greater computational burden than MRC but greater
potential for performance improvement.
[0018] For fast time-varying channels, implementing MSOC may
require excessive bandwidth overhead for pilot sub-symbols.
Basically, if MSOC is implemented on a FFC, it may not give any
more benefit than if MRC was used. In fact, the performance can
worsen if the channel coefficients are not tracked properly.
Unfortunately, the method required to remedy this requires a
different receiver architecture than above. Therefore, the receiver
architecture has to be chosen to be one of the two and it may not
be possible to change it on the fly unless the BS has a flexible
software radio architecture. To avoid the foregoing problems, the
preferred embodiment proposes the two-stage method as described
below.
[0019] First, the antenna array is partitioned into sub-arrays. The
elements of each sub-array are spaced close together (e.g., half
wavelength or less to avoid spatial aliasing or grating lobes) to
facilitate beamforming. Second, the individual sub-arrays are
spaced far apart (e.g., 5-15 wavelengths) to obtain spatial
diversity. The preferred embodiment uses each sub-array for
beamforming for CCI suppression. Since the post-beamforming outputs
from each sub-array may be largely affected by noise only, they can
be diversity combined. Diversity combining can be done using MRC
(which requires channel estimation). But channel estimation is
expected to be easier in the second stage since the first stage is
expected to greatly reduce the interference. No FEQ is required
since the MRC essentially serves the role of a "multi-element" FEQ.
If MRC is not feasible, switched diversity combining (SDC) may be
used by measuring the instantaneous SNR for each sub-array's output
and selecting the "best" option each symbol time (other variations
are also possible). In this case, a standard FEQ is still required.
As mentioned earlier, techniques for channel estimation via
time-frequency patterns of pilots can be used in conjunction with
the proposed AA architecture and an overall improvement in
performance and/or reduction in complexity/bandwidth overhead of
those algorithms is expected. Note that the use of this two-stage
architecture and method is not limited to operation over FFC only
and can be used over SFC as well instead of MSOC (but MSOC is very
difficult to implement over FFC). Thus, the two-stage method can be
considered to be more general. Nor is the number of elements in
each sub-array or the number of sub-arrays limited. Increasing the
number of elements in each sub-array can provide for more-optimal
beamforming while increasing the number of sub-arrays outputs that
are diversity combined can serve to further reduce interference. As
an example, a preferred embodiment may have each sub-array made up
of 4-8 elements with 2 sub-arrays for a total of 8-16 elements.
[0020] In addition, beamforming in stage 1 can be done according to
any one of a number of criterion. The preferred embodiment uses
direction-of-arrival (DOA) based constraints for beamforming. DOA
based constraints may be used when signals are directional such as
in rural or suburban environments, but are less desirable when the
angle spread is large (such as in indoor environments).
[0021] A number of methods may be used for DOA estimation. For
example, some remote transmitters may be equipped with GPS type
equipment to enable the BS to compute this information. Other
possible methods are the use of BS triangulation via
time-difference-of-arrival (TDOA) measurements. One method to
estimate DOA's of a given user is by using adjacent antenna array
elements in each sub-array. The idea here is to extract the phase
differences between complex baseband (symbol rate) samples from
adjacent sensors or doublets. As mentioned above, the sensors in
each sub-array are spaced a half-wavelength apart or closer to
avoid spatial aliasing. For a range of channel scenarios, the
fading experienced by adjacent sensors is almost perfectly
correlated. For the range of signal bandwidths and RF carrier
frequencies, the signals can be considered to be narrowband. Thus,
when the signals are coherently downconverted and demodulated, a
mutual phase offset is induced between the samples obtained from
adjacent sensors. This phase offset is proportional to the
inter-sensor spacing (normalized in wavelengths) and the sine of
the DOA measured with respect to the normal to the array. This
spatially induced phase offset is not only obtained for successive
symbols, but also for all bins being used by a particular user.
Thus, the measured phase offset can be smoothed in space (over
multiple doublets in each sub-array and using multiple sub-arrays),
in time (over a block of symbols), and in frequency (over multiple
bins used by the same user) to mitigate the effect of noise. Note
that since all array processing algorithms operate on complex
baseband outputs, the processing can be efficiently done in the
frequency domain. Also note that the AA receiver structures may
also be implemented in conjunction with sectorized cells. For
example, each cell can have 3-6 sectors and an AA receiver can be
used within each sector. Of course, one of the advantages of AA in
cellular/PCS systems is to achieve a reduction in the number of
sectors per cell (which will improve the trunking efficiency) and
still derive benefits of spatial separation between signals.
[0022] Generally, according to the preferred embodiment, the BS
attempts to allocate bins to facilitate or augment the mitigation
of IBI and CCI. In this method, the BS continually monitors a
number of parameters and uses them to compute the bin allocations
for a given user. Such allocations are typically made at start-up,
but may also be made on-the-fly for non-constant bit-rate type
applications. The allocations can also be changed dynamically in
response to changes in the prevalent noise and interference
conditions. For operation over SFC, deep fades may occur over
portions of the signal spectrum (perhaps spanning several bins) for
extended periods of time, perhaps seconds or even minutes. Bins can
also be dynamically reassigned in such cases.
[0023] To be more specific, consider a system where signals are
spatially localized (e.g. most cellular/PCS rural/suburban
systems). Assume that the BS has estimates of the direction and
received power of the dominant signal paths of all active users.
Due to multipath communications, each user may have more than one
distinct (and strong) multipath directions. The BS will typically
set a limit on the number of dominant paths that it can take into
account (such as 2 or 3) due to constraints in computation/memory
etc. Such estimates can be computed as per discussion above or by
pilots embedded in the sub-symbol streams of users. Similarly, the
BS also computes the power and directions of CCI across the band of
interest. For example, if the two stage receiver architecture
described above is used, these directions and powers can be
obtained from the beamforming coefficients used in each
sub-array.
[0024] For example, assume that a particular user is to be
allocated K bins. Using these inputs, the BS allocates bins to
satisfy the following (desired) criteria:
[0025] 1. The K bins belonging to any one user should be spaced as
far apart in frequency as possible to minimize mutual IBI. Spacing
the bins belonging to each user over a wide range of frequencies
within the band also provides frequency diversity. Frequency
diversity is desirable because it serves to lessen the effects of
fading over a certain frequency range. For example, by allocating
many widely spaced frequency bins to a single user, if the
operating environment is such that some of the bins experience
fading, the overall signal quality will still remain high because
the other user bins will not experience this fading; in short,
fading over a small frequency range within the band will not effect
the whole signal.
[0026] 2. Each bin is placed in a neighborhood with bins belonging
to other users which are spaced as far apart as possible in the
dominant DOAs of their signals. For example, a 3-5 bin neighborhood
is expected to be suitable for most applications.
[0027] 3. Each bin is placed in a neighborhood with bins belonging
to other users such that differences in signal strength of active
bins in the neighborhood are minimized.
[0028] 4. Each bin is placed in a spectral location such that there
are no co-channel interferers in the same frequency band. If no
such locations are available, spectral locations are chosen based
on the DOA of the CCI and the signal strength of the CCI. In
general, CCI bins with lower CCI signal strength are assigned
before bins with higher CCI signal strength. Also, bins are
allocated so that the difference in the DOA's of the particular
user and the CCI are as large as possible. These criteria are
balanced depending upon the operating environment.
[0029] These criterion lead to better separation of potentially
interfering signals in the spatial domain, thus facilitating the
operation of spatial interference suppression techniques. The above
criteria may be "weighted" differently to construct algorithms or
flowcharts optimized for a specific (or category of) channel and
interference scenarios, as will be apparent to those skilled in the
art. For example. If IBI is the dominant impairment, items 1 and 2
above can be given the most importance. Item 3 is important if the
system is operated in an environment with a wide range of received
power levels. Similarly, if CCI is the dominant impairment, item 4
is given the highest priority. Since the number of bins in an OFDM
can be quite large (several hundred bins is common), the
implementation of the overall algorithm must be reasonably simple
to enable execution in real-time. For instance, one possible way to
implement it would be to construct a data structure which contains
a table of information about each bin. This would include
information such as whether the bin is active or inactive (at any
given time), the user occupying the bin if any, whether it is a
data, control or pilot bin, modulation scheme and constellation
size of sub-symbols in the bin, received power level, dominant
DOA's of the user occupying the bin, power level and DOA's of any
co-channel interferers spectrally overlapping with the bin, etc.
Note that all items of the above information may not be available
or be able to be computed for each bin at all times. Certain of the
above factors (like received power levels) appear to be best suited
for update on a periodic (scheduled) basis (such as every n
milliseconds or during each frame as per some existing framing
structure). Other items are better suited for update in an event
driven mode (such as user activity and constellation size), i.e.,
when a user arrives, departs, requests (or is forced to have) a
change in the amount of allocated bandwidth.
[0030] Turning now to a more rigorous examination of uplink
multiple access using OFDM. In an OFDMA system, the entire uplink
bandwidth processed by a base-station is (dynamically) allocated
among a group of users. While the downlink is always synchronous,
unlike uplink TDMA systems in which remote units transmit in bursts
in specified periodic time-slots, uplink OFDMA can be made
synchronous using the method of loop-timing. In this method, each
mobile transceiver first synchronizes itself to the base-station on
the downlink and then derives its uplink transmitter timing
reference from the recovered downlink clock. To facilitate the
former task, the base-station embeds pilot tones in the transmitted
downlink signal which are utilized by the remote receiver to
"lock-on" to the base's timing reference. To overcome frequency
selective fading across the signal bandwidth, multiple pilots can
be used. While conventional baseband digital phase locked loops can
be used for operation over slowly time-varying channels, for
frequency acquisition and tracking algorithms suitable for
operation over fast time-varying channels. The local timing
reference for mobile transceivers are usually derived from a
Voltage Controlled Crystal Oscillator (VCXO) which provides the
timing reference for the receiver A/D, transmitter D/A and all
radio frequency (RF) circuitry. Frequency offsets between the
receive and transmit symbol clock occur due to non-idealities in
the remote transceiver VCXOs, possibly of the order of several
parts-per-million (ppm).
[0031] Assuming that the initial tasks of carrier frequency
synchronization, symbol timing recovery and symbol time alignment
have been completed this enables the base-station receiver to
demodulate received baseband signals from all users with a single
FFT. The base-station is also responsible for all bandwidth
management functions to provide each unit with shared access to the
uplink channel.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0032] FIGS. 1a-1c illustrate a preferred embodiment of the
adaptive antenna array architecture implemented for use in a
cellular telephone system according to the present invention.
[0033] FIGS. 2a-2b illustrate a preferred embodiment of the
sub-array of the adaptive antenna array architecture of FIG. 1.
[0034] FIG. 3 illustrates, graphically, a method for determining
the direction of arrival of received signals or co-channel
interference.
[0035] FIGS. 4a and 4b illustrate exemplary signal response
patterns of the adaptive antenna array as modified according to the
present invention to focus the gain at multi-path reception points
and reduce the gain at points of co-channel interference.
[0036] FIG. 5 is a general block diagram of the adaptive antenna
array architecture of the embodiment of FIG. 1.
[0037] FIG. 6 illustrates a typical environment for operation of
the adaptive antenna array architecture of the embodiment of FIG. 1
including the geographic positioning of multiple users and
co-channel interference.
[0038] FIG. 7 illustrates the manner by which frequency bins are
allocated by the adaptive antenna array architecture in the
frequency domain according to one embodiment of the present
invention.
[0039] FIG. 8 is a block diagram of one exemplary method for
frequency bin allocation of the preferred embodiment of the present
invention.
DESCRIPTION OF THE PREFERRED EMBODIMENT
[0040] Initially, an explanation is provided of an OFDM
transmission model before describing the preferred embodiments.
Consider a discrete time OFDM system model in which N orthogonal
sinusoids are transmitted each symbol time. The N bins are
partitioned among a total of L independent users in non-overlapping
sets of K N/L (assumed integer) bins for each user. Without loss of
generality, a complex baseband representation is used for all
signals. Thus, the normalized transmitted signal from the lth user
is given by
S l ( n ) = 1 N k .epsilon. S l a ( k ) j 2 .pi. N kn , n .epsilon.
[ 0 , N - 1 ] , l .epsilon. [ 0 , L - 1 ] ( 2.1 ) ##EQU00001##
where j= {square root over (-1)}; a(k) is the kth frequency domain
sub-symbol typically selected from a quadrature amplitude
modulation (QAM) constellation, and S.sub.l denotes the set of bin
indices belonging to the lth user. With a sample rate f.sub.s,
assume that each user encounters a time-selective multipath fading
channel impulse response (CIR) spanning a maximum duration
T.sub.CIR=U/f.sub.s. Thus, the received signal after passing
through the channel is given by
x 1 ( n ) = u = n n - U + 1 h u , l ( n ) s l ( u ) , l .epsilon. [
0 , L - 1 ] ( 2.2 ) ##EQU00002##
where h.sub.u,l(n) denotes the CIR for the lth user at time n due
to the impulse .delta.(n-u). Note that slowly time-varying channels
may be considered to be time-invariant over a symbol period leading
to h.sub.u,l(n)=h.sub.l(n-u). Using (2.1) in (2.2),
x 1 ( n ) = 1 N u = 0 U - 1 h n - u , l ( n ) k .epsilon. S l a ( k
) j 2 .pi. N k ( n - u ) , n .epsilon. [ 0 , N - 1 ] , l .epsilon.
[ 0 , L - 1 ] ( 2.3 ) ##EQU00003##
Each symbol is prefixed with a cyclic prefix (CP) or guard time.
The CP serves two main purposes. First, inter-symbol-interference
is eliminated when the CP length is chosen to be longer than the
CIR. Second, by appropriately selecting the transmitted samples of
the CP, the transmitted signal appears periodic to the channel
resulting in simplified frequency-domain equalization. This is done
by setting
s.sub.l(-j)=s.sub.l(N-j), j.epsilon.[1,G],l.epsilon.[0,L-1]
(2.4)
where G is chosen such that T.sub.CP.gtoreq.T.sub.CIR, i.e.
G.gtoreq.U. In addition, the CP can also be utilized for
synchronization purposes. At the receiver, the CP samples are
discarded before demodulation and it is sufficient to consider each
symbol independently. The received uplink signal at the
base-station from all users is given by
r ( n ) = l = 0 L - 1 x l ( n ) + v ( n ) + z ( n ) ( 2.5 )
##EQU00004##
where v(n) denotes discrete-time AWGN samples with variance
.sigma..sub.v.sup.2 and z(n) denotes discrete-time CCI samples.
After discarding the CP, (2.5) can be written compactly in matrix
form for an entire symbol as,
r = l = 0 L - 1 u = 0 U - 1 H u , l T l D u , l a l + v + z ( 2.6 )
##EQU00005##
where r=[r(0), r(1), . . . , r(N-1)].sup.T is the received signal
vector; a.sub.l.epsilon.C.sup.K; denotes the column vector of
frequency domain sub-symbols from the lth user; v=[v(0), v(1), . .
. , v (N-1)].sup.T.epsilon.C.sup.N; z=[z(0), z(1), . . . ,
z(N-1)].sup.T.epsilon.C.sup.N; H.sub.u,l=diag([h.sub.-u,l(0),
h.sub.-u+l,l(1), . . . ,
h.sub.-u+N-1,l(N-1)]).epsilon.C.sup.N.times.N, denotes the diagonal
CIR matrix for the lth user and uth delay;
D.sub.u,l.epsilon.C.sup.K.times.K denotes a diagonal matrix of
phase delays whose element corresponding to the kth bin is given
by
- j 2 .pi. N ku ##EQU00006##
and T.sub.l.epsilon.C.sup.N.times.K denotes the inverse-DFT
modulating matrix whose column corresponding to the kth bin is
given by
t l , k = 1 N [ 1 , j 2 .pi. N k , j 2 .pi. N k ( N - 1 ) ] T .
##EQU00007##
[0041] Turning our attention to the slowly time-varying channel, we
begin by considering a slowly varying fading channel in which the
impulse response may be taken to be time-invariant over a symbol
period. This case illustrates the demodulating procedure and will
also serve as a starting point for the rest of the analysis. Thus,
H.sub.u,l=h.sub.l(u)I.sub.N where h.sub.l(u) is the uth impulse
response coefficient. Using (2.6), the pth bin belonging to, say
the lth user, is demodulated as
y(p)=t.sub.l,p.sup.Hr=a.sub.s(p)a(p)+{circumflex over
(v)}(p)+{circumflex over (z)}(p), p.epsilon.S.sub.l (2.7)
where,
a s ( p ) = u = 0 U - 1 h l ( u ) - j 2 .pi. N up ( 2.8 )
##EQU00008##
and {circumflex over (v)}(p) and {circumflex over (z)}(p) denote
the post-demodulation residual noise and CCI respectively. Thus,
there is no fading induced IBI in this case. There are several
methods for frequency-domain equalization ranging from no
equalization at all (for instance, using
differential-phase-shift-keying schemes such as D-QPSK), to
sophisticated time-frequency adaptive filtering algorithms. The
former are usually sufficient for slowly time-varying channels
while the latter are used in more demanding environments.
[0042] Now consider the fast time-varying channel. As above, using
(2.6), the pth bin belonging to, say the lth user, is demodulated
as
y ( p ) = l = 0 L - 1 u = 0 U - 1 t l , p H H u , l T l D u , l a l
+ t l , p H ( v + z ) = a f ( p ) a ( p ) + i f ( p ) + v ^ ( p ) +
z ^ ( p ) p .di-elect cons. S l ##EQU00009##
where,
a f ( p ) = 1 N u = 0 U - 1 n = 0 N - 1 h n - u , l ( n ) - j 2
.pi. N up ##EQU00010## i f ( p ) = 1 N l = 0 L - 1 k .di-elect
cons. S l k .noteq. p u = 0 U - 1 n = 0 N - 1 h n - u , l ( n ) - j
2 .pi. N n ( k - p ) - j 2 .pi. N uk a ( k ) ##EQU00010.2##
It is straightforward to show that the variance of {circumflex over
(v)}(p) equals .sigma..sub.v.sup.2 and the post-demodulation CCI
variance is given by,
.sigma. z 2 ( p ) = E [ z ^ ( p ) 2 ] = n = - ( N - 1 ) N - 1 ( 1 -
n / N ) r z ( n ) - j 2 .pi. N up = P z ( w ) * sin 2 ( Nw / 2 ) N
sin 2 ( w / 2 ) w = 2 .pi. p N ##EQU00011##
where E[.cndot.] denotes the ensemble expectation; r.sub.z(.cndot.)
is the CCI discrete time auto-correlation function and P.sub.z(w)
is the CCI power spectral density (PSD). Thus, for a given, say pth
bin, the post-demodulation CCI is given by the convolution of the
CCI PSD with a sinc.sup.2(.cndot.) function evaluated at the
corresponding angular frequency. Each demodulated sub-symbol is now
corrupted by IBI from all other sub-symbols. The effect of IBI is
damaging for even small values of Doppler spreads and frequency
offsets and can severely limit the bit error rate performance. If
the tone interferer is f.sub.t Hz away from the center of a
particular bin, the normalized frequency offset is
f.sub.t/f.sub.bin. This shape of the spectral leakage function is a
direct consequence of using the discrete Fourier transform as the
OFDM modulation basis function.
[0043] Consider now the effect of time-varying random frequency
offsets due to imperfect transceiver synchronization and phase
noise. This issue becomes particularly important when the
synchronization problem is aggravated by fast time-varying
channels, or for systems that are sensitive to power and complexity
considerations. Let the normalized frequency offset be denoted by
.eta.=f.sub.off/f.sub.bin where the inter-bin spacing
f.sub.bin=f.sub.s/N. Each symbol time, .eta. is modeled as a
realization of an independent uniformly distributed random variable
in the interval [-.eta..sub.MAX,.eta..sub.MAX]. Thus, the
demodulating vector with frequency offset is given by J(.eta.)
t.sub.l,p where J(.eta.).epsilon.C.sup.N.times.N denotes a diagonal
offset matrix with nth element given by
j ( 2 .pi. N n .eta. + .beta. ) ##EQU00012##
where .beta..epsilon.[-.pi.,.pi.] denotes a phase offset varying
from symbol to symbol. Thus,
y ( p ) = l = 0 L - 1 u = 0 U - 1 t l , p H J H ( .eta. ) H u , l T
l D u , l a l + t l , p H J H ( .eta. ) ( v + z ) = a f , .eta. ( p
) a ( p ) + i f , .eta. ( p ) + v ^ .eta. ( p ) + z ^ n ( p ) , p
.di-elect cons. S l where , a f , .eta. ( p ) = 1 N u = 0 U - 1 n =
0 N - 1 h n - u , l ( n ) j ( 2 .pi. N n .eta. + .beta. ) - j 2
.pi. N up ( 2.9 ) i f , .eta. ( p ) = 1 N l = 0 L - 1 k .di-elect
cons. S l k .noteq. p u = 0 U - 1 n = 0 N - 1 h n - u , l ( n ) j (
2 .pi. N n ( k - p + .eta. ) + .beta. ) - j 2 .pi. N uk a ( k ) (
2.10 ) ##EQU00013##
The SINR for the pth bin is defined as
SINR ( p ) = E [ a f , n ( p ) a ( p ) 2 ] E [ i f , n ( p ) + z ^
.eta. ( p ) 2 ] + .sigma. v 2 ( 2.11 ) ##EQU00014##
[0044] Assuming a wide sense stationary uncorrelated scattering
(WSSUS) multipath fading model and a Rayleigh fading Doppler
spectrum [13], expressions for signal and interference powers in
(2.11) appear in the Appendix.
[0045] We now turn to an analysis of the reception of the antenna
array receiver. In conventional single-antenna wireless OFDM
receivers, FFT based demodulation is generally followed by
frequency domain equalization (FEQ) and subsequent mapping of the
equalized frequency domain sub-symbols to bits. In an adaptive
array OFDM receiver, demodulator outputs from each sub-array
element are fed into a bank of array combiners where a separate
array combining vector is used for each bin. We propose the use of
the maximum SINR criterion and constraint based beamforming for
weight adaptation under appropriate channel conditions as described
later in this section. In the former method, channel estimation is
necessary and antennas are widely spaced to obtain independent
fading between elements (i.e., spatial diversity). To maximize the
SINR at the output of the array, the optimum weight vector balances
diversity and interference suppression. In the latter approach,
antenna elements are spaced sufficiently close to prevent spatial
aliasing (i.e., grating lobes) and facilitate the application of
constraints, such as those derived from estimates of
direction-of-arrival (DOA) of impinging signals (i.e., the four
factors discussed above).
[0046] Considering reception over slowly time-varying. For a given
data rate and bandwidth, the ratio of symbol rate to fading rate
(or Doppler spread) in OFDM is much smaller compared to
single-carrier systems. For example, with a typical OFDM symbol
rate of 4 KHz and with a Doppler spread of 200 Hz, the ratio is 20
(the same ratio for the IS-136 TDMA system having a 24.3 KHz symbol
rate is about six times greater). For the maximum SINR method to be
effective, the temporal averaging used to estimate the
noise-plus-interference statistics must be done over a time
duration much smaller than the duration over which the fading
changes significantly. Channel estimates for each antenna element
are also required. Moreover, this procedure has to be carried out
separately for every data carrying bin. Thus, this approach is
suitable only for OFDM systems with slow time-varying fading, for
example in low mobility scenarios.
[0047] Thus, we utilize statistically optimum array combining as
per the maximum SINR criterion for operation over slowly fading
channels. With an M element array receiver depicted in FIG. 5, let
the CIR matrix for the mth element user be denoted by
H.sub.u,l.sup.(m)=h.sub.l.sup.(m)(u)I.sub.N. The array elements are
spaced sufficiently apart to obtain uncorrelated fading. The
corresponding received signal is given by
r ( m ) = l = 0 L - 1 u = 0 U - 1 h l ( m ) ( u ) T l D u , l a l +
v ( m ) + e z e ( m ) ( 3.1 ) ##EQU00015##
where z.sub.e.sup.(m) denotes the eth CCI component received at the
mth sub-array. Thus, the demodulated signal at the output of the
mth element is given by
y ( m ) ( p ) = l = 0 L - 1 u = 0 U - 1 t l , p H J H ( .eta. ) h l
( m ) ( u ) T l D u , l a + t l , p H J H ( .eta. ) ( v ( m ) + e z
e ( m ) ) = a s , .eta. ( m ) ( p ) a ( p ) + i s , .eta. ( m ) ( p
) + v ^ .eta. ( m ) + e z ^ e , .eta. ( m ) ( p ) ##EQU00016##
where a.sub.s,.eta.(p) and i.sub.s,n.sup.(m)(p) are given by
setting h.sub.n-u,1(n) to h.sub.l.sup.(m)(u) in (2.10). Denoting
the vector of demodulated pth bin outputs from all M elements as
y(p)=[y.sup.(0)(p), y.sup.(1)(p); . . . , y.sup.(M-1)(p).sup.T and
the estimated pth sub-symbol is obtained as
{circumflex over (a)}(p)=w.sup.H(p)y(p) (3.2)
The optimum weight vector for (3.2) above which maximizes the SINR
at the output of the array is given by
w.sub.opt(p)=.gamma.(p)R.sub.y.sup.-1(p).LAMBDA.(p) (3.3)
where .gamma.(p) is a constant (not affecting the output SINR);
R.sub.y(p).epsilon.C.sup.M.times.M is the received data covariance
matrix and .LAMBDA.(p)=[a.sub.s,n.sup.(0)(p), a.sub.s,n.sup.(1)(p),
. . . , a.sub.s,n.sup.(M-1)(p)].sup.T is the propagation vector for
the pth bin. R.sub.y(p) and .LAMBDA.(p) are estimated by periodic
pilot sub-symbols inserted in the each active bin. A number of
techniques can be used for channel estimation (.LAMBDA.(p)) using
data directed (i.e. training sequence) or decision directed
operation taking into account the time-frequency dispersive
characteristics of the channel.
[0048] In this case, constraint based beamforming is used with the
constraints chosen such that their rate of change is significantly
slower than the data rate. This approach also allows for flexible
and general constraints, albeit at the expense of higher
computation required for their generation. In the sequel, DOA based
constraints are used to exploit angle diversity. To enable the
simultaneous exploitation of spatial and angle diversity, the
base-station array is partitioned into multiple sub-arrays. While
the elements within each sub-array are closely spaced, the
individual sub-arrays are spaced far apart. This allows for
combined use of angle diversity (via constraint based beamforming
in each sub-array) and spatial diversity (via diversity combining
of all sub-array outputs). Thus, consider a base-station antenna
array configuration comprised of M sub-arrays, each with S
elements, for a total of MS elements. For an inter-element spacing
of .rho. and narrowband signal wavelength .lamda., the fading
experienced at adjacent sensors is almost perfectly correlated for
sufficiently small values of .rho./.lamda. (such as 0.5 or less)
and angle spreading (around 5.degree.-10.degree. or less). Thus,
the inter-element spacing within each sub-array .rho. is chosen to
facilitate beamforming. A large inter sub-array spacing, on the
other hand, is chosen to obtain a spatial diversity gain. For
example, a spacing of 5.lamda. to 10.lamda. or more is regarded to
be adequate for obtaining sufficiently low fading correlation. Note
that conventional beamforming only, or diversity combining only
arrays are special cases of this configuration with M=1 and S=1
respectively. If maximal ratio combining (MRC) is used for
combining outputs from different sub-arrays in the second stage, a
separate FEQ is not needed. On the other hand, if switched
diversity combining is used, a FEQ is still required. Thus, if
channel estimation is to be eliminated, switched diversity is
appropriate in conjunction with differential signaling.
[0049] For typical cellular systems, the rate of change of DOAs is
much lower than the symbol rate allowing for the use of only a few
pilot bins to obtain DOA estimates. Moreover, each of these
constraints can be utilized for multiple bins, thus greatly
reducing the total computational burden for constraint generation.
Another key advantage of DOA based beamforming is that since DOA
information is independent of carrier frequency, the information
can be re-used for downlink beamforming as well. In addition to
conventional algorithms, DOA estimation techniques based on
time-difference-of-arrival and multiple base-station triangulation
are also emerging resulting information can also be utilized for
other tasks such as mobile hand-offs and geolocation.
[0050] Extending the notation of the previous section, let the CIR
matrix for the mth sub-array be denoted by H.sub.u,l.sup.(m). The
received signal at the sth element of the mth sub-array in the
presence of spatially directional desired signals and CCI is given
by
r ( m , s ) = l = 0 L - 1 c l ( m , s ) u = 0 U - 1 H u , l ( m ) T
l D u , l a l + v ( m , s ) + e c e ( m , s ) Z e ( m , s ) ( 3.4 )
##EQU00017##
where c.sub.l.sup.(m,s) and c.sub.e.sup.(m,s) denote the
multiplicative factors which can be factored out in the sth
sensor's response of the mth sub-array with respect to the
reference sensor (s=0) for the lth user and eth CCI component
respectively. For instance, if the signals are assumed to emerge
from point sources, c.sub.l.sup.(m,s)=e.sup.jsOp; p.epsilon.S.sub.l
and c.sub.l.sup.(m,s)=e.sup.jsOe where O.sub.p and O.sub.e denote
the spatial DOA's given by
O.sub.p2.pi.(.rho./.lamda.)sin(.theta..sub.p), p.epsilon.S.sub.l
and .phi..sub.e=2.pi.(.rho./.lamda.)sin(.theta..sub.e)
corresponding to DOA's O.sub.p and O.sub.e of the particular
desired signal and CCI respectively. If the sources are assumed to
be in the array far-field, the directions can be assumed to be
unchanged with respect to each sub-array's reference element. Thus,
the demodulated signal at the output of the (m,s)th element is
given by
y ( m , s ) ( p ) = l = 0 L - 1 u = 0 U - 1 t l , p H c l ( m , s )
J H ( .eta. ) H u , l ( m ) T l D u , l a l + t l , p H J H ( .eta.
) ( v ( m , s ) + e c e ( m , s ) z e ( m , s ) ) = c l ( m , s )
.alpha. f , .eta. ( p ) a + i f , .eta. ( m , s ) ( p ) + v ^ .eta.
( m , s ) ( p ) + e c e ( m , s ) z ^ e , .eta. ( m , s ) ( p )
##EQU00018##
where a.sub.f,n(p) is given by (2.9) and
i f , n ( m , s ) ( p ) = 1 N l = 0 L - 1 k .di-elect cons. S l k
.noteq. p u = 0 U - 1 n = 0 N - 1 c l ( s ) h n - u , l ( m ) ( n )
j ( 2 .pi. N n ( k - p + .eta. ) + .beta. ) - j 2 .pi. N uk a ( k )
( 3.5 ) ##EQU00019##
[0051] A single DOA estimation and beamforming processor is shared
between all sub-arrays. DOAs of the received signal's dominant path
(and possibly other secondary multipath components) are assigned to
sub-arrays to enable computation and update of their respective
weight vectors. Also, the same weight vector may be used for more
than one sub-array if secondary paths are unused (or for economy of
implementation). Let w.sub.b.sup.(m)(p).epsilon.C.sup.S denote the
pth bin's beamforming vector for the mth sub-array. The mth
sub-array output is given by
.sup.(m)(p)=[w.sub.b.sup.(m)(p)].sup.H.sub.y.sup.(m)(p)
where Y.sup.(m)(p)=[y.sup.(m,0)(p), y.sup.(m,1)(p), . . . ,
y.sup.(m,S-1)(p)].sup.T. We formulate the solution for
W.sub.b.sup.(m)(p) using the well known generalized sidelobe
canceler (GSC) framework. The GSC formulation of the beam-former is
particularly useful since it readily lends itself to recursive
implementations using standard LMS or RLS type algorithms, or via
block sample covariance matrix inversion. The GSC uses a
constrained output energy minimization criterion and under a signal
preserving constraint, it yields the corresponding MMSE solution
for beamformer weights. The constrained optimization problem may be
formulated as,
w b ( m ) ( p ) = arg min w w H R y ( m ) ( p ) w subject to [ c (
m ) ] p H w = f ( 3.6 ) ##EQU00020##
where C.sub.p.sup.(m) is the constraint matrix whose columns
represent multiple constraints; f is the desired constraint
response;
R.sub.y.sup.(m)(p)=R.sub.S.sup.(m)(p)+R.sub.i.sup.(m)(p)+R.sub.v+R.sub.z.-
sup.(m)(p); R.sub.y.sup.(m)(p).epsilon.C.sup.S.times.S; is the pth
bin's received data covariance matrix for the mth sub-array and
R.sub.s.sup.(m)(p), R.sub.i.sup.(m)(p),
R.sub.v=.sigma..sub.v.sup.2I.sub.S and R.sub.z.sup.(m)(p) denote
the corresponding signal, IBI, additive noise and CCI covariance
matrices respectively. The GSC solution to (3.6) is well known [9,
25] and is given by
w.sub.b.sup.(m)(p)=w.sub.q.sup.(m)(p)-C.sub.p,a.sup.(m)w.sub.a.sup.(m)(p-
) where
w.sub.b.sup.(m)(p)=(C.sub.p,a.sup.(m).sup.HR.sub.y.sup.(m)(p)C.sub.p,a.s-
up.(m)).sup.-1C.sub.p,a.sup.(m).sup.HR.sub.y.sup.(m)(p)w.sub.q.sup.(m)(p)
(3.7)
w.sub.q.sup.(m)(p)=C.sub.p.sup.(m)[C.sub.p.sup.(m).sup.HC.sub.p.sup.(m)].-
sup.-1 f and C.sub.p,a.sup.(m).epsilon.C.sup.S.times.S.sup.c is the
matrix spanning the null space of C.sub.p.sup.(m) where
S.sub.c<S is the number of constraints used. If only one signal
preserving constraint is used, C.sub.p.sup.(m)=d.sup.m(p) and f=1
where d.sup.m(p) denotes the chosen estimated steering vector of
the desired user. The output SINR with the mth beamformer is given
as
SINR ( m ) ( p ) = [ w b ( m ) ] H ( p ) R S ( m ) ( p ) w b ( m )
( p ) [ w b ( m ) ] H ( p ) ( R i ( m ) ( p ) + R v + R z ( m ) ( p
) ) w b ( m ) ( p ) ##EQU00021##
To determine the performance gain which may be obtained from
diversity combining, let the average signal-to-noise ratio per bit
at each sub-array output be denoted by {circumflex over
(.gamma.)}b. Assuming uncorrelated Rayleigh distributed received
signals, the average probability of bit error (P.sub.b) for
coherent PSK sub-symbols using MRC is given by
P _ bMRC = [ P ( .gamma. _ b ) ] M m = 1 M ( M - 2 + m m - 1 ) [ 1
+ P ( .gamma. _ b ) ] m - 1 ( 3.8 ) ##EQU00022##
where P(.gamma..sub.b) denotes the probability of error for a
specific alphabet size. For example, if the probability of error in
a AWGN channel is given by aerfc ( {square root over
(b.gamma..sub.b)}), then by averaging over the probability density
function of
.gamma..sub.b.sub.3P(.gamma..sub.b).apprxeq.a/(2b.gamma..sub.b).
Channel estimation for MRC is performed on post-beamforming outputs
from each sub-array which greatly minimizes the impact of CCI on
the channel estimates.
[0052] The joint space-frequency bin allocation scheme
automatically determines bin allocations for mobile users taking
the spatial dimension into account. Spectral locations are sought
for each bin such that the K bins belonging to any one user are
spaced as far apart in frequency as possible to minimize mutual
IBI. Spacing the bins belonging to each user over a range of
frequencies also increases frequency diversity (i.e., because the
bins of a particular user are spaced in frequency, typical CCI
sources operating in a small, in-band frequency range have less
effect on the overall signal then if the signal bins were closely
grouped in frequency.) Also, each bin is co-located with bins
belonging to other users which are spaced as far apart as possible
in the DOAs of their signals. This enables the beamformer to
suppress IBI between adjacent bins by exploiting spatial
selectivity.
[0053] Accomplishing these goals simultaneously at each arrival
(when a new user requires bin allotment) or departure (when an
existing user terminates its connection) can be a computationally
formidable task due to the typically large number of bins and
users. The following method is proposed which sequentially solves
the above problem in an efficient manner. To begin, the entire
spectrum is partitioned into K contiguous frequency blocks
containing L bins. Every user is allotted one bin in each of the K
blocks and spatial information is used to determine the bin
distribution within each block. If .psi. denotes the dominant DOA
of the pth user, the bin placement in any one block is done by
computing the following metric for each available bin as
n p = arg max n min i .DELTA..theta. n , i ( 3.9 ) ##EQU00023##
where
.DELTA..theta..sub.n,i=|.psi.-.theta.(n-i)| i.epsilon.[-W,W],
i.noteq.0 (3.10)
is the magnitude of the differences between .psi. and DOAs of bins
in a neighborhood of 2 W bins. If an adjacent bin is unoccupied,
the corresponding value of .DELTA..theta..sub.n,i is set to the
maximum angular difference possible. Note that owing to the block
structure of bin allocation, it is sufficient to compute the metric
for any one block and replicate the bin allotment in the remaining
blocks. Moreover, the objective function is easy to compute and can
be maintained in a tabular form for fast look-up. These metrics are
updated whenever there is an user arrival or departure. In severe
multipath environments, the spatial selection is generalized by
taking into account multiple DOAs as well as power levels for each
bin in computing the windowed DOA difference for each bin. In
propagation environments dominated by CCI instead of (or in
addition to) IBI, the above criterion can take into account the DOA
and frequency location, as well as signal strength, of co-channel
interferes to minimize the effect of spectral overlap and
leakage.
[0054] Consider an example wireless OFDM system with the following
parameters: Total number of bins N=256; Useful symbol time
T.sub.sym=230 .mu.s; CP or guard time T.sub.CP=20 .mu.s and symbol
rate f.sub.sym=1/(T.sub.sym+T.sub.CP)=4 KHz. Thus, inter-bin
spacing f.sub.bin=1/T.sub.sym=4.348 KHz and total occupied
bandwidth=N f.sub.bin=1.11 MHz. QPSK modulated sub-symbols, the
aggregate data rate=(2 bits/bin) (N bins/symbol) (f.sub.sym
symbols/sec)=2.048 Mb/s. For an uplink multiple access system with
these parameters, L=32 independent users, each with K=8 bins can
each be allocated a raw bit rate of 64 Kb/s. Other factors which
can reduce the user available data rate or the number of usable
bins include analog and digital filtering constraints, spectral
mask requirements, and bandwidth overhead for control and
signaling.
[0055] Consider first the effect of IBI only without additive noise
and CCI. In this application, it is known that M-branch spatial
diversity using MRC (M=2,3) is very effective. This result is not
surprising since IBI is spatially and spectrally distributed and
MRC is known to be the optimum array combining method in the
presence of noise only.
[0056] Consider now the performance of maximum SINR optimum array
combining for CCI suppression on a slowly time-varying channel. For
M=2, both MRC and maximum SINR combining yield similar performance
and a 3 dB array processing gain is obtained for AWGN only (when
CCI is negligible).
[0057] For a given number of total elements, combined use of angle
and spatial diversity is clearly superior to angle diversity alone.
It is known that diversity combining is most beneficial at
relatively higher input SINRs while beamforming is most effective
for relatively lower SINR's when the interference is strong.
[0058] Now the discussion turns to the preferred embodiment. FIG. 1
illustrates one exemplary embodiment of an adaptive array
architecture 10 with a base station 7 implemented for use in a
cellular telephone system according to the present invention. An
array support structure 1, may be implemented as single or multiple
towers as shown or by any other means that enable the array to be
placed at the desired elevation and spacing including but not
limited to conical towers or fixation on commercial buildings of
sufficient elevation. In the preferred embodiment, the array
support structure 1 is attached to the array fixation structure 2
by means of support beams 3. The array support structure 1 thus
maintains the array fixation structure 2 at a fixed elevation. In
the embodiment of FIGS. 1a-1c, the array fixation structure 2 is
arranged in the shape of a triangle, thereby dividing the complete
360.degree. service area into three sectors of 120.degree. each. By
way of example only, each 120.degree. sector constitutes a single
adaptive array 4, each adaptive array being comprised, by way of
example only, of two sub-arrays 5. The array structure may be
varied without departing from the present invention. For instance,
the service area may be divided into differing numbers of sectors,
the number of sub-arrays may be increased or their orientation
changed. The sectors need not be equal in size. Each sub-array is
electrically connected to a base station 7 which may be located on
the tower as shown or at any other convenient location including
mounted on the array fixation structure 2 or in an enclosed area at
the base of the array support structure 1.
[0059] In each adaptive array, the sub-arrays are separated by a
distance sufficient to allow the resultant signals from each
sub-array to be spatial diversity combined. Spatial diversity
requires a sufficient element spacing to allow independent fading
at different elements. The signals from such spaced elements can be
combined to lessen interference and increase the received signal
strength. For spatial diversity combining to be effective at the
effective operating distance of a cellular telephony system, an
array spacing of at least 2 wavelengths at the frequency of
operation is beneficial with the spacing preferably between 5 to 15
wavelengths. In the present embodiment, the minimum group spacing
12 is in the range of 5-15 wavelengths.
[0060] FIGS. 2a and 2b, illustrate exemplary geometries that may be
used for the sub-arrays 5. By way of example only, the sub-array 5
may be implemented as a dipole array 20 comprised of three antenna
array elements 21 oriented vertically and arranged side-by-side.
The spacing between the antenna array elements 21, in this
embodiment, is less than a predetermined maximum element spacing,
for example, one half of one wavelength at the frequency of
operation (<.lamda./2) to facilitate steering. Steering or
beamforming is the ability of the signal response of an array to be
altered through modification of the timing or phasing of the array
elements; for instance, by altering the phasing of array elements
the array can be made to receive desired user signals at a higher
gain while at the same time damping undesired interference signals.
To provide effective steering, the elements should be spaced as
close as possible; the element spacing must be less than a
wavelength and classically less and one-half of one wavelength to
provide steering.
[0061] The antenna array elements 21 are attached to and supported
by the array fixation structure 2 and are electrically connected to
the base station 7. Sub array 5 may also be implemented as a
microstrip patch array 25 (as shown in FIG. 2b). Microstrip patch
array 25 may be configured as a Butler array comprised of eight
total patches 26 arranged in patch rows 27 of four patches each. As
in the dipole array 20, each of the patches 26 in a given patch row
27, in this preferred embodiment is separated by less than the
maximum element spacing, for example, one half of one wavelength at
the frequency of operation (<.lamda./2) to facilitate steering.
Alternatively, the microstrip patch array 25 may be replaced with
two dipole antenna elements arranged horizontally side-by side that
provide a similar signal response pattern. The microstrip patch
array 25 may be desirable because of its low manufacturing cost in
some applications.
[0062] FIG. 3 graphically illustrates the manner by which the base
station 7 determines the direction of arrival of either a remote
unit or a source of co-channel interference. FIG. 3 illustrates a
top view of a sub-array 5 comprised of two antenna array elements
31a and 31b arranged vertically side-by-side with a separation less
than .lamda./2. Incoming signal 33 from a user impinges upon the
antenna array elements 31a and 31b. Because the incoming signal 33
is arriving from a large distance relative to the separation
between elements 31a and 31b, the far field approximation (the
signal source is so far away from the receiver that the incident
waves appear as plane waves) is valid and the incoming signal 33
can be approximated as impinging upon array elements 31a and 31b at
the same angle. The additional signal travel distance 37 that the
incoming signal 33 must travel to impinge upon the more distant of
the array elements 31b can be calculated in several ways, such as
by time delay or phase shift. Because the separation between the
array elements 31a and 31b is also known, the sine of angle 38 may
be calculated and is equal to direction of arrival 39 normal to the
sub-array 5. The base station 7 then computes the direction of
arrival.
[0063] Although the exemplary embodiment contains two elements, it
will be obvious to those skilled in the art that the direction of
arrival may be calculated by means of many different methods. The
accuracy of the determination of the direction of arrival is, of
course, dependant upon the method used. In general, a greater
number of antenna elements can provide greater resolution of the
direction of arrival. Thus, the BS 7 could distinguish between
remote users who are disposed closer together with regard to
direction of arrival. Determination of the direction of arrival is
also dependant upon the filter used by the BS 7. In addition, the
BS 7 may utilize a number of multipath signals to determine
direction of arrival. In this case, the direction of arrival may be
along multiple paths.
[0064] FIGS. 4a and 4b illustrate a general signal response pattern
in FIG. 4a as well as, in FIG. 4b, a signal response pattern
modified to provide higher gain to desired user signals while
damping interference. FIG. 4b illustrates exemplary signal response
patterns of the adaptive array, along with multi-path reception and
co-channel interference. Multi-path reception refers to an
individual user's signal that is received by the BS from more than
one direction such as when user signals are reflected from
structures in an urban environment. Co-channel interference (CCI)
refers to any other undesired signal whose spectrum overlaps with
the spectrum of the particular OFDM system under consideration and
causes interference. An idealized signal response pattern is shown
as 40a in FIG. 4a. The radius of the response pattern 40a from the
BS 7 in a given angular direction indicates the relative gain or
signal response level of the BS in that radial direction. Thus, in
the response pattern 40a, the adaptive array receives with equal
strength signals from any direction. This response may be seen to
be less than ideal when operating in the presence of CCI because it
is desired to minimize or "damp out" undesired CCI to increase
system performance while increasing the gain for desired signals.
Thus, the signal response pattern 40a is altered.
[0065] Once the direction of arrival of a communications signal
from a remote user is known, the array elements can be energized or
their responses placed through a filter with varying phases, time
delays or both to produce the signal response pattern 40b in FIG.
4b. Again, in signal response pattern 40b the radial distance of
the response pattern 40b from BS 7 is indicative of the relative
gain or signal response level in that radial direction. The BS 7
modifies the idealized pattern to provide increased gain for the
signals of user 41 as well as the multi-path propagation 11, 12 of
the signals of user 41. At the same time, the signal response
pattern provides for damping in the direction of CCIs 13 and 14 to
minimize received interference signals.
[0066] FIG. 5 is a block diagram of an exemplary embodiment of a
receiver 60 for the adaptive antenna array architecture 10. The
receiver 60 is capable of correcting for incoming channels which
experience fast time-varying fading. The receiver 60 illustrates
two stages of an array. Signals from mobile users 51 impinge upon
the adaptive array 52 comprised of a plurality of sub-arrays 59
numbered 0 to M. Each sub-array 59 comprises a plurality of
elements 54 numbered 0 to S. The number of elements 54 in each
sub-array 59 may not be equal. Each sub-array 59 can handle signals
from many mobile users 51 at the same time. At each sub-array 59,
the signals from mobile users 51 pass through coherent demodulators
to beamformers 56 which are supplied with direction of arrival data
from the DOA processor 57 in the BS 7 to construct the desired
signal response pattern. The DOA processor 57 calculates the
direction of arrival in accordance with the method described above
in connection with FIG. 3. The output signals from the beamformers
56 are passed through a spatial diversity combiner 58 to remove
interference. The output signal from the spatial diversity combiner
58 may be fed into a standard voice or data network.
[0067] In an alternative embodiment, the adaptive antenna array
architecture 10 may be used in an orthogonal frequency division
multiple access (OFDMA) system. The base station 7 determines the
direction of arrival (DOA) in the manner described above. The base
station 7 of the OFDMA system segments the available bandwidth into
multiple frequency bins which can then be allocated based on
predetermined factors. The inclusion of the DOA as a factor in an
OFDMA bin allocation scheme improves overall system performance by
allowing the OFDMA bin allocation algorithm to differentiate
between user signals on the basis of DOA as well as differentiate
between the DOA of CCIs and user signals thus providing for less
overall CCI and Inter-Bin Interference (IBI).
[0068] FIG. 6 illustrates an exemplary operating environment for a
cellular system. The base station 7 is operating in the presence of
signals from co-channel interferer 61 and signals from mobile users
62a, 62b, 62c, 62d and 62e.
[0069] The direction of arrival of all signals relative to the base
station 7 can be observed as lines leading from the spatial
positions of the various signals to BS 7. Mobile users 62c and 62b
have substantially the same DOA, while mobile user 62e and
co-channel interferer 61 have substantially the same DOA. Also,
mobile users 62a, 62b, and 62c are located at approximately the
same distance from base station 10 and thus have approximately the
same signal strength. While, in this exemplary environment, the DOA
of each signal is shown as a straight line from the remote unit, in
a more complex implementation of the present invention the BS 7 may
take into account multipath signals of a certain magnitude (usually
not more than two or three signal paths for computational
simplicity) as well as accounting for the angle spreads of the
incident signal wither directly from the remote unit or multipath.
For ease of representation, the exemplary environment of FIG. 6
shows dominant, straight-line signal paths from the remote units
and the CCI without angle spreading.
[0070] FIG. 7 illustrates a frequency band distribution according
to an embodiment of the present invention. The frequency band 70 of
the OFDMA system, expressed in the frequency domain as shown,
includes the range of frequencies between the bottom or low
frequency cut-off 72a and the top or high frequency cut-off 72b. In
an OFDMA system, the frequency band 70 is segmented into bins 73
for allocation to individual users which are then grouped into
neighborhoods 71a and 71b (e.g., three to five bins per
neighborhood) which are shown as an outtake 76 of the frequency
band 70. However, the use of neighborhoods in the present
embodiment is done for the sake of computational convenience.
Although the preferred embodiment above was implemented using
groupings of frequency bins called neighborhoods, many aspects of
the preferred embodiment can be implemented without grouping the
frequency bins in this way. In this case, the above preferred
embodiment may operate in a different fashion, such as on a
bin-by-bin basis, to accomplish the invention.
[0071] Once the bins 73 and neighborhoods 71a and 71b have been
established, the BS 7 allocates bins to a particular user so as to
maximize the overall system performance. An noted above, two
significant constraints on performance are Inter-Bin Interference
(IBI) and Co-Channel Interference (CCI). In an effort to minimize
IBI and CCI, the BS 7 continually monitors a number of parameters
and uses them to compute the bin allocations for a given user. Bin
allocations are typically made at start-up, but can also be changed
dynamically throughout operation in response to changes in the
prevalent noise, interference, or fading conditions.
[0072] FIG. 8 is a block diagram illustrating an exemplary method
for allocating frequency bins by the BS 7 according to one
embodiment of the present invention. To illustrate the bin
selection method of the BS 7, it is assumed that the mobile users
of FIGS. 6 and 7 are to be allocated K bins each. Using these
inputs, the BS 7 allocates bins to satisfy the following (desired)
criteria.
[0073] First, the BS 7 determines which bins are available to be
allocated at step 80. Bins are not available to be allocated if the
bin is in use by another user or the level of CCI is to high to
provide adequate user signal resolution. For instance, in FIG. 7,
the presence of CCI is indicated by frequency artifact 74. The bins
thus dominated by CCI will not be available to be allocated at this
stage. The BS 7 then determines if enough open bins exist to
support a remote user seeking registration with the system or
seeking to use more frequency bins (step 81). If the BS 7 can not
allocate enough bins, the BS 7 analyzes (step 82) the previously
rejected high-CCI bins 75. If the difference in the DOA's of the
particular user and the co-channel interferers are sufficiently
large to permit data to be carried in the bin 75, the BS 7
allocates the bin 75 to the user 82. Referring to FIG. 6, if the
frequency artifact 74 is the output of CCI 61, the bins 75
dominated by frequency artifact 74 may be allocated to users 62a,
62b, 62c, or 62d because the DOA of these users is widely different
from CCI 61 (FIG. 6). However, such CCI bins 75 could not be
allocated to user 62e because the DOA of user 62e and CCI 61 are
substantially similar. After determining what additional CCI bins
75, if any, may be allocated to the given user, the BS 7 then
determines if sufficient bins now exist after the CCI-bin 75
allocation to support the user 83. If sufficient bins still do not
exist, service is refused at step 84.
[0074] If sufficient bins exist to support the user, control passes
to step 85 where the K bins belonging to any one user are spaced as
far apart in frequency as possible to minimize mutual IBI. Thus, if
many bins in the system are open, the bins used to carry data may
be separated by several bins to reduce IBI.
[0075] Next at step 86, each bin is placed by the BS 7 in a
neighborhood with bins belonging to other users which are spaced as
far apart as possible in the dominant DOAs of their signals. Stated
another way, the BS 7 collects unique sets of bins 71a and 71b as
neighborhoods such that each frequency bin in a given neighborhood
is assigned to remote users having substantially different DOAs.
For example, a 3-5 bin neighborhood may be used. For example, FIG.
7 illustrates two 5-bin neighborhoods 71a and 71b. Applying the
user DOAs from FIG. 6 and assuming for a moment a system in which
only two neighborhoods exist and assuming all signals are of
similar received power levels, ideally, the signals from user 62c
may be placed in bins distant from the signals from user 62b
because of the similarity in their DOAs. An ideal bin placement
under this constraint would maximize the differences between the
DOAs of successive bins for an overall neighborhood as shown in
neighborhood 71b. The signal from user 62e is places in the bin
between the signals from users 62b and 62c and the signals from
users 62a and 62d are placed in successive bins as shown. This
method of bin placement serves to reduce overall IBI. Spacing the
bins belonging to each user over a wide range of frequencies within
the band also provides frequency diversity. Frequency diversity is
desirable because it serves to lessen the effects of fading over a
certain frequency range. For example, by allocating many widely
spaced frequency bins to a single user, if the operating
environment is such that some of the bins experience fading, the
overall signal quality will still remain high because the other
user bins will not experience this fading; in short, fading over a
small frequency range within the band will not effect the whole
signal.
[0076] Next, at step 87, the BS 7 reevaluates the bin allocations.
The BS 7 determines whether to place each bin in a neighborhood
with bins belonging to other users such that differences in
received signal power level of active bins in the neighborhood are
minimized. By re-assigning bins according to signal power, the BS 7
ensures that weaker more distant signals are not overpowered by
closer more powerful signals. Thus, turning to FIG. 6 for
reference, the closer, stronger signals of users 62e and 62c may be
grouped together, in a first group, while the more distant, weaker
signals of users 62a, 62b, and 62d may be placed in a second group
spaced from the first in frequency band 70.
[0077] The above method leads to better separation of potentially
interfering signals in the spatial domain, thus facilitating the
operation of spatial interference suppression. Optionally, the
above criteria of steps 81-87 may be "weighted" differently, or
considered in different orders to construct methods optimized for a
specific (or category of) channel and interference scenarios, as
will be apparent to those skilled in the art. In the example of
FIG. 8, because IBI is a dominant impairment, spacing bins in
frequency and placing bins in a neighborhood with bins of differing
DOAs is given the most importance. Depending on the environment,
other criteria may become dominant. For instance, the bins in a
neighborhood may be placed with bins of similar power if the system
is operated in an environment with a wide range of received signal
power levels.
[0078] Since the number of bins in an OFDM system can be quite
large (several hundred bins), the implementation of the overall
method may be reasonably simple to enable execution in real-time.
For instance, one possible way to implement it would be to
construct a data structure which contains a table of information
about each bin. The table may include information such as whether
the bin is active or inactive (at any given time), the user
occupying the bin if any, whether it is a data, control or pilot
bin, modulation scheme and constellation size of sub-symbols in the
bin, received power level, dominant DOA's of the user occupying the
bin, power level and DOA's of any CCIs spectrally overlapping with
the bin etc. Optionally, all items of the above information may not
be available for each bin at all times. Certain items in the above
table like received power levels appear to be best suited for
update on a periodic (scheduled) basis (such as every n
milliseconds or during each frame as per some existing framing
structure). Other items are better suited for update in an event
driven mode (such as user activity and constellation size), e.g.,
when a user arrives, departs, requests (or is forced to have) a
change in the amount of allocated bandwidth.
[0079] Thus, through the use of the above factors, individual bins
may be dynamically allocated and re-allocated on-the-fly. The
composition of neighborhoods may also be changed dynamically if a
trigger event, such as the advent of a new CCI source should arise.
The number of bins assigned to a neighborhood may also change.
[0080] Although the preferred embodiment above was implemented
using groupings of frequency bins called neighborhoods, many
aspects of the preferred embodiment can be implemented without
grouping the frequency bins in this way. In this case, the above
preferred embodiment may operate in a different fashion, such as on
a bin-by-bin basis, to accomplish the above invention. For
instance, if frequency bins are not grouped into neighborhoods,
allocating on a bin-by-bin fashion such that the DOAs of adjacent
or approximately adjacent bins differ would accomplish the above
invention. In this fashion, the above invention may also be
implemented with neighborhoods containing only one bin.
[0081] Although the present invention has been described with
reference to specific embodiments, those of skill in the art will
recognize that changes may be made thereto without departing from
the scope and spirit of the invention as set forth in the appended
claims.
* * * * *