U.S. patent application number 16/474570 was filed with the patent office on 2019-12-26 for method and apparatus for demodulating signal.
This patent application is currently assigned to DATANG MOBILE COMMUNICATIONS EQUIPMENT CO., LTD. The applicant listed for this patent is DATANG MOBILE COMMUNICATIONS EQUIPMENT CO., LTD. Invention is credited to Yuetan CHEN, Lihua NI, Ling WANG.
Application Number | 20190393982 16/474570 |
Document ID | / |
Family ID | 62707771 |
Filed Date | 2019-12-26 |
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United States Patent
Application |
20190393982 |
Kind Code |
A1 |
CHEN; Yuetan ; et
al. |
December 26, 2019 |
METHOD AND APPARATUS FOR DEMODULATING SIGNAL
Abstract
Disclosed are a method and apparatus for demodulating a signal.
The method comprises: obtaining a received signal, wherein the
received signal comprises a phase noise signal; establishing a
likelihood probability ratio integral model on the basis of the
received signal and a preset phase noise parameter, wherein the
phase noise parameter represents the phase noise signal and is a
random variable; performing phase rotation angle extraction and
transformation processing and discretization processing on the
likelihood probability ratio integral model to obtain a likelihood
probability ratio discrete model, wherein the phase rotation angle
represents the phase rotation angle obtained on the basis of the
phase noise signal; and determining a likelihood probability ratio
corresponding to the received signal on the basis of the likelihood
probability ratio discrete model to obtain a demodulation result.
In this way, a base station overcomes phase noise, and improves
signal receiving performance, signal demodulation efficiency and
signal demodulation accuracy by performing phase compensation and
discrete calculation on a received signal to obtain the
demodulation result.
Inventors: |
CHEN; Yuetan; (Beijing,
CN) ; WANG; Ling; (Beijing, CN) ; NI;
Lihua; (Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DATANG MOBILE COMMUNICATIONS EQUIPMENT CO., LTD |
Beijing |
|
CN |
|
|
Assignee: |
DATANG MOBILE COMMUNICATIONS
EQUIPMENT CO., LTD
Beijing
CN
|
Family ID: |
62707771 |
Appl. No.: |
16/474570 |
Filed: |
August 31, 2017 |
PCT Filed: |
August 31, 2017 |
PCT NO: |
PCT/CN2017/099938 |
371 Date: |
June 28, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 27/22 20130101;
H04L 25/08 20130101; H04L 1/0054 20130101; H04L 27/2649 20130101;
H04L 25/067 20130101; H04L 27/34 20130101; H04L 27/32 20130101;
H04L 27/38 20130101 |
International
Class: |
H04L 1/00 20060101
H04L001/00; H04L 27/26 20060101 H04L027/26; H04L 27/22 20060101
H04L027/22 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 29, 2016 |
CN |
201611248829.9 |
Claims
1. A method for demodulating a signal, comprising: obtaining a
received signal, wherein the received signal comprises a phase
noise signal; creating a likelihood probability ratio integral
model based upon the received signal and a preset phase noise
parameter, wherein the phase noise parameter represents the phase
noise signal, and is a random variable; performing phase rotation
angle extraction conversion process, and discretization process on
the likelihood probability ratio integral model to obtain a
likelihood probability ratio discretization model, wherein the
phase rotation angle represents a phase rotation angle obtained
based upon the phase noise signal; and determining likelihood
probability ratios corresponding to the received signal based upon
the likelihood probability ratio discretization model, and
obtaining a demodulation result.
2. The method according to claim 1, wherein creating the likelihood
probability ratio integral model based upon the received signal and
the preset phase noise parameter comprises: obtaining each bit in
the received signal and a sequence number corresponding to the each
bit; determining a first set of constellation points and a second
set of constellation points corresponding to the each bit
respectively based upon a preset association relationship between
bits, sequence numbers of the bits, and constellation points,
wherein the first set of constellation points corresponding to one
bit is a corresponding set of constellation points when the bit is
0, and the second set of constellation points corresponding to one
bit is a corresponding set of constellation points when the bit is
1; creating a first likelihood probability model corresponding to
the each bit based upon the corresponding first set of
constellation points and the phase noise parameter, and creating a
second likelihood probability model corresponding to each bit based
upon a corresponding second set of constellation points and the
phase noise parameter, wherein the first likelihood probability
model corresponding to one bit represents a corresponding
likelihood probability when the bit is 0, and the second likelihood
probability model corresponding to one bit represents a
corresponding likelihood probability when the bit is 1; and
creating the likelihood probability ratio integral model
corresponding to each bit based upon a logarithm of a ratio of the
first likelihood probability model to the second likelihood
probability model corresponding to each bit.
3. The method according to claim 2, wherein creating the first
likelihood probability model corresponding to the each bit based
upon the corresponding first set of constellation points and the
phase noise parameter comprises: determining the phase rotation
angle based upon the phase noise parameter, wherein the phase
rotation angle is positively correlated to a parameter being a
complex index of e, the complex index being the phase noise
parameter; creating a constellation point probability model
corresponding to each constellation point based upon a product of
each constellation point and the phase rotation angle; and creating
the first likelihood probability model corresponding to the each
bit based upon constellation point probability models corresponding
to respective constellation points in the corresponding first set
of constellation points, wherein the first likelihood probability
model corresponding to one bit is positively correlated to a sum of
the constellation point probability models corresponding to the
respective constellation points in the first set of constellation
points corresponding to the bit.
4. The method according to claim 2, wherein creating the second
likelihood probability model corresponding to the each bit based
upon the corresponding second set of constellation points and the
phase noise parameter comprises: determining the phase rotation
angle based upon the phase noise parameter, wherein the phase
rotation angle is positively correlated to a parameter being a
complex index of e, the complex index being the phase noise
parameter; creating a constellation point probability model
corresponding to each constellation point based upon a product of
each constellation point and the phase rotation angle; and creating
the second likelihood probability model corresponding to each bit
based upon constellation point probability models corresponding to
the respective constellation points in the corresponding second set
of constellation points, wherein the second likelihood probability
model corresponding to one bit is positively correlated to a sum of
the constellation point probability models corresponding to the
respective constellation points in the second set of constellation
points corresponding to the bit.
5. The method according to claim 2, wherein performing phase
rotation angle extraction conversion process, and discretization
process on the likelihood probability ratio integral model to
obtain the likelihood probability ratio discretization model
comprises: multiplying a numerator and a denominator of the
likelihood probability ratio integral model respectively with a
preset extraction conversion parameter, and obtaining a likelihood
probability ratio phase compensation model, wherein the extraction
conversion parameter is positively correlated to a parameter being
a complex index of e, the complex index being a negative of the
phase noise parameter, and the likelihood probability ratio phase
compensation model represents phase compensation on the received
signal for phase rotation; and performing discrete summation on the
likelihood probability ratio phase compensation model, and
performing approximation process based on a max-log-map algorithm
to obtain the likelihood probability ratio discretization model,
wherein the likelihood probability ratio discretization model is
positively correlated to a difference between a first Euclidean
distance and a second Euclidean distance, the first Euclidean
distance represents a shortest Euclidean distance from respective
constellation points in a corresponding first set of constellation
points when a bit is 0, and the second Euclidean distance
represents a shortest Euclidean distance from respective
constellation points in a corresponding second set of constellation
points when the bit is 1.
6. The method according to claim 5, wherein determining the
likelihood probability ratios corresponding to the received signal
based upon the likelihood probability ratio discretization model
and obtaining the demodulation result comprises: determining the
first Euclidean distance and the second Euclidean distance
corresponding to the each bit in the received signal respectively
based upon the likelihood probability ratio discretization model;
determining the likelihood probability ratio corresponding to the
each bit based upon the first Euclidean distance and the second
Euclidean distance corresponding to the each bit in the received
signal, wherein the likelihood probability ratio corresponding to
one bit is positively correlated to the difference between the
first Euclidean distance and the second Euclidean distance
corresponding to the bit; and determining the demodulation result
of the received signal based upon the likelihood probability ratio
corresponding to the each bit in the received signal.
7. An apparatus for demodulating a signal, comprising: an obtaining
unit configured to obtain a received signal, wherein the received
signal comprises a phase noise signal; a creating unit configured
to create a likelihood probability ratio integral model based upon
the received signal and a preset phase noise parameter, wherein the
phase noise parameter represents the phase noise signal, and is a
random variable; a discretizing unit configured to perform phase
rotation angle extraction conversion process, and discretization
process on the likelihood probability ratio integral model to
obtain a likelihood probability ratio discretization model, wherein
the phase rotation angle represents a phase rotation angle obtained
based upon the phase noise signal; and a determining unit
configured to determine likelihood probability ratios corresponding
to the received signal based upon the likelihood probability ratio
discretization model, and obtain a demodulation result.
8. The apparatus according to claim 7, wherein the creating unit
configured to create the likelihood probability ratio integral
model based upon the received signal and the preset phase noise
parameter is configured: to obtain each bit in the received signal
and a sequence number corresponding to the each bit; to determine a
first set of constellation points and a second set of constellation
points corresponding to the each bit respectively based upon a
preset association relationship between bits, sequence numbers of
the bits, and constellation points, wherein the first set of
constellation points corresponding to one bit is a corresponding
set of constellation points when the bit is 0, and the second set
of constellation points corresponding to one bit is a corresponding
set of constellation points when the bit is 1; to create a first
likelihood probability model corresponding to the each bit based
upon the corresponding first set of constellation points and the
phase noise parameter, and to create a second likelihood
probability model corresponding to the each bit based upon the
corresponding second set of constellation points and the phase
noise parameter, wherein the first likelihood probability model
corresponding to one bit represents a corresponding likelihood
probability when the bit is 0, and the second likelihood
probability model corresponding to one bit represents a
corresponding likelihood probability when the bit is 1; and to
create the likelihood probability ratio integral model
corresponding to the each bit based upon a logarithm of a ratio of
the first likelihood probability model to the second likelihood
probability model corresponding to the each bit.
9. The apparatus according to claim 8, wherein the creating unit
configured to create the first likelihood probability model
corresponding to the each bit based upon the corresponding first
set of constellation points and the phase noise parameter is
configured: to determine the phase rotation angle based upon the
phase noise parameter, wherein the phase rotation angle is
positively correlated to a parameter being a complex index of e,
the complex index being the phase noise parameter; to create a
constellation point probability model corresponding to each
constellation point based upon a product of each constellation
point and the phase rotation angle; and to create the first
likelihood probability model corresponding to the each bit based
upon constellation point probability models corresponding to
respective constellation points in the corresponding first set of
constellation points, wherein the first likelihood probability
model corresponding to one bit is positively correlated to a sum of
the constellation point probability models corresponding to the
respective constellation points in the first set of constellation
points corresponding to the bit.
10. The apparatus according to claim 8, wherein the creating unit
configured to create the second likelihood probability model
corresponding to the each bit based upon the corresponding second
set of constellation points and the phase noise parameter is
configured: to determine the phase rotation angle based upon the
phase noise parameter, wherein the phase rotation angle is
positively correlated to a parameter being a complex index of e,
the complex index being the phase noise parameter; to create a
constellation point probability model corresponding to each
constellation point based upon a product of each constellation
point and the phase rotation angle; and to create the second
likelihood probability model corresponding to each bit based upon
the constellation point probability models corresponding to
respective constellation points in the corresponding second set of
constellation points, wherein the second likelihood probability
model corresponding to one bit is positively correlated to a sum of
the constellation point probability models corresponding to the
respective constellation points in the second set of constellation
points corresponding to the bit.
11. The apparatus according to claim 8, wherein the discretizing
unit configured to perform phase rotation angle extraction
conversion process, and discretization process on the likelihood
probability ratio integral model to obtain the likelihood
probability ratio discretization model is configured: to multiply a
numerator and a denominator of the likelihood probability ratio
integral model respectively with a preset extraction conversion
parameter, and to obtain a likelihood probability ratio phase
compensation model, wherein the extraction conversion parameter is
positively correlated to a parameter being a complex index of e,
the complex index being a negative of the phase noise parameter,
and the likelihood probability ratio phase compensation model
represents phase compensation on the received signal for phase
rotation; and to perform discrete summation on the likelihood
probability ratio phase compensation model, and to perform
approximation process based on a max-log-map algorithm to obtain
the likelihood probability ratio discretization model, wherein the
likelihood probability ratio discretization model is positively
correlated to a difference between a first Euclidean distance and a
second Euclidean distance, the first Euclidean distance represents
a shortest Euclidean distance from respective constellation points
in a corresponding first set of constellation points when a bit is
0, and the second Euclidean distance represents a shortest
Euclidean distance from respective constellation points in a
corresponding second set of constellation points when the bit is
1.
12. The apparatus according to claim 11, wherein the determining
unit configured to determine the likelihood discretization ratios
corresponding to the received signal based upon the likelihood
discretization ratio discretization model and obtain the
demodulation result is configured: to determine the first Euclidean
distance and the second Euclidean distance corresponding to the
each bit in the received signal respectively based upon the
likelihood probability ratio discretization model; to determine the
likelihood probability ratio corresponding to each bit based upon
the first Euclidean distance and the second Euclidean distance
corresponding to the each bit in the received signal, wherein the
likelihood probability ratio corresponding to one bit is positively
correlated to the difference between the first Euclidean distance
and the second Euclidean distance corresponding to the bit; and to
determine the demodulation result of the received signal based upon
the likelihood probability ratio corresponding to the each bit in
the received signal.
Description
[0001] This application claims priority to Chinese Patent
Application No. 201611248829.9, filed with the Chinese Patent
Office on Dec. 29, 2016, and entitled "Method and apparatus for
demodulating signal", which is hereby incorporated by reference in
its entirety.
FIELD
[0002] The present invention relates to the field of
communications, and particularly to a method and apparatus for
demodulating a signal.
BACKGROUND
[0003] In a Long Term Evolution (LTE) system, phase noise
independent of Gaussian white noise may occur during a transmission
signal of a User Equipment (UE) or an evolved Node B (eNB) is being
processed. The phase noise refers to such a random change in phase
of a signal output by the system (such as various radio frequency
devices) that arises from various noises in the system.
[0004] Since there is a random change in phase of the transmission
signal due to the phase noise, the phase noise may impose some
negative influence on demodulation of a received signal, e.g., on a
calculation result of a Log-Likelihood Ratio (LLR), and may impose
a larger negative influence thereon at a higher level of Modulation
and Coding Scheme (MCS), and for example, the precision of
calculating an LLR may be higher at a higher level of MCS, where
there is a higher transmission rate of communication at a higher
level of MCS.
[0005] In the prior art, the eNB generally demodulates the received
signal in the following schemes.
[0006] In a first scheme, the eNB regards the received signal
equivalently as a signal of a standard constellation point, onto
which noise in a complex Gaussian distribution is superimposed,
i.e., Gaussian white noise, and then calculates an LLR of the
received signal.
[0007] However only the Gaussian white noise is taken into account,
but the phase noise is not treated correspondingly in the first
scheme, so the calculated LLR may not be precise.
[0008] In a second scheme, the phase noise is introduced to a
constellation point of the transmission signal, and then the LLR of
the received signal is calculated.
[0009] However when the phase noise is verified in the second
scheme, the reception performance of a receiver may be degraded,
and the demodulation efficiency may be lowered.
SUMMARY
[0010] Embodiments of the invention provide a method and apparatus
for demodulating a signal, so as to address phase noise while a
signal is being demodulated, to thereby improve the performance of
receiving the signal by an eNB, and the efficiency and accuracy of
demodulating the signal.
[0011] Specific technical solutions according to the embodiments of
the invention are as follows.
[0012] An embodiment of the invention provides a method for
demodulating a signal, the method including:
[0013] obtaining a received signal, wherein the received signal
includes a phase noise signal;
[0014] creating a likelihood probability ratio integral model based
upon the received signal and a preset phase noise parameter,
wherein the phase noise parameter represents the phase noise
signal, which is a random variable;
[0015] performing phase rotation angle extraction conversion
process, and discretization process on the likelihood probability
ratio integral model to obtain a likelihood probability ratio
discretization model, wherein the phase rotation angle represents a
phase rotation angle obtained based upon the phase noise signal;
and
[0016] determining likelihood probability ratios corresponding to
the received signal based upon the likelihood probability ratio
discretization model, and obtaining a demodulation result.
[0017] In one embodiment creating the likelihood probability ratio
integral model based upon the received signal and the preset phase
noise parameter includes:
[0018] obtaining each bit in the received signal and a sequence
number corresponding to the each bit;
[0019] determining a first set of constellation points and a second
set of constellation points corresponding to the each bit
respectively based upon a preset association relationship between
bits, sequence numbers of the bits, and constellation points,
wherein the first set of constellation points corresponding to one
bit is a corresponding set of constellation points when the bit is
0, and the second set of constellation points corresponding to one
bit is a corresponding set of constellation points when the bit is
1;
[0020] creating a first likelihood probability model corresponding
to the each bit based upon the corresponding first set of
constellation points and the phase noise parameter, and creating a
second likelihood probability model corresponding to the each bit
based upon the corresponding second set of constellation points and
the phase noise parameter, wherein the first likelihood probability
model corresponding to one bit represents a corresponding
likelihood probability when the bit is 0, and the second likelihood
probability model corresponding to one bit represents a
corresponding likelihood probability when the bit is 1; and
[0021] creating the likelihood probability ratio integral model
corresponding to the each bit based upon a logarithm of a ratio of
the first likelihood probability model to the second likelihood
probability model corresponding to the each bit.
[0022] In one embodiment creating the first likelihood probability
model corresponding to the each bit based upon the corresponding
first set of constellation points and the phase noise parameter
includes:
[0023] determining the phase rotation angle based upon the phase
noise parameter, wherein the phase rotation angle is positively
correlated to a parameter being a complex index of e, the complex
index being the phase noise parameter;
[0024] creating a constellation point probability model
corresponding to each constellation point based upon a product of
each constellation point and the phase rotation angle; and
[0025] creating the first likelihood probability model
corresponding to the each bit based upon the constellation point
probability models corresponding to the respective constellation
points in the corresponding first set of constellation points,
wherein the first likelihood probability model corresponding to one
bit is positively correlated to a sum of the constellation point
probability models corresponding to the respective constellation
points in the first set of constellation points corresponding to
the bit.
[0026] In one embodiment creating the second likelihood probability
model corresponding to the each bit based upon the corresponding
second set of constellation points and the phase noise parameter
includes:
[0027] determining the phase rotation angle based upon the phase
noise parameter, wherein the phase rotation angle is positively
correlated to a parameter being a complex index of e, the complex
index being the phase noise parameter;
[0028] creating a constellation point probability model
corresponding to the each constellation point based upon a product
of the each constellation point and the phase rotation angle;
and
[0029] creating the second likelihood probability model
corresponding to the each bit based upon the constellation point
probability models corresponding to the respective constellation
points in the corresponding second set of constellation points,
wherein the second likelihood probability model corresponding to
one bit is positively correlated to a sum of the constellation
point probability models corresponding to the respective
constellation points in the second set of constellation points
corresponding to the bit.
[0030] In one embodiment performing phase rotation angle extraction
conversion process, and discretization process on the likelihood
probability ratio integral model to obtain the likelihood
probability ratio discretization model includes:
[0031] multiplying the numerator and the denominator of the
likelihood probability ratio integral model respectively with a
preset extraction conversion parameter, and obtaining a likelihood
probability ratio phase compensation model, wherein the extraction
conversion parameter is positively correlated to a parameter being
a complex index of e, the complex index being a negative of the
phase noise parameter, and the likelihood probability ratio phase
compensation model represents phase compensation on the received
signal for phase rotation; and
[0032] performing discrete summation on the likelihood probability
ratio phase compensation model, and performing approximation
process based on a max-log-map algorithm to obtain the likelihood
probability ratio discretization model, wherein the likelihood
probability ratio discretization model is positively correlated to
the difference between a first Euclidean distance and a second
Euclidean distance, the first Euclidean distance represents the
shortest Euclidean distance from respective constellation points in
a corresponding first set of constellation points when a bit is 0,
and the second Euclidean distance represents the shortest Euclidean
distance from respective constellation points in a corresponding
second set of constellation points when the bit is 1.
[0033] In one embodiment determining the likelihood probability
ratios corresponding to the received signal based upon the
likelihood probability ratio discretization model and obtaining the
demodulation result includes:
[0034] determining the first Euclidean distance and the second
Euclidean distance corresponding to each bit in the received signal
respectively based upon the likelihood probability ratio
discretization model;
[0035] determining the likelihood probability ratio corresponding
to the each bit respectively based upon the first Euclidean
distance and the second Euclidean distance corresponding to the
each bit in the received signal, wherein the likelihood probability
ratio corresponding to one bit is positively correlated to the
difference between the first Euclidean distance and the second
Euclidean distance corresponding to the bit; and
[0036] determining the demodulation result of the received signal
based upon the likelihood probability ratio corresponding to the
each bit in the received signal.
[0037] An embodiment of the invention provides an apparatus for
demodulating a signal, the apparatus including:
[0038] an obtaining unit configured to obtain a received signal,
wherein the received signal includes a phase noise signal;
[0039] a creating unit configured to create a likelihood
probability ratio integral model based upon the received signal and
a preset phase noise parameter, wherein the phase noise parameter
represents the phase noise signal, and is a random variable;
[0040] a discretizing unit configured to perform phase rotation
angle extraction conversion process, and discretization process on
the likelihood probability ratio integral model to obtain a
likelihood probability ratio discretization model, wherein the
phase rotation angle represents a phase rotation angle obtained
based upon the phase noise signal; and
[0041] a determining unit configured to determine likelihood
probability ratios corresponding to the received signal based upon
the likelihood probability ratio discretization model and obtain a
demodulation result.
[0042] In one embodiment the creating unit configured to create the
likelihood probability ratio integral model based upon the received
signal and the preset phase noise parameter is configured:
[0043] to obtain each bit in the received signal and a sequence
number corresponding to the each bit;
[0044] to determine a first set of constellation points and a
second set of constellation points corresponding to the each bit
respectively based upon a preset association relationship between
bits, sequence numbers of the bits, and constellation points,
wherein the first set of constellation points corresponding to one
bit is a corresponding set of constellation points when the bit is
0, and the second set of constellation points corresponding to one
bit is a corresponding set of constellation points when the bit is
1;
[0045] to create a first likelihood probability model corresponding
to the each bit based upon the corresponding first set of
constellation points and the phase noise parameter, and to create a
second likelihood probability model corresponding to the each bit
based upon the corresponding second set of constellation points and
the phase noise parameter, wherein the first likelihood probability
model corresponding to one bit represents a corresponding
likelihood probability when the bit is 0, and the second likelihood
probability model corresponding to one bit represents a
corresponding likelihood probability when the bit is 1; and
[0046] to create the likelihood probability ratio integral model
corresponding to the each bit based upon a logarithm of a ratio of
the first likelihood probability model to the second likelihood
probability model corresponding to the each bit.
[0047] In one embodiment the creating unit configured to create the
first likelihood probability model corresponding to the each bit
based upon the corresponding first set of constellation points and
the phase noise parameter is configured:
[0048] to determine the phase rotation angle based upon the phase
noise parameter, wherein the phase rotation angle is positively
correlated to a parameter being a complex index of e, the complex
index being the phase noise parameter;
[0049] to create a constellation point probability model
corresponding to each constellation point based upon a product of
each constellation point and the phase rotation angle; and
[0050] to create the first likelihood probability model
corresponding to the each bit based upon the constellation point
probability models corresponding to the respective constellation
points in the corresponding first set of constellation points,
wherein the first likelihood probability model corresponding to one
bit is positively correlated to a sum of the constellation point
probability models corresponding to the respective constellation
points in the first set of constellation points corresponding to
the bit.
[0051] In one embodiment the creating unit configured to create the
second likelihood probability model corresponding to the each bit
based upon the corresponding second set of constellation points and
the phase noise parameter is configured:
[0052] to determine the phase rotation angle based upon the phase
noise parameter, wherein the phase rotation angle is positively
correlated to a parameter being a complex index of e, the complex
index being the phase noise parameter;
[0053] to create a constellation point probability model
corresponding to each constellation point based upon a product of
each constellation point and the phase rotation angle; and
[0054] to create the second likelihood probability model
corresponding to the each bit based upon the constellation point
probability models corresponding to the respective constellation
points in the corresponding second set of constellation points,
wherein the second likelihood probability model corresponding to
one bit is positively correlated to a sum of the constellation
point probability models corresponding to the respective
constellation points in the second set of constellation points
corresponding to the bit.
[0055] In one embodiment the discretizing unit configured to
perform phase rotation angle extraction conversion process, and
discretization process on the likelihood probability ratio integral
model to obtain the likelihood probability ratio discretization
model is configured:
[0056] to multiply a numerator and a denominator of the likelihood
probability ratio integral model respectively with a preset
extraction conversion parameter, and to obtain a likelihood
probability ratio phase compensation model, wherein the extraction
conversion parameter is positively correlated to a parameter being
a complex index of e, the complex index being a negative of the
phase noise parameter, and the likelihood probability ratio phase
compensation model represents phase compensation on the received
signal for phase rotation; and
[0057] to perform discrete summation on the likelihood probability
ratio phase compensation model, and to perform approximation
process based on a max-log-map algorithm to obtain the likelihood
probability ratio discretization model, wherein the likelihood
probability ratio discretization model is positively correlated to
a difference between a first Euclidean distance and a second
Euclidean distance, the first Euclidean distance represents the
shortest Euclidean distance from respective constellation points in
a corresponding first set of constellation points when a bit is 0,
and the second Euclidean distance represents the shortest Euclidean
distance from respective constellation points in a corresponding
second set of constellation points when the bit is 1.
[0058] In one embodiment the determining unit configured to
determine the likelihood probability ratios corresponding to the
received signal based upon the likelihood probability ratio
discretization model to obtain the demodulation result is
configured: to determine the first Euclidean distance and the
second Euclidean distance corresponding to each bit in the received
signal respectively based upon the likelihood probability ratio
discretization model; to determine the likelihood probability ratio
corresponding to each bit based upon the first Euclidean distance
and the second Euclidean distance corresponding to each bit in the
received signal, wherein the likelihood probability ratio
corresponding to one bit is positively correlated to the difference
between the first Euclidean distance and the second Euclidean
distance corresponding to the bit; and to determine the
demodulation result of the received signal based upon the
likelihood probability ratio corresponding to each bit in the
received signal.
[0059] In the embodiments of the invention, a received signal is
obtained, where the received signal includes a phase noise signal;
a likelihood probability ratio integral model is created based upon
the received signal and a preset phase noise parameter, where the
phase noise parameter represents the phase noise signal, and is a
random variable; phase rotation angle extraction conversion
process, and discretization process are performed on the likelihood
probability ratio integral model to obtain a likelihood probability
ratio discretization model, where the phase rotation angle
represents a phase rotation angle obtained based upon the phase
noise signal; and likelihood probability ratios corresponding to
the received signal are determined based upon the likelihood
probability ratio discretization model and a demodulation result is
obtained. In this way, the likelihood probability ratio integral
model corresponding to the received signal is created, phase
compensation and discretization is performed on the received signal
based upon the likelihood probability ratio integral model, the
likelihood probability ratios corresponding to the received signal
are determined, and the demodulation result is obtained, thus
addressing the phase noise, improving the performance of receiving
the signal, and the efficiency and accuracy of demodulating the
signal.
BRIEF DESCRIPTION OF THE DRAWINGS
[0060] FIG. 1 is a flow chart of a method for demodulating a signal
according to an embodiment of the invention;
[0061] FIG. 2 is a schematic diagram of a constellation chart of
signal demodulation according to an embodiment of the invention;
and
[0062] FIG. 3 is a schematic structural diagram of an apparatus for
demodulating a signal according to an embodiment of the
invention.
DETAILED DESCRIPTION
[0063] In order to make the objects, technical solutions, and
advantages of the embodiments of the invention more apparent, the
technical solutions according to the embodiments of the invention
will be described below clearly and fully with reference to the
drawings in the embodiments of the invention. Apparently the
embodiments to be described are only a part but not all of the
embodiments of the invention. Based upon the embodiments here of
the invention, all of other embodiments which can occur to those
ordinarily skilled in the art without any inventive effort shall
come into the scope of the invention as claimed.
[0064] It shall be appreciated that the technical solutions
according to the invention can be applicable to various
communication systems, e.g., a Global System of Mobile
communication (GSM) system, a Code Division Multiple Access (CDMA)
system, a Wideband Code Division Multiple Access (WCDMA) system, a
General Packet Radio Service (GPRS) system, a Long Term Evolution
(LTE) system, a Long Term Evolution-Advanced (LTE-A) system, a
Universal Mobile Telecommunication System (UMTS), etc.
[0065] It shall be further appreciated that in the embodiments of
the invention, a User Equipment (UE) includes but will not be
limited to a Mobile Station (MS), a mobile terminal, a mobile
telephone, a handset, a portable equipment, etc., and the user
equipment can communicate with one or more core networks over a
Radio Access Network (RAN). For example, the user equipment can be
a mobile phone (referred to as a "cellular" phone), a computer with
a function of radio communication, etc., and the user equipment can
also be a portable, pocket, handheld, built-in-computer, or
on-vehicle mobile device.
[0066] In the embodiments of the invention, a base station (e.g.,
an access point) can be such a device in an access network that
communicates with a radio terminal over one or more sectors via an
air interface. The base station can be configured to convert a
received air frame into an IP packet, and a received IP packet into
an air frame, and operate as a router between the radio terminal,
and the remaining components of the access network, where the
remaining components of the access network can include an Internet
Protocol (IP) network. The base station can further coordinate
attribute management on the air interface, and for example, the
base station can be a Base Communication module Station (BTS) in a
GSM or CDMA system, or can be a base station (Node B) in a WCDMA
system, or can be an evolved base station (Node B or eNB or e-Node
B) in an LTE system, although the invention will not be limited
thereto.
[0067] The technical solutions according to the embodiments of the
invention will be described below clearly and fully with reference
to the drawings in the embodiments of the invention. Apparently the
embodiments to be described are only a part but all of the
embodiments of the invention. Based upon the embodiments here of
the invention, all of other embodiments which can occur to those
ordinarily skilled in the art without any inventive effort shall
come into the scope of the invention as claimed.
[0068] In order to overcome phase noise while a base station is
demodulating a signal, to thereby improve the performance of
receiving the signal, and the efficiency and accuracy of
demodulating the signal, there is designed in an embodiment of the
invention a method for demodulating a signal, where the method
includes: creating a likelihood probability ratio integral model
corresponding to a received signal, performing phase compensation
and discretization process on the received signal based upon the
likelihood probability ratio integral model, determining likelihood
probability ratio(s) corresponding to the received signal, and
obtaining a demodulation result.
[0069] An embodiment of the invention will be described below in
details with reference to the drawings.
[0070] As illustrated in FIG. 1, a specific flow of demodulating a
signal according to an embodiment of the invention is as
follows.
[0071] In the step 100, a base station obtains a received
signal.
[0072] In a real application, the base station obtains the received
signal transmitted by a transmitting device, and optionally the
transmitting device can be a base station, a commercial terminal,
or a test terminal.
[0073] Phase noise independent of Gaussian noise may occur while
the received signal transmitted by the terminal or the base station
is being processed, and phase noise may also occur while the
received signal is being obtained by the base station, so the
received signal further includes the phase noise and the Gaussian
noise.
[0074] In the step 101, the base station determines constellation
points corresponding to the received signal.
[0075] In a real application, the base station obtains a preset
association relationship between bits, sequences numbers of the
bits, and constellation points based upon a modulation scheme of
the received signal. Optionally the modulation scheme can be
4-Quadrature Amplitude Modulation (QAM), 16QAM, or 64QAM.
[0076] The base station obtains respective bits in the received
signal, and determines a first set of constellation points and a
second set of constellation points corresponding to each bit
according to the preset association relationship between bits,
sequence numbers of the bits, and constellation points, where the
first set of constellation points corresponding to one bit is a
corresponding set of constellation points when the bit is 0, and
the second set of constellation points corresponding to one bit is
a corresponding set of constellation points when the bit is 1.
[0077] For example, FIG. 2 illustrates constellation point chart
corresponding to 16QAM, every four bits are mapped to a
constellation point chart, and given that sequence numbers of the
four bits are a, b, c, and d. When the bit a is 0, the base station
determines a first set of constellation points corresponding to the
bit a as {1, 2, 3, 4, 5, 6, 7, 8}; and when the bit a is 1, the
base station determines a first set of constellation points
corresponding to the bit a as {9, 10, 11, 12, 13, 14, 15, 16}.
[0078] In the step 102, the base station creates constellation
point probability models based upon the constellation points
corresponding to the received signal and a preset phase noise
parameter.
[0079] In a real application, the base station determines a phase
rotation angle of the received signal based upon the preset phase
noise parameter, where the phase rotation angle is positively
correlated to a parameter being a complex index of e, where the
complex index being the phase noise parameter, i.e., complex index
of e being e.sup.j.theta..
[0080] Then the base station creates a received signal model based
upon the product of the constellation point and the phase rotation
angle.
[0081] Optionally the received signal model can be represented in
the equation of:
y=xe.sup.j.theta.+n.
[0082] Where y is the received signal, x is a constellation point,
.theta. is phase noise, which is a random variable obeying a
uniform distribution (-a, a), a.di-elect cons.(-.infin.,.infin.), n
is Gaussian noise, and e.sup.j.theta. is the phase rotation
angle.
[0083] Furthermore the base station creates the constellation point
probability model corresponding to the each constellation point
based upon the received signal model.
[0084] Optionally each constellation point probability model can be
represented in the equation of:
p ( y x = x k , .theta. ) = 1 .pi. .sigma. 2 e - y - x k e j
.theta. 2 .sigma. 2 . ##EQU00001##
[0085] Where p(y|x=x.sub.k, .theta.) is a constellation point
probability model corresponding to a constellation point x.sub.k,
x.sub.k is a constellation point, k is a natural number, .theta. is
phase noise, which is a random variable obeying a uniform
distribution (-a, a), a.di-elect cons.(-.infin.,.infin.), .sigma.
is a standard deviation, and .pi. is the circumference ratio.
[0086] In the step 103, the base station creates first likelihood
probability models when respective bits in the received signal are
0 respectively based upon the constellation point probability
models.
[0087] In a real application, the base station creates the
corresponding first likelihood probability model corresponding each
bit when each bit in the received signal is 0 based upon the first
set of constellation points corresponding to each bit in the
received signal and the corresponding constellation point
probability model, where the first likelihood probability model
corresponding to one bit represents a corresponding likelihood
probability when the bit is 0, and the first likelihood probability
model corresponding to the bit is positively correlated to the sum
of the constellation point probability models corresponding to the
respective constellation points in the first set of constellation
points corresponding to the bit.
[0088] Optionally each first likelihood probability model can be
represented in the equation of:
p ( y b m = 0 ) = x i .di-elect cons. X m , 0 p ( y x = x i ) = x i
.di-elect cons. X m , 0 1 2 a .intg. - a a p ( y x = x i , .theta.
) d .theta. . ##EQU00002##
[0089] Where p(y|b.sub.m=0) is a first likelihood probability,
x.sub.i is a constellation point, x.sub.i.di-elect cons.x.sub.m,
X.sub.m,0 represents a corresponding first set of constellation
points when bm is 0, i and m are natural noise, .theta. is phase
noise, which is a random variable obeying a uniform distribution
(-a, a), and a.di-elect cons.(-.infin.,.infin.).
[0090] In the step 104, the base station creates corresponding
second likelihood probability models when the respective bits in
the received signal is 1, respectively based upon the constellation
point probability models.
[0091] In a real application, the base station creates the
corresponding second likelihood probability model corresponding to
each bit when each bit in the received signal is 1, based upon the
second set of constellation points corresponding to the each bit in
the received signal, and their constellation point probability
models, where the second likelihood probability model corresponding
to a bit represents a corresponding likelihood probability when the
bit is 1, and the second likelihood probability model corresponding
to the bit is positively correlated to the sum of the constellation
point probability models corresponding to the respective
constellation points in the second set of constellation points
corresponding to the bit.
[0092] Optionally each second likelihood probability model can be
represented in the equation of:
p ( y b m = 1 ) = x r .di-elect cons. X m , 0 p ( y x = x r ) = x r
.di-elect cons. X m , 0 1 2 a .intg. - a a p ( y x = x r , .theta.
) d .theta. . ##EQU00003##
[0093] Where p(y|b.sub.m=1) is a second likelihood probability,
x.sub.r is a constellation point, X.sub.r.di-elect cons.x.sub.m,
X.sub.m,1 represents a corresponding second set of constellation
points when bm is 1, i and m are natural noise, .theta. is phase
noise, which is a random variable obeying a uniform distribution
(-a, a), and a.di-elect cons.(-.infin.,.infin.).
[0094] In the step 105, the base station creates a likelihood
probability ratio integral model corresponding to the received
signal based upon the logarithms of the ratios of the first
likelihood probability models to the second likelihood probability
models corresponding to the respective bits in the received
signal.
[0095] In a real application, the likelihood probability ratio
integral model can be represented in the step 105 in the equation
of:
p = ln p ( y b m = 0 ) p ( y b m = 1 ) = ln x i .di-elect cons. X m
, 0 1 2 a .intg. - a a p ( y x = x i , .theta. ) d .theta. x r
.di-elect cons. X m , 0 1 2 a .intg. - a a p ( y x = x r , .theta.
) d .theta. . ##EQU00004##
[0096] Where p is a likelihood probability ratio, p (y|b.sub.m=0)
is a first likelihood probability, p (y|b.sub.m=1) is a second
likelihood probability, x.sub.i is a constellation point, X.sub.m,0
represents a corresponding first set of constellation points when a
bit b.sub.m is 0, x.sub.r is a constellation point, X.sub.m,1
represents a corresponding second set of constellation points when
the bit b.sub.m is 1, m, i, and r are natural numbers, .theta. is
phase noise, which is a random variable obeying a uniform
distribution (-a, a), and a.di-elect cons.(-.infin.,.infin.).
[0097] In the step 106, the base station multiplies the numerator
and the denominator of the likelihood probability ratio integral
model respectively by a preset extraction conversion parameter to
obtain a likelihood probability ratio phase compensation model.
[0098] In a real application, in the step 106, the extraction
conversion parameter is positively correlated to a parameter being
a complex index of e, the complex index being a negative of the
phase noise parameter, and the likelihood probability ratio phase
compensation model represents phase compensation on the received
signal for phase rotation.
[0099] Optionally the likelihood probability ratio phase
compensation model can be represented in the step 105 in the
equation of:
p = ln p ( y b m = 0 ) e - 2 j .theta. p ( y b m = 1 ) e - 2 j
.theta. = ln x i .di-elect cons. X m , 0 1 2 a .intg. - a a p ( y x
= x i , .theta. ) d .theta. e - 2 j .theta. x r .di-elect cons. X m
, 0 1 2 a .intg. - a a p ( y x = x r , .theta. ) d .theta. e - 2 j
.theta. = 1 .sigma. 2 ( x i .di-elect cons. X m , 0 .intg. - a a y
e - j .theta. - x i 2 d .theta. - x r .di-elect cons. X m , 1
.intg. - a a y e - j .theta. - x r 2 d .theta. ) . ##EQU00005##
[0100] Where p is a likelihood probability ratio, e.sup.-2j.theta.
is a extraction conversion parameter, p (y|b.sub.m=0) is a first
likelihood probability, p (y|b.sub.m=1) is a second likelihood
probability, x.sub.i is a constellation point, X.sub.m,0 represents
a corresponding first set of constellation points when a bit
b.sub.m is 0, x.sub.r is a constellation point, X.sub.m,1
represents a corresponding second set of constellation points when
the bit b.sub.m is 1, m, i, and r are natural numbers, .theta. is
phase noise, which is a random variable obeying a uniform
distribution (-a, a), and a.di-elect cons.(-.infin.,.infin.).
[0101] In the step 107, the base station performs discrete
summation on the likelihood probability ratio phase compensation
model, and performs approximation process based on a max-log-map
algorithm to obtain the likelihood probability ratio discretization
model.
[0102] In a real application, the base station performs discrete
summation on the likelihood probability ratio phase compensation
model, and performs approximation process based on the max-log-map
algorithm to obtain the likelihood probability ratio discretization
model, where the likelihood probability ratio discretization model
is positively correlated to the difference between a first
Euclidean distance and a second Euclidean distance, the first
Euclidean distance represents the shortest Euclidean distance from
respective constellation points in a corresponding first set of
constellation points when a bit is 0, and the second Euclidean
distance represents the shortest Euclidean distance from respective
constellation points in a corresponding second set of constellation
points when the bit is 1.
[0103] Optionally the first Euclidean distance can be represented
in the equation of:
s 1 = min x i .di-elect cons. X m , 0 0 .ltoreq. t .ltoreq. T y e -
j ( - a + 2 at T ) - x i 2 . ##EQU00006##
[0104] Where s.sub.1 is a first Euclidean distance; optionally T
can be 16, and a can be
.pi. 24 ; ##EQU00007##
x.sub.i is a constellation point; X.sub.m,0 is a corresponding
first set of constellation points when a bit b.sub.m is 0; and i is
a natural number.
[0105] Optionally the second Euclidean distance can be represented
in the equation of:
s 2 = min x r .di-elect cons. X m , 1 0 .ltoreq. t .ltoreq. T y e -
j ( - a + 2 at T ) - x r 2 . ##EQU00008##
[0106] Where s.sub.2 is a second Euclidean distance; optionally T
can be 16, and a can be
.pi. 24 ; ##EQU00009##
x.sub.r is a constellation point; X.sub.m,1 is a corresponding
second set of constellation points when the bit b.sub.m is 1; and r
is a natural number.
[0107] Optionally the likelihood probability ratio discretization
model can be represented in the equation of:
p = 1 .sigma. 2 ( s 1 - s 2 ) . ##EQU00010##
[0108] Where p is a likelihood probability ratio, s.sub.1 is a
first Euclidean distance, and s.sub.2 is a second Euclidean
distance.
[0109] In this way, based upon the likelihood probability ratio
discretization model, firstly a first Euclidean distance and a
second Euclidean distance corresponding to each bit in the received
signal are determined respectively, and then the difference between
the first Euclidean distance and the second Euclidean distance
corresponding to each bit is calculated; and furthermore a
likelihood probability ratio corresponding to each bit is
determined according to the difference corresponding to each bit,
thus resulting in a demodulation result of the received signal.
[0110] Furthermore since the first Euclidean distance represents
the shortest Euclidean distance from respective constellation
points in a corresponding first set of constellation points when a
bit is 0, and the second Euclidean distance represents the shortest
Euclidean distance from respective constellation points in a
corresponding second set of constellation points when the bit is 1,
the likelihood probability ratio corresponding to each bit can be
further determined directly according to constellation chart
corresponding to QAM to thereby obtain the demodulation result of
the received signal.
[0111] Optionally as illustrated in FIG. 2, when the modulation
scheme of the received signal is 16QAM, then every four bits are
mapped to a constellation point chart. For example, a
phase-compensated constellation point is (x, y).
[0112] Optionally the bit bm=0, the Euclidean distance from each
constellation point in the corresponding first set of constellation
points can be represented in the equation of:
s(m,0)=(D-|x.sub.d-2D|).sup.2+(D-|y.sub.e-2D|).sup.2.
[0113] Where s(m, 0) is an Euclidean distance, optionally D can be
1/ {square root over (10)}, (x.sub.d, y.sub.e) is coordinates of
each constellation point in the first set of constellation points
corresponding to bm, and m is a natural number.
[0114] Accordingly the first Euclidean distance s.sub.1 is the
smallest one of the Euclidean distances s(m, 0) corresponding to
the respective constellation points in the first set of
constellation points.
[0115] Optionally the bit bm=1, the Euclidean distance from each
constellation point in the corresponding second set of
constellation points can be represented in the equation of:
s(m,1)=(x.sub.u+D).sup.2+(D-|y.sub.w-2D|).sup.2.
[0116] Where s(m, 1) is an Euclidean distance, and optionally D can
be 1/ {square root over (10)}, (x.sub.u, y.sub.w) is coordinates of
each constellation point in the second set of constellation points
corresponding to bm.
[0117] Accordingly the second Euclidean distance s.sub.2 is the
smallest one of the Euclidean distances s(m, 1) corresponding to
the respective constellation points in the second set of
constellation points.
[0118] In this way, the base station can firstly determine the
first sets of constellation points and the second sets of
constellation points corresponding to the respective bits in the
received signal. Then the base station obtains the first Euclidean
distance by calculating the shortest Euclidean distance
corresponding to each bit from the respective constellation points
in the corresponding first set of constellation points, and obtains
the second Euclidean distance by calculating the shortest Euclidean
distance corresponding to each bit from the respective
constellation points in the corresponding second set of
constellation points. Furthermore the base station determines the
demodulation result of the received signal according to the
difference between the first Euclidean distance and the second
Euclidean distance corresponding to each bit.
[0119] Based upon the embodiment above, as illustrated in FIG. 3
illustrating a schematic structural diagram of an apparatus for
demodulating a signal, an apparatus for demodulating a signal
according to an embodiment of the invention particularly includes
the followings.
[0120] An obtaining unit 30 is configured to obtain a received
signal, where the received signal includes a phase noise
signal.
[0121] A creating unit 31 is configured to create a likelihood
probability ratio integral model based upon the received signal and
a preset phase noise parameter, where the phase noise parameter
represents the phase noise signal, and is a random variable.
[0122] A discretizing unit 32 is configured to perform phase
rotation angle extraction conversion process, and discretization
process on the likelihood probability ratio integral model to
obtain a likelihood probability ratio discretization model, where
the phase rotation angle represents a phase rotation angle obtained
based upon the phase noise signal.
[0123] A determining unit 33 is configured to determine likelihood
probability ratios corresponding to the received signal based upon
the likelihood probability ratio discretization model to obtain a
demodulation result.
[0124] In one embodiment the creating unit 31 configured to create
the likelihood probability ratio integral model based upon the
received signal and the preset phase noise parameter is configured:
to obtain each bit in the received signal and a sequence number
corresponding to the each bit; to determine a first set of
constellation points and a second set of constellation points
corresponding to the each bit respectively based upon a preset
association relationship between bits, sequence numbers of the
bits, and constellation points, where the first set of
constellation points corresponding to one bit is a corresponding
set of constellation points when the bit is 0, and the second set
of constellation points corresponding to one bit is a corresponding
set of constellation points when the bit is 1; to create a first
likelihood probability model corresponding to the each bit based
upon the corresponding first set of constellation points and the
phase noise parameter, and to create a second likelihood
probability model corresponding to the each bit based upon the
corresponding second set of constellation points and the phase
noise parameter, where the first likelihood probability model
corresponding to one bit represents a corresponding likelihood
probability when the bit is 0, and the first likelihood probability
model corresponding to the bit is positively correlated to a sum of
the constellation point probability models corresponding to the
respective constellation points in the first set of constellation
points corresponding to the bit; and to create the likelihood
probability ratio integral model corresponding to the each bit
based upon a logarithm of a ratio of the first likelihood
probability model to the second likelihood probability model
corresponding to the each bit.
[0125] In one embodiment the creating unit 31 configured to create
the first likelihood probability model corresponding to the each
bit based upon the corresponding first set of constellation points
and the phase noise parameter is configured: to determine the phase
rotation angle based upon the phase noise parameter, where the
phase rotation angle is positively correlated to a parameter being
a complex index of e, the complex index being the phase noise
parameter; to create a constellation point probability model
corresponding to each constellation point based upon a product of
each constellation point and the phase rotation angle; and to
create the first likelihood probability model corresponding to each
bit based upon the constellation point probability models
corresponding to the respective constellation points in the
corresponding first set of constellation points, where the first
likelihood probability model corresponding to one bit is positively
correlated to a sum of the constellation point probability models
corresponding to the respective constellation points in the first
set of constellation points corresponding to the bit.
[0126] In one embodiment the creating unit 31 configured to create
the second likelihood probability model corresponding to the each
bit based upon the corresponding second set of constellation points
and the phase noise parameter is configured: to determine the phase
rotation angle based upon the phase noise parameter, where the
phase rotation angle is positively correlated to a parameter being
a complex index of e, the complex index being the phase noise
parameter; to a create constellation point probability model
corresponding to each constellation point based upon a product of
each constellation point and the phase rotation angle; and to
create the second likelihood probability model corresponding to the
each bit based upon the constellation point probability models
corresponding to the respective constellation points in the
corresponding second set of constellation points, where the second
likelihood probability model corresponding to one bit is positively
correlated to a sum of the constellation point probability models
corresponding to the respective constellation points in the second
set of constellation points corresponding to the bit.
[0127] In one embodiment the discretizing unit configured to
perform phase rotation angle extraction conversion process, and
discretization process on the likelihood probability ratio integral
model to obtain the likelihood probability ratio discretization
model is configured: to multiply the numerator and the denominator
of the likelihood probability ratio integral model respectively
with a preset extraction conversion parameter, and to obtain a
likelihood probability ratio phase compensation model, where the
extraction conversion parameter is positively correlated to a
parameter being a complex index of e, the complex index being a
negative of the phase noise parameter, and the likelihood
probability ratio phase compensation model represents phase
compensation on the received signal for phase rotation; and to
perform discrete summation on the likelihood probability ratio
phase compensation model, and to perform approximation process
based on a max-log-map algorithm to obtain the likelihood
probability ratio discretization model, where the likelihood
probability ratio discretization model is positively correlated to
the difference between a first Euclidean distance and a second
Euclidean distance, the first Euclidean distance represents the
shortest Euclidean distance from respective constellation points in
a corresponding first set of constellation points when a bit is 0,
and the second Euclidean distance represents the shortest Euclidean
distance from respective constellation points in a corresponding
second set of constellation points when the bit is 1.
[0128] In one embodiment the determining unit 33 configured to
determine the likelihood probability ratios corresponding to the
received signal based upon the likelihood probability ratio
discretization model and obtain the demodulation result is
configured: to determine the first Euclidean distance and the
second Euclidean distance corresponding to each bit in the received
signal respectively based upon the likelihood probability ratio
discretization model; to determine the likelihood probability ratio
corresponding to each bit respectively based upon the first
Euclidean distance and the second Euclidean distance corresponding
to each bit in the received signal, where the likelihood
probability ratio corresponding to one bit is positively correlated
to the difference between the first Euclidean distance and the
second Euclidean distance corresponding to the bit; and to
determine the demodulation result of the received signal based upon
the likelihood probability ratio corresponding to each bit in the
received signal.
[0129] In the embodiments of the invention, a received signal is
obtained, where the received signal includes a phase noise signal;
a likelihood probability ratio integral model is created based upon
the received signal and a preset phase noise parameter, where the
phase noise parameter represents the phase noise signal, and is a
random variable; phase rotation angle extraction conversion
process, and discretization process are performed on the likelihood
probability ratio integral model to obtain a likelihood probability
ratio discretization model, where the phase rotation angle
represents a phase rotation angle obtained based upon the phase
noise signal; and likelihood probability ratios corresponding to
the received signal are determined based upon the likelihood
probability ratio discretization model and a demodulation result is
obtained. In this way, the likelihood probability ratio integral
model corresponding to the received signal is created, phase
compensation and discretization is performed on the received signal
based upon the likelihood probability ratio integral model, the
likelihood probability ratios corresponding to the received signal
are determined, and the demodulation result is obtained, thus
addressing the phase noise, improving the performance of receiving
the signal, and the efficiency and accuracy of demodulating the
signal.
[0130] Those skilled in the art shall appreciate that the
embodiments of the invention can be embodied as a method, a system
or a computer program product. Therefore the invention can be
embodied in the form of an all-hardware embodiment, an all-software
embodiment or an embodiment of software and hardware in
combination. Furthermore the invention can be embodied in the form
of a computer program product embodied in one or more computer
useable storage mediums (including but not limited to a disk
memory, a CD-ROM, an optical memory, etc.) in which computer
useable program codes are contained.
[0131] The invention has been described in a flow chart and/or a
block diagram of the method, the device (system) and the computer
program product according to the embodiments of the invention. It
shall be appreciated that respective flows and/or blocks in the
flow chart and/or the block diagram and combinations of the flows
and/or the blocks in the flow chart and/or the block diagram can be
embodied in computer program instructions. These computer program
instructions can be loaded onto a general-purpose computer, a
specific-purpose computer, an embedded processor or a processor of
another programmable data processing device to produce a machine so
that the instructions executed on the computer or the processor of
the other programmable data processing device create means for
performing the functions specified in the flow(s) of the flow chart
and/or the block(s) of the block diagram.
[0132] These computer program instructions can also be stored into
a computer readable memory capable of directing the computer or the
other programmable data processing device to operate in a specific
manner so that the instructions stored in the computer readable
memory create an article of manufacture including instruction means
which perform the functions specified in the flow(s) of the flow
chart and/or the block(s) of the block diagram.
[0133] These computer program instructions can also be loaded onto
the computer or the other programmable data processing device so
that a series of operational steps are performed on the computer or
the other programmable data processing device to create a computer
implemented process so that the instructions executed on the
computer or the other programmable device provide steps for
performing the functions specified in the flow(s) of the flow chart
and/or the block(s) of the block diagram.
[0134] Although the embodiments of the invention have been
described, those skilled in the art benefiting from the underlying
inventive concept can make additional modifications and variations
to these embodiments. Therefore the appended claims are intended to
be construed as encompassing the embodiments and all the
modifications and variations coming into the scope of the
invention.
[0135] Evidently those skilled in the art can make various
modifications and variations to the invention without departing
from the spirit and scope of the invention. Thus the invention is
also intended to encompass these modifications and variations
thereto so long as the modifications and variations come into the
scope of the claims appended to the invention and their
equivalents.
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