WO2018120896A1 - 一种信号解调的方法及装置 - Google Patents

一种信号解调的方法及装置 Download PDF

Info

Publication number
WO2018120896A1
WO2018120896A1 PCT/CN2017/099938 CN2017099938W WO2018120896A1 WO 2018120896 A1 WO2018120896 A1 WO 2018120896A1 CN 2017099938 W CN2017099938 W CN 2017099938W WO 2018120896 A1 WO2018120896 A1 WO 2018120896A1
Authority
WO
WIPO (PCT)
Prior art keywords
bit
constellation point
model
likelihood
likelihood probability
Prior art date
Application number
PCT/CN2017/099938
Other languages
English (en)
French (fr)
Inventor
陈跃潭
汪玲
倪立华
Original Assignee
大唐移动通信设备有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 大唐移动通信设备有限公司 filed Critical 大唐移动通信设备有限公司
Priority to US16/474,570 priority Critical patent/US10530525B1/en
Priority to JP2019535884A priority patent/JP7016368B2/ja
Priority to EP17886385.8A priority patent/EP3565208B1/en
Publication of WO2018120896A1 publication Critical patent/WO2018120896A1/zh

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/06—Dc level restoring means; Bias distortion correction ; Decision circuits providing symbol by symbol detection
    • H04L25/067—Dc level restoring means; Bias distortion correction ; Decision circuits providing symbol by symbol detection providing soft decisions, i.e. decisions together with an estimate of reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0054—Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/08—Modifications for reducing interference; Modifications for reducing effects due to line faults ; Receiver end arrangements for detecting or overcoming line faults
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/18—Phase-modulated carrier systems, i.e. using phase-shift keying
    • H04L27/22—Demodulator circuits; Receiver circuits
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
    • H04L27/34—Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
    • H04L27/34—Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems
    • H04L27/345—Modifications of the signal space to allow the transmission of additional information
    • H04L27/3461—Modifications of the signal space to allow the transmission of additional information in order to transmit a subchannel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
    • H04L27/34—Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems
    • H04L27/38—Demodulator circuits; Receiver circuits

Definitions

  • the present invention relates to the field of communications technologies, and in particular, to a method and apparatus for signal demodulation.
  • phase noise refers to the random variation of the phase of the output signal of the system caused by various noises of the system (for example, various RF devices).
  • phase noise causes random changes in the phase of the transmitted signal
  • the phase noise will have a certain negative impact on the demodulation process of the received signal, such as affecting the calculation result of Log Likelihood Ratio (LLR), and modulation
  • LLR Log Likelihood Ratio
  • MCS Modulation and Coding Scheme
  • the higher the MCS the lower the accuracy of the LLR calculation.
  • the higher the MCS the faster the transmission rate of the communication.
  • the base station demodulates the received signal by using the following two methods:
  • the first way is to superimpose the received signal equal to a standard constellation point with a noise of a complex Gaussian distribution, that is, Gaussian white noise, and then calculate the LLR of the received signal.
  • the second way is to introduce phase noise on the transmitted signal constellation point and then calculate the LLR of the received signal.
  • the receiving performance of the receiving end is lowered, and the efficiency of demodulation is lowered.
  • Embodiments of the present invention provide a method and apparatus for signal demodulation, which are used to overcome phase noise, improve signal reception performance, signal demodulation efficiency, and signal demodulation accuracy when performing signal demodulation.
  • a method of signal demodulation comprising:
  • phase noise parameter represents the phase noise signal and is a random variable
  • phase rotation angle extraction conversion process and a discretization process are performed on the likelihood probability ratio integration model to obtain a likelihood probability ratio discrete model, wherein the phase rotation angle characterizes an angle of phase rotation obtained based on the phase noise signal;
  • the likelihood ratio corresponding to the received signal is determined, and the demodulation result is obtained.
  • the likelihood ratio ratio integration model is established based on the received signal and the preset phase noise parameter, and specifically includes:
  • Determining, according to a preset bit, a sequence number of the bit, and an association relationship between the constellation points respectively determining a first constellation point set and a second constellation point set corresponding to each bit, wherein the first constellation point set corresponding to one bit is one a set of corresponding constellation points when the bit is 0, and a second constellation point set corresponding to one bit is a set of corresponding constellation points when one bit is 1.
  • a first likelihood probability model corresponding to each bit based on each of the first constellation point sets and the phase noise parameter and establishing a second corresponding to each bit based on each of the second constellation point sets and the phase noise parameter respectively a likelihood probability model, wherein a first likelihood probability model corresponding to one bit represents a likelihood probability corresponding to a bit being 0, and a second likelihood probability model corresponding to a bit characterizes a likelihood probability corresponding to a bit of 1;
  • a likelihood probability ratio integral model corresponding to each bit is established respectively.
  • a first likelihood probability model corresponding to each bit including:
  • phase rotation angle based on the phase noise parameter, wherein the phase rotation angle is positively correlated with the complex exponent of e as a parameter of the phase noise parameter;
  • a second likelihood probability model corresponding to each bit including:
  • phase rotation angle based on the phase noise parameter, wherein the phase rotation angle is positively correlated with the complex exponent of e as a parameter of the phase noise parameter;
  • phase probability rotation extraction conversion processing and the discretization processing are performed on the likelihood probability ratio integration model to obtain a likelihood probability ratio discrete model, which specifically includes:
  • the likelihood probability is multiplied by the numerator and denominator of the integral model and the preset extraction transformation parameters respectively to obtain a likelihood probability ratio phase compensation model, wherein the parameters of the conversion parameter and the complex exponent of e are negative values of the phase noise parameter are extracted. Positive correlation, and the likelihood probability is compared with the phase compensation model to phase compensate the received signal to achieve phase rotation;
  • the likelihood ratio is compared with the phase compensation model, and the approximation is performed based on the max-log-map algorithm to obtain the likelihood probability ratio discrete model.
  • the likelihood probability ratio is compared with the discrete model and the first Euclidean distance and the first The difference between the two Euclidean distances is positively correlated, and the first Euclidean distance characterizes the minimum Euclidean distance of each constellation point in the first constellation point set when the bit is 0, and the second Euclidean distance representation bit is 1 The minimum Euclidean distance of each constellation point in the corresponding second constellation point set.
  • the likelihood ratio of the received signal is determined based on the likelihood probability ratio discrete model, and the demodulation result is obtained, which specifically includes:
  • a demodulation result of the received signal is determined based on a likelihood ratio corresponding to each bit in the received signal.
  • a device for signal demodulation comprising:
  • An acquiring unit configured to acquire a received signal, where the received signal includes a phase noise signal
  • phase rotation angle characterizes an angle of phase rotation obtained based on the phase noise signal
  • a determining unit configured to determine a likelihood ratio corresponding to the received signal based on the likelihood probability ratio discrete model, and obtain a demodulation result.
  • the establishing unit is specifically configured to:
  • Determining, according to a preset bit, a sequence number of the bit, and an association relationship between the constellation points respectively determining a first constellation point set and a second constellation point set corresponding to each bit, wherein the first constellation point set corresponding to one bit is one a set of corresponding constellation points when the bit is 0, and a second constellation point set corresponding to one bit is a set of corresponding constellation points when one bit is 1.
  • a first likelihood probability model corresponding to each bit based on each of the first constellation point sets and the phase noise parameter and establishing a second corresponding to each bit based on each of the second constellation point sets and the phase noise parameter respectively a likelihood probability model, wherein a first likelihood probability model corresponding to one bit represents a likelihood probability corresponding to a bit being 0, and a second likelihood probability model corresponding to a bit characterizes a likelihood probability corresponding to a bit of 1;
  • a likelihood probability ratio integral model corresponding to each bit is established respectively.
  • the establishing unit is further configured to:
  • phase rotation angle based on the phase noise parameter, wherein the phase rotation angle is positively correlated with the complex exponent of e as a parameter of the phase noise parameter;
  • the establishing unit is further configured to:
  • phase rotation angle based on the phase noise parameter, wherein the phase rotation angle is positively correlated with the complex exponent of e as a parameter of the phase noise parameter;
  • the discrete unit is specifically used for:
  • the likelihood probability is multiplied by the numerator and denominator of the integral model and the preset extraction transformation parameters respectively to obtain a likelihood probability ratio phase compensation model, wherein the parameters of the conversion parameter and the complex exponent of e are negative values of the phase noise parameter are extracted. Positive phase Off, and the likelihood probability is compared with the phase compensation model to phase compensate the received signal to achieve phase rotation;
  • the likelihood ratio is compared with the phase compensation model, and the approximation is performed based on the max-log-map algorithm to obtain the likelihood probability ratio discrete model.
  • the likelihood probability ratio is compared with the discrete model and the first Euclidean distance and the first The difference between the two Euclidean distances is positively correlated, and the first Euclidean distance characterizes the minimum Euclidean distance of each constellation point in the first constellation point set when the bit is 0, and the second Euclidean distance representation bit is 1 The minimum Euclidean distance of each constellation point in the corresponding second constellation point set.
  • the likelihood ratio corresponding to the received signal is determined based on the likelihood probability ratio discrete model, and when the demodulation result is obtained, the determining unit is further configured to:
  • a demodulation result of the received signal is determined based on a likelihood ratio corresponding to each bit in the received signal.
  • the received signal is obtained, wherein the received signal includes a phase noise signal; and based on the received signal and the preset phase noise parameter, a likelihood probability ratio integral model is established, wherein the phase noise parameter represents the phase noise signal, a random variable; a phase rotation angle extraction conversion process for the likelihood probability ratio integration model, and a discretization process to obtain a likelihood probability ratio discrete model, wherein the phase rotation angle characterizes the angle of the phase rotation obtained based on the phase noise signal; The likelihood probability ratio is determined by the discrete model, and the likelihood ratio corresponding to the received signal is determined to obtain a demodulation result.
  • phase compensation is performed on the received signal, and discrete processing is performed to determine a ratio likelihood ratio of the received signal, and a demodulation result is obtained to overcome Phase noise improves signal reception performance, signal demodulation efficiency, and signal demodulation accuracy.
  • FIG. 1 is a flowchart of a method for signal demodulation in an embodiment of the present invention
  • FIG. 2 is a schematic diagram of a constellation diagram of signal demodulation according to an embodiment of the present invention
  • FIG. 3 is a schematic structural diagram of an apparatus for signal demodulation according to an embodiment of the present invention.
  • GSM Global System of Mobile communication
  • CDMA Code Division Multiple Access
  • WCDMA Wideband Code Division Multiple Access
  • GPRS General Packet Radio Service
  • LTE Long Term Evolution
  • LTE-A Advanced Long Term Evolution
  • UMTS Universal Mobile Telecommunication System
  • the user equipment includes but is not limited to a mobile station (Mobile Station, MS), a mobile terminal (Mobile Terminal), a mobile phone (Mobile Telephone), a mobile phone (handset). And portable devices, etc., the user equipment can communicate with one or more core networks via a Radio Access Network (RAN), for example, the user equipment can be a mobile phone (or "cellular"
  • RAN Radio Access Network
  • the user equipment can be a mobile phone (or "cellular"
  • the telephone device, the computer with wireless communication function, etc., the user equipment can also be a mobile device that is portable, pocket-sized, handheld, built-in, or in-vehicle.
  • a base station may refer to a device in an access network that communicates with a wireless terminal over one or more sectors over an air interface.
  • the base station can be used to convert the received air frame to the IP packet as a router between the wireless terminal and the rest of the access network, wherein the remainder of the access network can include an Internet Protocol (IP) network.
  • IP Internet Protocol
  • the base station can also coordinate attribute management of the air interface.
  • the base station may be a Base Transceiver Station (BTS) in GSM or CDMA, or may be a base station (NodeB) in WCDMA, or may be an evolved base station in LTE (NodeB or eNB or e-NodeB, evolutional Node B), the invention is not limited.
  • BTS Base Transceiver Station
  • NodeB base station
  • NodeB evolved base station
  • LTE Long Term Evolutional Node B
  • a signal demodulation method is designed. The method is: establishing a likelihood probability ratio integral model corresponding to the received signal, and performing phase compensation on the received signal and discrete processing based on the likelihood probability ratio integral model, determining a ratio of the likelihood ratio of the received signal, and obtaining a demodulation result.
  • Step 100 The base station acquires a received signal.
  • the base station obtains the received signal sent by the sending device.
  • the sending device may be a base station, a commercial terminal, or a test terminal.
  • the received signal transmitted by the terminal or the base station generates phase noise independent of Gaussian noise during signal processing, and the base station generates phase noise in the process of acquiring the received signal, and the phase signal is also included in the received signal, and Gaussian noise.
  • Step 101 The base station determines a constellation point corresponding to the received signal.
  • the base station acquires a preset bit, a bit number, and an association relationship between the constellation points based on the modulation of the received signal.
  • the modulation may be Quadrature Amplitude Modulation (Quadrature Amplitude Modulation, QAM), which can be 16QAM or 64QAM.
  • the base station acquires each bit included in the received signal, and determines a first constellation point set and a second constellation point set corresponding to each bit based on the preset bit, the bit sequence number, and the association relationship between the constellation points.
  • the first constellation point set corresponding to one bit is a set of constellation points corresponding to one bit when the bit is 0, and the second constellation point set corresponding to one bit is a set of corresponding constellation points when one bit is 1.
  • every 4 bits are mapped onto a constellation point map, and the sequence numbers of 4 bits are assumed to be bit a, bit b, bit c, and bit d, respectively.
  • bit a the base station determines that the first constellation point set corresponding to the bit a is ⁇ 1, 2, 3, 4, 5, 6, 7, 8 ⁇ .
  • bit a 1, the base station determines that the first constellation point set corresponding to the bit a is ⁇ 9, 10, 11, 12, 13, 14, 15, 16 ⁇ .
  • Step 102 The base station establishes a constellation point probability model based on the constellation point corresponding to the received signal and the preset phase noise parameter.
  • the base station determines the phase rotation angle of the received signal based on the preset phase noise parameter, wherein the phase rotation angle is positively correlated with the complex index of e as the parameter of the phase noise parameter, ie, e j ⁇ .
  • the base station then establishes a received signal model based on the product of the constellation point and the phase rotation angle.
  • the received signal model can be expressed by the following formula:
  • y is the received signal
  • x is the constellation point
  • ⁇ is the phase noise, which is a random variable obeying the uniform distribution of (-a, a), a ⁇ (- ⁇ , ⁇ ), n is Gaussian noise, and e j ⁇ is Phase rotation angle.
  • the base station separately establishes a constellation point probability model corresponding to each constellation point based on the received signal model.
  • the constellation point probability model can be expressed by the following formula:
  • x k is a constellation point
  • k is a natural number
  • ⁇ is a phase noise
  • ⁇ is a random variable, obeys (-a , a) uniform distribution
  • a ⁇ (- ⁇ , ⁇ ) is the standard deviation
  • ⁇ is the pi.
  • Step 103 The base station separately establishes a first likelihood probability model corresponding to each bit in the received signal when the bit is 0 based on the constellation point probability model.
  • the base station establishes a first likelihood probability model corresponding to each bit in the received signal when the bit is 0, based on the first constellation point set corresponding to each bit in the received signal and the corresponding constellation point probability model.
  • the first likelihood probability model corresponding to one bit represents a likelihood probability corresponding to the bit when the bit is 0, and the first likelihood probability model corresponding to one bit and each constellation included in the first constellation point set corresponding to one bit The sum of the constellation point probability models corresponding to the points is positively correlated.
  • the first likelihood probability model can be expressed by the following formula:
  • x r is the constellation point
  • x i ⁇ x m , x m, 0 represents the corresponding first constellation point set when bm is 0, i, m
  • ⁇ is phase noise, which is a random variable obeying the uniform distribution of (-a, a), a ⁇ (- ⁇ , ⁇ ).
  • Step 104 The base station separately establishes a second likelihood probability model corresponding to each bit in the received signal when the bit is 1 based on the constellation point probability model.
  • the base station establishes a second likelihood probability model corresponding to each bit of the received signal when the bit is 1 based on the second constellation point set corresponding to each bit in the received signal and the corresponding constellation point probability model.
  • the second likelihood probability model corresponding to one bit represents a likelihood probability corresponding to the bit when the bit is 1, and the second likelihood probability model corresponding to one bit and each constellation included in the second constellation point set corresponding to one bit The sum of the constellation point probability models corresponding to the points is positively correlated.
  • the second likelihood probability model can be expressed by the following formula:
  • ⁇ is phase noise, which is a random variable obeying the uniform distribution of (-a, a), a ⁇ (- ⁇ , ⁇ ).
  • Step 105 The base station establishes a likelihood probability ratio integral model corresponding to the received signal based on the logarithm of the ratio of the first likelihood probability model and the second likelihood probability model corresponding to each bit in the received signal.
  • the optional likelihood ratio-integral model can be expressed by the following formula:
  • p is the likelihood ratio
  • x i is the constellation point
  • X m, 0 denotes a corresponding first constellation point set when bit b m is
  • x r is a constellation point
  • X m, 1 denotes a corresponding second constellation point set when bit b m is 1
  • m, i, r are natural numbers
  • ⁇ For phase noise, it is a random variable, obeying the uniform distribution of (-a, a), a ⁇ (- ⁇ , ⁇ ).
  • Step 106 The base station multiplies the likelihood probability ratio numerator and the denominator of the integral model by a preset extraction conversion parameter to obtain a likelihood probability ratio phase compensation model.
  • step 106 when step 106 is performed, the extraction conversion parameter is positively correlated with the parameter that the complex exponent of e is a negative value of the phase noise parameter, and the likelihood probability is compared with the phase compensation model to phase compensate the received signal to achieve phase rotation.
  • the likelihood probability ratio phase compensation model can be expressed in the following manner:
  • p is the likelihood ratio
  • e -2j ⁇ is the extraction transformation parameter
  • x i is a constellation point
  • X m, 0 represents a first set of constellation points corresponding to a bit b m of
  • x r is a constellation point
  • X m, 1 represents a second constellation point set corresponding to a bit b m of 1
  • m , i, r is a natural number
  • ⁇ is phase noise
  • is a random variable obeys the uniform distribution of (-a, a), a ⁇ (- ⁇ , ⁇ ).
  • Step 107 The base station performs discrete summation on the likelihood probability ratio phase compensation model, and performs approximation processing based on the max-log-map algorithm to obtain a likelihood probability ratio discrete model.
  • the base station performs discrete summation on the likelihood probability ratio phase compensation model, and performs approximation processing based on the max-log-map algorithm to obtain the likelihood probability ratio discrete model, the likelihood probability ratio discrete model and the first European style.
  • the distance is positively correlated with the difference of the second Euclidean distance, and the first Euclidean distance characterizes a minimum Euclidean distance from each constellation point in the first set of constellation points when the bit is 0, and the second Euclidean distance representation bit is The minimum Euclidean distance from each constellation point in the corresponding second constellation point set at 1 o'clock.
  • the first Euclidean distance can be expressed by the following formula:
  • T can be 16 and a can be x i is a constellation point, X m, 0 represents a corresponding first constellation point set when the bit b m is 0, and i is a natural number.
  • the second Euclidean distance can be expressed by the following formula:
  • T can be 16 and a can be x r is the constellation point, X m, 1 represents the corresponding second constellation point set when the bit b m is 1, and r is a natural number.
  • the likelihood probability ratio discrete model can be expressed by the following formula:
  • the first Euclidean distance corresponding to each bit included in the received signal and the corresponding second Euclidean distance are respectively determined, and then the first Euclidean distance corresponding to each bit is calculated respectively.
  • the difference between the two Euclidean distances, and further, the likelihood ratio corresponding to each bit is determined by the difference corresponding to each bit, that is, the demodulation result of the received signal is obtained.
  • the minimum Euclidean distance from each constellation point in the corresponding first constellation point set when the first Euclidean distance representation bit is 0, and the second Euclidean distance representation bit is 1 and the corresponding second constellation point set The minimum Euclidean distance of each constellation point in the middle, therefore, the likelihood ratio of each bit corresponding to the QAM corresponding to the QAM can be directly determined, that is, the demodulation result of the received signal is obtained.
  • the modulation mode of the received signal is 16QAM
  • every 4 bits are mapped to a constellation point map.
  • the constellation point after the phase compensation is set is (x, y).
  • D can be (x d , y e ) is the coordinate of each constellation point in the first constellation point set corresponding to bm, and m is a natural number.
  • the first Euclidean distance s 1 is a minimum value of the Euclidean distance s(m, 0) corresponding to each constellation point in the first constellation point set.
  • D can be (x u , y w ) is the coordinates of each constellation point in the second constellation point set corresponding to bm.
  • the second Euclidean distance s 2 is a minimum value of the Euclidean distance s(m, 1) corresponding to each constellation point in the second constellation point set.
  • the base station can first determine the first constellation point set and the second constellation point set corresponding to each bit in the received signal. Then, the base station obtains a first Euclidean distance corresponding to each bit by calculating a minimum Euclidean distance between each bit and each constellation point in the corresponding first constellation point set, and calculates each bit and corresponding second by calculating each bit The second Euclidean distance is obtained by the minimum Euclidean distance between each constellation point in the constellation point set. Further, the base station determines, according to the difference between the first Euclidean distance and the second Euclidean distance corresponding to each bit, the corresponding likelihood of each bit. Rate ratio, thereby obtaining a demodulation result of the received signal.
  • FIG. 3 a schematic structural diagram of a device for demodulating a signal, in the embodiment of the present invention, the signal demodulating device specifically includes:
  • a device for signal demodulation comprising:
  • the acquiring unit 30 is configured to acquire a received signal, where the received signal includes a phase noise signal;
  • the establishing unit 31 is configured to establish a likelihood probability ratio integration model based on the received signal and the preset phase noise parameter, wherein the phase noise parameter represents the phase noise signal and is a random variable;
  • the discrete unit 32 is configured to perform a phase rotation angle extraction conversion process on the likelihood probability ratio integration model, and a discretization process to obtain a likelihood probability ratio discrete model, wherein the phase rotation angle represents an angle of the phase rotation obtained based on the phase noise signal ;
  • the determining unit 33 is configured to determine a likelihood ratio corresponding to the received signal based on the likelihood probability ratio discrete model, and obtain a demodulation result.
  • the establishing unit 31 is specifically configured to:
  • Determining, according to a preset bit, a sequence number of the bit, and an association relationship between the constellation points respectively determining a first constellation point set and a second constellation point set corresponding to each bit, wherein the first constellation point set corresponding to one bit is one a set of corresponding constellation points when the bit is 0, and a second constellation point set corresponding to one bit is a set of corresponding constellation points when one bit is 1.
  • a likelihood probability ratio integral model corresponding to each bit is established respectively.
  • the establishing unit 31 is further configured to:
  • phase rotation angle based on the phase noise parameter, wherein the phase rotation angle is positively correlated with the complex exponent of e as a parameter of the phase noise parameter;
  • the establishing unit 31 is further configured to:
  • phase rotation angle based on the phase noise parameter, wherein the phase rotation angle is positively correlated with the complex exponent of e as a parameter of the phase noise parameter;
  • the discrete unit 32 is specifically used for:
  • the likelihood probability is multiplied by the numerator and denominator of the integral model and the preset extraction transformation parameters respectively to obtain a likelihood probability ratio phase compensation model, wherein the parameters of the conversion parameter and the complex exponent of e are negative values of the phase noise parameter are extracted. Positive correlation, and the likelihood probability is compared with the phase compensation model to phase compensate the received signal to achieve phase rotation;
  • the likelihood ratio is compared with the phase compensation model, and the approximation is performed based on the max-log-map algorithm to obtain the likelihood probability ratio discrete model.
  • the likelihood probability ratio is compared with the discrete model and the first Euclidean distance and the first The difference between the two Euclidean distances is positively correlated, and the first Euclidean distance characterizes the minimum Euclidean distance of each constellation point in the first constellation point set when the bit is 0, and the second Euclidean distance representation bit is 1 The minimum Euclidean distance of each constellation point in the corresponding second constellation point set.
  • the likelihood ratio corresponding to the received signal is determined based on the likelihood ratio ratio discrete model, and when the demodulation result is obtained, the determining unit 33 is further configured to:
  • a demodulation result of the received signal is determined based on a likelihood ratio corresponding to each bit in the received signal.
  • the received signal is obtained, wherein the received signal includes a phase noise signal; and based on the received signal and the preset phase noise parameter, a likelihood probability ratio integral model is established, wherein the phase noise parameter represents the phase noise signal, a random variable; a phase rotation angle extraction conversion process for the likelihood probability ratio integration model, and a discretization process to obtain a likelihood probability ratio discrete model, wherein the phase rotation angle characterizes the angle of the phase rotation obtained based on the phase noise signal; Likelihood probability ratio discrete model, determining the likelihood ratio corresponding to the received signal, obtaining demodulation result.
  • phase compensation is performed on the received signal, and discrete processing is performed to determine a ratio likelihood ratio of the received signal, and a demodulation result is obtained to overcome Phase noise improves signal reception performance, signal demodulation efficiency, and signal demodulation accuracy.
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Digital Transmission Methods That Use Modulated Carrier Waves (AREA)

Abstract

本发明公开了一种信号解调的方法及装置，该方法为获取接收信号，其中，接收信号包含相位噪声信号；基于接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，其中，相位噪声参数表征相位噪声信号，是一个随机变量；对似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，其中，相位旋转角度表征基于相位噪声信号获得的相位旋转的角度；基于似然概率比离散模型，确定接收信号对应的似然概率比，获得解调结果，这样，基站通过对接收信号进行相位补偿和离散计算，获得解调结果，克服了相位噪声，提高了信号接收的性能，信号解调的效率，以及信号解调的准确性。

Description

一种信号解调的方法及装置
本申请要求在2016年12月29日提交中国专利局、申请号为201611248829.9、发明名称为“一种信号解调的方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及通信技术领域，尤其涉及一种信号解调的方法及装置。
背景技术
在长期演进(Long Term Evolution，LTE)系统中，终端或基站的发射信号在信号处理的过程中，会生成独立于高斯白噪声的相位噪声。所谓相位噪声，是指系统(如，各种射频器件)在各种噪声的作用下引起的系统输出信号相位的随机变化。
由于相位噪声引起发射信号相位的随机变化，因此，相位噪声会给接收信号的解调过程带来一定的负面影响，如，影响对数似然比(LogLikelihood Ratio，LLR)的计算结果，而调制与编码策略(Modulation and Coding Scheme，MCS)等级越高，负面影响越大，如，MCS越高，LLR的计算精准度越低，其中，MCS越高，表征通信的传输速率越快。
现有技术下，基站对接收信号进行解调，主要采用以下两种方式：
第一种方式为：将接收信号等效成一个标准星座点的信号叠加上一个复高斯分布的噪声即高斯白噪声，然后，计算接收信号的LLR。
但是，采用第一种方式，仅考虑了高斯白噪声的影响，而没有对相位噪声进行相应的处理，获得的LLR的结果并不精确。
第二种方式为：在发射信号星座点上引入相位噪声，然后，计算接收信号的LLR。
但是，采用第二种方式，在进行相位噪声验证时，会降低接收端的接收性能，并且会降低解调的效率。
发明内容
本发明实施例提供一种信号解调的方法及装置，用于在进行信号解调时，克服相位噪声，提高基站信号接收的性能，信号解调的效率，以及信号解调的准确性。
本发明实施例提供的具体技术方案如下：
一种信号解调的方法，包括：
获取接收信号，其中，接收信号包含相位噪声信号；
基于接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，其中，相位噪声参数表征相位噪声信号，是一个随机变量；
对似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，其中，相位旋转角度表征基于相位噪声信号获得的相位旋转的角度；
基于似然概率比离散模型，确定接收信号对应的似然概率比，获得解调结果。
较佳的，基于接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，具体包括：
获取接收信号中包含的每一个比特和相应的序号；
基于预设的比特、比特的序号和星座点之间的关联关系，分别确定每一个比特对应的第一星座点集合和第二星座点集合，其中，一个比特对应的第一星座点集合为一个比特为0时对应的星座点的集合，并且一个比特对应的第二星座点集合为一个比特为1时对应的星座点的集合；
基于各个第一星座点集合，以及相位噪声参数，分别建立每一个比特对应的第一似然概率模型，以及基于各个第二星座点集合，以及相位噪声参数，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第一似然概率模型表征比特为0时对应的似然概率，并且一个比特对应的第二似然概率模型表征比特为1时对应的似然概率；
基于各个比特的第一似然概率模型和相应的第二似然概率模型的比值的对数，分别建立每一个比特对应的似然概率比积分模型。
较佳的，基于各个第一星座点集合，以及相位噪声参数，分别建立每一个比特对应的第一似然概率模型，包括：
基于相位噪声参数，确定相位旋转角度，其中，相位旋转角度与e的复指数为相位噪声参数的参数呈正相关；
基于每一个星座点与相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率模型；
基于各个第一星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第一似然概率模型，其中，一个比特对应的第一似然概率模型与一个比特对应的第一星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
较佳的，基于各个第二星座点集合，以及相位噪声参数，分别建立每一个比特对应的第二似然概率模型，包括：
基于相位噪声参数，确定相位旋转角度，其中，相位旋转角度与e的复指数为相位噪声参数的参数呈正相关；
基于每一个星座点与相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率 模型；
基于各个第二星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第二似然概率模型与一个比特对应的第二星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
较佳的，对似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，具体包括：
将似然概率比积分模型的分子和分母分别与预设的提取转化参数相乘，获得似然概率比相位补偿模型，其中，提取转化参数与e的复指数为相位噪声参数的负值的参数呈正相关，并且似然概率比相位补偿模型表征对接收信号进行相位补偿，实现相位旋转；
对似然概率比相位补偿模型，进行离散求和，并基于max-log-map算法，进行近似处理，获得似然概率比离散模型，其中，似然概率比离散模型与第一欧式距离与第二欧式距离的差值呈正相关，并且，第一欧式距离表征比特为0时与相应的第一星座点集合中的各个星座点的最小欧氏距离，并且第二欧式距离表征比特为1时与相应的第二星座点集合中的各个星座点的最小欧式距离。
较佳的，基于似然概率比离散模型，确定接收信号对应的似然概率比，获得解调结果，具体包括：
基于似然概率比离散模型，分别确定接收信号中的每一个比特对应的第一欧式距离，以及对应的第二欧式距离；
基于接收信号中各个比特对应的第一欧式距离以及对应的第二欧式距离，分别确定每一个比特对应的似然概率比，其中，一个比特对应的似然概率比与一个比特对应的第一欧式距离与一个比特对应的第二欧式距离的差值呈正相关；
基于接收信号中每一个比特对应的似然概率比，确定接收信号的解调结果。
一种信号解调的装置，包括：
获取单元，用于获取接收信号，其中，接收信号包含相位噪声信号；
建立单元，用于基于接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，其中，相位噪声参数表征相位噪声信号，是一个随机变量；
离散单元，用于对似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，其中，相位旋转角度表征基于相位噪声信号获得的相位旋转的角度；
确定单元，用于基于似然概率比离散模型，确定接收信号对应的似然概率比，获得解调结果。
较佳的，在基于接收信号，以及预设的相位噪声参数，建立似然概率比积分模型时，建立单元具体用于：
获取接收信号中包含的每一个比特和相应的序号；
基于预设的比特、比特的序号和星座点之间的关联关系，分别确定每一个比特对应的第一星座点集合和第二星座点集合，其中，一个比特对应的第一星座点集合为一个比特为0时对应的星座点的集合，并且一个比特对应的第二星座点集合为一个比特为1时对应的星座点的集合；
基于各个第一星座点集合，以及相位噪声参数，分别建立每一个比特对应的第一似然概率模型，以及基于各个第二星座点集合，以及相位噪声参数，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第一似然概率模型表征比特为0时对应的似然概率，并且一个比特对应的第二似然概率模型表征比特为1时对应的似然概率；
基于各个比特的第一似然概率模型和相应的第二似然概率模型的比值的对数，分别建立每一个比特对应的似然概率比积分模型。
较佳的，在基于各个第一星座点集合，以及相位噪声参数，分别建立每一个比特对应的第一似然概率模型时，建立单元还用于：
基于相位噪声参数，确定相位旋转角度，其中，相位旋转角度与e的复指数为相位噪声参数的参数呈正相关；
基于每一个星座点与相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率模型；
基于各个第一星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第一似然概率模型，其中，一个比特对应的第一似然概率模型与一个比特对应的第一星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
较佳的，在基于各个第二星座点集合，以及相位噪声参数，分别建立每一个比特对应的第二似然概率模型时，建立单元还用于：
基于相位噪声参数，确定相位旋转角度，其中，相位旋转角度与e的复指数为相位噪声参数的参数呈正相关；
基于每一个星座点与相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率模型；
基于各个第二星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第二似然概率模型与一个比特对应的第二星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
较佳的，在对似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型时，离散单元具体用于：
将似然概率比积分模型的分子和分母分别与预设的提取转化参数相乘，获得似然概率比相位补偿模型，其中，提取转化参数与e的复指数为相位噪声参数的负值的参数呈正相 关，并且似然概率比相位补偿模型表征对接收信号进行相位补偿，实现相位旋转；
对似然概率比相位补偿模型，进行离散求和，并基于max-log-map算法，进行近似处理，获得似然概率比离散模型，其中，似然概率比离散模型与第一欧式距离与第二欧式距离的差值呈正相关，并且，第一欧式距离表征比特为0时与相应的第一星座点集合中的各个星座点的最小欧氏距离，并且第二欧式距离表征比特为1时与相应的第二星座点集合中的各个星座点的最小欧式距离。
较佳的，在基于似然概率比离散模型，确定接收信号对应的似然概率比，获得解调结果时，确定单元还用于：
基于似然概率比离散模型，分别确定接收信号中的每一个比特对应的第一欧式距离，以及对应的第二欧式距离；
基于接收信号中各个比特对应的第一欧式距离以及对应的第二欧式距离，分别确定每一个比特对应的似然概率比，其中，一个比特对应的似然概率比与一个比特对应的第一欧式距离与一个比特对应的第二欧式距离的差值呈正相关；
基于接收信号中每一个比特对应的似然概率比，确定接收信号的解调结果。
本发明实施例中，获取接收信号，其中，接收信号包含相位噪声信号；基于接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，其中，相位噪声参数表征相位噪声信号，是一个随机变量；对似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，其中，相位旋转角度表征基于相位噪声信号获得的相位旋转的角度；基于似然概率比离散模型，确定接收信号对应的似然概率比，获得解调结果。这样，通过建立接收信号对应的似然概率比积分模型，并基于似然概率比积分模型，对接收信号进行相位补偿，以及离散处理，确定接收信号对应似然概率比，获得解调结果，克服了相位噪声，提高了信号接收的性能，信号解调的效率，以及信号解调的准确性。
附图说明
图1为本发明实施例中信号解调的方法的流程图；
图2为本发明实施例中信号解调的星座图示意图；
图3为本发明实施例中信号解调的装置的结构示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员 在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
应理解，本发明的技术方案可以应用于各种通信系统，例如：全球移动通讯(Global System of Mobile communication，GSM)系统、码分多址(Code Division Multiple Access，CDMA)系统、宽带码分多址(Wideband Code Division Multiple Access，WCDMA)系统、通用分组无线业务(General Packet Radio Service，GPRS)、长期演进(Long Term Evolution，LTE)系统、先进的长期演进(Advanced long term evolution，LTE-A)系统、通用移动通信系统(Universal Mobile Telecommunication System，UMTS)等。
还应理解，在本发明实施例中，用户设备(User Equipment，UE)包括但不限于移动台(Mobile Station，MS)、移动终端(Mobile Terminal)、移动电话(Mobile Telephone)、手机(handset)及便携设备(portable equipment)等，该用户设备可以经无线接入网(Radio Access Network，RAN)与一个或多个核心网进行通信，例如，用户设备可以是移动电话(或称为“蜂窝”电话)、具有无线通信功能的计算机等，用户设备还可以是便携式、袖珍式、手持式、计算机内置的或者车载的移动装置。
在本发明实施例中，基站(例如，接入点)可以是指接入网中在空中接口上通过一个或多个扇区与无线终端通信的设备。基站可用于将收到的空中帧与IP分组进行相互转换，作为无线终端与接入网的其余部分之间的路由器，其中接入网的其余部分可包括网际协议(IP)网络。基站还可协调对空中接口的属性管理。例如，基站可以是GSM或CDMA中的基站(Base Transceiver Station，BTS)，也可以是WCDMA中的基站(NodeB)，还可以是LTE中的演进型基站(NodeB或eNB或e-NodeB，evolutional Node B)，本发明并不限定。
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，并不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
为了使基站在信号解调时，克服相位噪声，提高信号接收的性能，信号解调的效率，以及信号解调的准确性，本发明实施例中，设计了一种信号解调的方法，该方法为，建立接收信号对应的似然概率比积分模型，并基于似然概率比积分模型，对接收信号进行相位补偿，以及离散处理，确定接收信号对应似然概率比，获得解调结果。
下面结合附图对本申请优选的实施方式进行详细说明。
参阅图1所示，本发明实施例中，对信号解调的具体流程如下：
步骤100：基站获取接收信号。
实际应用中，基站获取发送设备发送的接收信号，可选的，发送设备可以是基站，可以是商用终端，还可以是测试终端。
终端或基站发送的接收信号在信号处理的过程中，会生成独立于高斯噪声的相位噪声，以及基站在获取接收信号的过程中，也会生成相位噪声，接收信号中还包含相位噪声，以及高斯噪声。
步骤101：基站确定接收信号对应的星座点。
实际应用中，基站基于接收信号的调制的方式，获取预设的比特、比特的序号以及星座点之间的关联关系，可选的，调制的方式可以为4正交振幅调制(Quadrature Amplitude Modulation，QAM)，可以为16QAM，还可以为64QAM。
基站获取接收信号中包含的各个比特，并基于预设的比特、比特的序号，以及星座点之间的关联关系，确定每一个比特对应的第一星座点集合和第二星座点集合。其中，一个比特对应的第一星座点集合为一个比特为0时对应的星座点的集合，并且一个比特对应的第二星座点集合为一个比特为1时对应的星座点的集合。
例如，参阅图2所示，为16QAM对应的星座点图，每4个比特映射到一个星座点图上，假设4个比特的序号分别为比特a，比特b，比特c，比特d。当比特a为0时，基站确定比特a对应的第一星座点集合为{1，2，3，4，5，6，7，8}。当比特a为1时，基站确定比特a对应的第一星座点集合为{9，10，11，12，13，14，15，16}。
步骤102：基站基于接收信号对应的星座点，以及预设的相位噪声参数，建立星座点概率模型。
实际应用中，基站基于预设的相位噪声参数，确定接收信号的相位旋转角度，其中，相位旋转角度与e的复指数为相位噪声参数的参数即ejθ呈正相关。
然后，基站基于星座点与相位旋转角度的乘积，建立接收信号模型。
可选的，接收信号模型可以采用以下公式表示：
y＝xejθ+n
其中，y为接收信号，x为星座点，θ为相位噪声，是一个随机变量，服从(-a，a)的均匀分布，a∈(-∞,∞)，n为高斯噪声，ejθ为相位旋转角度。
进一步地，基站基于接收信号模型，分别建立每一个星座点对应的星座点概率模型。
可选的，星座点概率模型可以采用以下公式表示：
Figure PCTCN2017099938-appb-000001
其中，p(y|x＝xk，θ)为一个星座点xk对应的星座点概率模型，xk为星座点，k为自然数，θ为相位噪声，是一个随机变量，服从(-a，a)的均匀分布，a∈(-∞,∞)，σ为标准偏差，π为圆周率。
步骤103：基站基于星座点概率模型，分别建立接收信号中每一个比特为0时对应的第一似然概率模型。
实际应用中，基站基于接收信号中每一个比特对应的第一星座点集合，以及相应的星座点概率模型，分别建立接收信号中每一个比特为0时对应的第一似然概率模型。其中，一个比特对应的第一似然概率模型表征比特为0时对应的似然概率，并且，一个比特对应的第一似然概率模型与一个比特对应的第一星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
可选的，第一似然概率模型可以采用以下公式表示：
Figure PCTCN2017099938-appb-000002
其中，p(y|bm＝0)为第一似然概率，xr为星座点，xi∈xm，xm，0表示bm为0时对应的第一星座点集合，i，m为自然数，θ为相位噪声，是一个随机变量，服从(-a，a)的均匀分布，a∈(-∞,∞)。
步骤104：基站基于星座点概率模型，分别建立接收信号中每一个比特为1时对应的第二似然概率模型。
实际应用中，基站基于接收信号中每一个比特对应的第二星座点集合，以及相应的星座点概率模型，分别建立接收信号中每一个比特为1时对应的第二似然概率模型。其中，一个比特对应的第二似然概率模型表征比特为1时对应的似然概率，并且，一个比特对应的第二似然概率模型与一个比特对应的第二星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
可选的，第二似然概率模型可以采用以下公式表示：
Figure PCTCN2017099938-appb-000003
其中，p(y|bm＝1)为第二似然概率，xr为星座点，xr∈xm，xm，1表示bm为1时对应的第一星座点集合，r，m为自然数，θ为相位噪声，是一个随机变量，服从(-a，a)的均匀分布，a∈(-∞,∞)。
步骤105：基站基于接收信号中各个比特对应的第一似然概率模型和第二似然概率模型的比值的对数，建立接收信号对应的似然概率比积分模型。
实际应用中，在执行步骤105时，可选的，似然概率比积分模型可以采用以下公式表示：
Figure PCTCN2017099938-appb-000004
其中，p为似然概率比，p(y|bm＝0)为第一似然概率，p(y|bm＝1)为第二似然概率，xi为星座点，Xm,0表示比特bm为0时对应的第一星座点集合，xr为星座点，Xm,1表示比特bm为1时对应的第二星座点集合，m，i，r为自然数，θ为相位噪声，是一个随机变量， 服从(-a，a)的均匀分布，a∈(-∞,∞)。
步骤106：基站将似然概率比积分模型的分子和分母分别与预设的提取转化参数相乘，获得似然概率比相位补偿模型。
实际应用中，执行步骤106时，提取转化参数与e的复指数为相位噪声参数的负值的参数呈正相关，并且似然概率比相位补偿模型表征对接收信号进行相位补偿，实现相位旋转。
可选的，似然概率比相位补偿模型可以采用以下方式表示：
Figure PCTCN2017099938-appb-000005
其中，p为似然概率比，e-2jθ为提取转化参数，p(y|bm＝0)为第一似然概率，p(y|bm＝1)为第二似然概率，xi为星座点，Xm,0表示比特bm为0时对应的第一星座点集合，xr为星座点，Xm,1表示比特bm为1时对应的第二星座点集合，m，i，r为自然数，θ为相位噪声，是一个随机变量，服从(-a，a)的均匀分布，a∈(-∞,∞)。
步骤107：基站对似然概率比相位补偿模型，进行离散求和，并基于max-log-map算法，进行近似处理，获得似然概率比离散模型。
实际应用中，基站对似然概率比相位补偿模型，进行离散求和，并基于max-log-map算法，进行近似处理，获得似然概率比离散模型，似然概率比离散模型与第一欧式距离与第二欧式距离的差值呈正相关，并且，第一欧式距离表征比特为0时与相应的第一星座点集合中的各个星座点的最小欧氏距离，并且第二欧式距离表征比特为1时与相应的第二星座点集合中的各个星座点的最小欧式距离。
可选的，第一欧式距离可以采用以下公式表示：
Figure PCTCN2017099938-appb-000006
其中，s1为第一欧式距离，可选的，T可以为16并且a可以为
Figure PCTCN2017099938-appb-000007
xi为星座点，Xm,0表示比特bm为0时对应的第一星座点集合，i为自然数。
可选的，第二欧式距离可以采用以下公式表示：
Figure PCTCN2017099938-appb-000008
其中，s2为第二欧式距离，可选的，T可以为16并且a可以为
Figure PCTCN2017099938-appb-000009
xr为星座点，Xm,1表示比特bm为1时对应的第二星座点集合，r为自然数。
可选的，似然概率比离散模型可以采用以下公式表示：
Figure PCTCN2017099938-appb-000010
其中，P为似然概率比，s1为第一欧式距离，s2为第二欧式距离。
这样，基于似然概率比离散模型，先分别确定接收信号中包含的每一个比特对应的第一欧式距离和相应的第二欧式距离，然后，分别计算每一个比特对应的第一欧式距离与第二欧式距离的差值，进一步地，通过每一个比特对应的差值，确定每一个比特对应的似然概率比，即获得接收信号的解调结果。
进一步地，由于第一欧式距离表征比特为0时与相应的第一星座点集合中的各个星座点的最小欧氏距离，并且第二欧式距离表征比特为1时与相应的第二星座点集合中的各个星座点的最小欧式距离，因此，还可以直接根据QAM对应的星座图，确定每一个比特对应的似然概率比，即获得接收信号的解调结果。
可选的，参阅图2所示，若接收信号的调制方式为16QAM，则每4个比特映射到一个星座点图上。设定相位补偿后的星座点为(x，y)。
可选的，比特bm＝0与相应的第一星座点集合中各个星座点之间的欧式距离可以采用以下公式表示：
s(m，0)＝(D-|xd-2D|)2+(D-|ye-2D|)2
其中，s(m，0)为欧式距离，可选的，D可以为
Figure PCTCN2017099938-appb-000011
(xd，ye)为bm对应的第一星座点集合中的各个星座点的坐标，m为自然数。
则第一欧式距离s1为第一星座点集合中的各个星座点对应的欧式距离s(m，0)的最小值。
可选的，比特bm＝1与相应的第二星座点集合中各个星座点之间的欧式距离还可以可以采用以下公式表示：
s(m，1)＝(xu+D)2+(D-|yw-2D|)2
其中，s(m，1)为欧式距离，可选的，D可以为
Figure PCTCN2017099938-appb-000012
(xu，yw)为bm对应的第二星座点集合中的各个星座点的坐标。
则第二欧式距离s2为第二星座点集合中的各个星座点对应的欧式距离s(m，1)的最小值。
这样，基站就可以先确定接收信号中各个比特对应的第一星座点集合和第二星座点集合。然后，基站通过计算每一个比特与相应的第一星座点集合中的各个星座点之间的最小欧式距离，获得每一个比特对应的第一欧式距离，并通过计算每一个比特与相应的第二星座点集合中的各个星座点之间的最小欧式距离，获得第二欧氏距离。进一步地，基站根据每一个比特对应的第一欧式距离与第二欧式距离之间的差值，确定每一个比特对应似然概 率比，从而获得接收信号的解调结果。
基于上述实施例，参阅图3所示，信号解调的装置的结构示意图，本发明实施例中，信号解调装置具体包括：
一种信号解调的装置，包括：
获取单元30，用于获取接收信号，其中，接收信号包含相位噪声信号；
建立单元31，用于基于接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，其中，相位噪声参数表征相位噪声信号，是一个随机变量；
离散单元32，用于对似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，其中，相位旋转角度表征基于相位噪声信号获得的相位旋转的角度；
确定单元33，用于基于似然概率比离散模型，确定接收信号对应的似然概率比，获得解调结果。
较佳的，在基于接收信号，以及预设的相位噪声参数，建立似然概率比积分模型时，建立单元31具体用于：
获取接收信号中包含的每一个比特和相应的序号；
基于预设的比特、比特的序号和星座点之间的关联关系，分别确定每一个比特对应的第一星座点集合和第二星座点集合，其中，一个比特对应的第一星座点集合为一个比特为0时对应的星座点的集合，并且一个比特对应的第二星座点集合为一个比特为1时对应的星座点的集合；
基于各个第一星座点集合，以及相位噪声参数，分别建立每一个比特对应的第一似然概率模型，以及基于各个第二星座点集合，以及相位噪声参数，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第一似然概率模型表征比特为0时对应的似然概率，并且一个比特对应的第二似然概率模型表征比特为1时对应的似然概率；
基于各个比特的第一似然概率模型和相应的第二似然概率模型的比值的对数，分别建立每一个比特对应的似然概率比积分模型。
较佳的，在基于各个第一星座点集合，以及相位噪声参数，分别建立每一个比特对应的第一似然概率模型时，建立单元31还用于：
基于相位噪声参数，确定相位旋转角度，其中，相位旋转角度与e的复指数为相位噪声参数的参数呈正相关；
基于每一个星座点与相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率模型；
基于各个第一星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第一似然概率模型，其中，一个比特对应的第一似然概率模型与一个比特对应的 第一星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
较佳的，在基于各个第二星座点集合，以及相位噪声参数，分别建立每一个比特对应的第二似然概率模型时，建立单元31还用于：
基于相位噪声参数，确定相位旋转角度，其中，相位旋转角度与e的复指数为相位噪声参数的参数呈正相关；
基于每一个星座点与相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率模型；
基于各个第二星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第二似然概率模型与一个比特对应的第二星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
较佳的，在对似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型时，离散单元32具体用于：
将似然概率比积分模型的分子和分母分别与预设的提取转化参数相乘，获得似然概率比相位补偿模型，其中，提取转化参数与e的复指数为相位噪声参数的负值的参数呈正相关，并且似然概率比相位补偿模型表征对接收信号进行相位补偿，实现相位旋转；
对似然概率比相位补偿模型，进行离散求和，并基于max-log-map算法，进行近似处理，获得似然概率比离散模型，其中，似然概率比离散模型与第一欧式距离与第二欧式距离的差值呈正相关，并且，第一欧式距离表征比特为0时与相应的第一星座点集合中的各个星座点的最小欧氏距离，并且第二欧式距离表征比特为1时与相应的第二星座点集合中的各个星座点的最小欧式距离。
较佳的，在基于似然概率比离散模型，确定接收信号对应的似然概率比，获得解调结果时，确定单元33还用于：
基于似然概率比离散模型，分别确定接收信号中的每一个比特对应的第一欧式距离，以及对应的第二欧式距离；
基于接收信号中各个比特对应的第一欧式距离以及对应的第二欧式距离，分别确定每一个比特对应的似然概率比，其中，一个比特对应的似然概率比与一个比特对应的第一欧式距离与一个比特对应的第二欧式距离的差值呈正相关；
基于接收信号中每一个比特对应的似然概率比，确定接收信号的解调结果。
本发明实施例中，获取接收信号，其中，接收信号包含相位噪声信号；基于接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，其中，相位噪声参数表征相位噪声信号，是一个随机变量；对似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，其中，相位旋转角度表征基于相位噪声信号获得的相位旋转的角度；基于似然概率比离散模型，确定接收信号对应的似然概率比，获得解调 结果。这样，通过建立接收信号对应的似然概率比积分模型，并基于似然概率比积分模型，对接收信号进行相位补偿，以及离散处理，确定接收信号对应似然概率比，获得解调结果，克服了相位噪声，提高了信号接收的性能，信号解调的效率，以及信号解调的准确性。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
尽管已描述了本发明的优选实施例，但本领域内的技术人员一旦得知了基本创造性概念，则可对这些实施例作出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本发明范围的所有变更和修改。
显然，本领域的技术人员可以对本发明实施例进行各种改动和变型而不脱离本发明实施例的精神和范围。这样，倘若本发明实施例的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。

Claims (12)

  1. 一种信号解调的方法，其特征在于，包括：
    获取接收信号，其中，所述接收信号包含相位噪声信号；
    基于所述接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，其中，所述相位噪声参数表征所述相位噪声信号，是一个随机变量；
    对所述似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，其中，所述相位旋转角度表征基于所述相位噪声信号获得的相位旋转的角度；
    基于所述似然概率比离散模型，确定所述接收信号对应的似然概率比，获得解调结果。
  2. 如权利要求1所述的方法，其特征在于，基于所述接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，具体包括：
    获取所述接收信号中包含的每一个比特和相应的序号；
    基于预设的比特、比特的序号和星座点之间的关联关系，分别确定每一个比特对应的第一星座点集合和第二星座点集合，其中，一个比特对应的第一星座点集合为所述一个比特为0时对应的星座点的集合，并且一个比特对应的第二星座点集合为所述一个比特为1时对应的星座点的集合；
    基于各个第一星座点集合，以及所述相位噪声参数，分别建立每一个比特对应的第一似然概率模型，以及基于各个第二星座点集合，以及所述相位噪声参数，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第一似然概率模型表征比特为0时对应的似然概率，并且一个比特对应的第二似然概率模型表征比特为1时对应的似然概率；
    基于各个比特的第一似然概率模型和相应的第二似然概率模型的比值的对数，分别建立每一个比特对应的似然概率比积分模型。
  3. 如权利要求2所述方法，其特征在于，基于各个第一星座点集合，以及所述相位噪声参数，分别建立每一个比特对应的第一似然概率模型，包括：
    基于所述相位噪声参数，确定相位旋转角度，其中，所述相位旋转角度与e的复指数为所述相位噪声参数的参数呈正相关；
    基于每一个星座点与所述相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率模型；
    基于各个第一星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第一似然概率模型，其中，一个比特对应的第一似然概率模型与所述一个比特对应的第一星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
  4. 如权利要求2所述方法，其特征在于，基于各个第二星座点集合，以及所述相位噪声参数，分别建立每一个比特对应的第二似然概率模型，包括：
    基于所述相位噪声参数，确定相位旋转角度，其中，所述相位旋转角度与e的复指数为所述相位噪声参数的参数呈正相关；
    基于每一个星座点与所述相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率模型；
    基于各个第二星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第二似然概率模型与所述一个比特对应的第二星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
  5. 如权利要求2或3或4所述的方法，其特征在于，对所述似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，具体包括：将所述似然概率比积分模型的分子和分母分别与预设的提取转化参数相乘，获得似然概率比相位补偿模型，其中，所述提取转化参数与e的复指数为所述相位噪声参数的负值的参数呈正相关，并且所述似然概率比相位补偿模型表征对所述接收信号进行相位补偿，实现相位旋转；
    对所述似然概率比相位补偿模型，进行离散求和，并基于max-log-map算法，进行近似处理，获得似然概率比离散模型，其中，所述似然概率比离散模型与第一欧式距离与第二欧式距离的差值呈正相关，并且，所述第一欧式距离表征比特为0时与相应的第一星座点集合中的各个星座点的最小欧氏距离，并且所述第二欧式距离表征比特为1时与相应的第二星座点集合中的各个星座点的最小欧式距离。
  6. 如权利要求5所述的方法，其特征在于，基于所述似然概率比离散模型，确定所述接收信号对应的似然概率比，获得解调结果，具体包括：
    基于所述似然概率比离散模型，分别确定所述接收信号中的每一个比特对应的第一欧式距离，以及对应的第二欧式距离；
    基于所述接收信号中各个比特对应的第一欧式距离以及对应的第二欧式距离，分别确定每一个比特对应的似然概率比，其中，一个比特对应的似然概率比与所述一个比特对应的第一欧式距离与所述一个比特对应的第二欧式距离的差值呈正相关；
    基于所述接收信号中每一个比特对应的似然概率比，确定所述接收信号的解调结果。
  7. 一种信号解调的装置，其特征在于，包括：
    获取单元，用于获取接收信号，其中，所述接收信号包含相位噪声信号；
    建立单元，用于基于所述接收信号，以及预设的相位噪声参数，建立似然概率比积分模型，其中，所述相位噪声参数表征所述相位噪声信号，是一个随机变量；
    离散单元，用于对所述似然概率比积分模型进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型，其中，所述相位旋转角度表征基于所述相位噪声信号获得的相位旋转的角度；
    确定单元，用于基于所述似然概率比离散模型，确定所述接收信号对应的似然概率比，获得解调结果。
  8. 如权利要求7所述的装置，其特征在于，在基于所述接收信号，以及预设的相位噪声参数，建立似然概率比积分模型时，所述建立单元具体用于：
    获取所述接收信号中包含的每一个比特和相应的序号；基于预设的比特、比特的序号和星座点之间的关联关系，分别确定每一个比特对应的第一星座点集合和第二星座点集合，其中，一个比特对应的第一星座点集合为所述一个比特为0时对应的星座点的集合，并且一个比特对应的第二星座点集合为所述一个比特为1时对应的星座点的集合；
    基于各个第一星座点集合，以及所述相位噪声参数，分别建立每一个比特对应的第一似然概率模型，以及基于各个第二星座点集合，以及所述相位噪声参数，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第一似然概率模型表征比特为0时对应的似然概率，并且一个比特对应的第二似然概率模型表征比特为1时对应的似然概率；基于各个比特的第一似然概率模型和相应的第二似然概率模型的比值的对数，分别建立每一个比特对应的似然概率比积分模型。
  9. 如权利要求8所述装置，其特征在于，在基于各个第一星座点集合，以及所述相位噪声参数，分别建立每一个比特对应的第一似然概率模型时，所述建立单元还用于：
    基于所述相位噪声参数，确定相位旋转角度，其中，所述相位旋转角度与e的复指数为所述相位噪声参数的参数呈正相关；
    基于每一个星座点与所述相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率模型；
    基于各个第一星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第一似然概率模型，其中，一个比特对应的第一似然概率模型与所述一个比特对应的第一星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
  10. 如权利要求8所述装置，其特征在于，在基于各个第二星座点集合，以及所述相位噪声参数，分别建立每一个比特对应的第二似然概率模型时，所述建立单元还用于：
    基于所述相位噪声参数，确定相位旋转角度，其中，所述相位旋转角度与e的复指数为所述相位噪声参数的参数呈正相关；
    基于每一个星座点与所述相位旋转角度的乘积，分别建立每一个星座点对应的星座点概率模型；
    基于各个第二星座点集合中包含的星座点对应的星座点概率模型，分别建立每一个比特对应的第二似然概率模型，其中，一个比特对应的第二似然概率模型与所述一个比特对应的第二星座点集合中包含的各个星座点对应的星座点概率模型的和呈正相关。
  11. 如权利要求8或9或10所述的装置，其特征在于，在对所述似然概率比积分模型 进行相位旋转角度提取转化处理，以及离散化处理，获得似然概率比离散模型时，所述离散单元具体用于：
    将所述似然概率比积分模型的分子和分母分别与预设的提取转化参数相乘，获得似然概率比相位补偿模型，其中，所述提取转化参数与e的复指数为所述相位噪声参数的负值的参数呈正相关，并且所述似然概率比相位补偿模型表征对所述接收信号进行相位补偿，实现相位旋转；
    对所述似然概率比相位补偿模型，进行离散求和，并基于max-log-map算法，进行近似处理，获得似然概率比离散模型，其中，所述似然概率比离散模型与第一欧式距离与第二欧式距离的差值呈正相关，并且，所述第一欧式距离表征比特为0时与相应的第一星座点集合中的各个星座点的最小欧氏距离，并且所述第二欧式距离表征比特为1时与相应的第二星座点集合中的各个星座点的最小欧式距离。
  12. 如权利要求11所述的装置，其特征在于，在基于所述似然概率比离散模型，确定所述接收信号对应的似然概率比，获得解调结果时，所述确定单元还用于：
    基于所述似然概率比离散模型，分别确定所述接收信号中的每一个比特对应的第一欧式距离，以及对应的第二欧式距离；
    基于所述接收信号中各个比特对应的第一欧式距离以及对应的第二欧式距离，分别确定每一个比特对应的似然概率比，其中，一个比特对应的似然概率比与所述一个比特对应的第一欧式距离与所述一个比特对应的第二欧式距离的差值呈正相关；
    基于所述接收信号中每一个比特对应的似然概率比，确定所述接收信号的解调结果。
PCT/CN2017/099938 2016-12-29 2017-08-31 一种信号解调的方法及装置 WO2018120896A1 (zh)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US16/474,570 US10530525B1 (en) 2016-12-29 2017-08-31 Method and apparatus for demodulating signal
JP2019535884A JP7016368B2 (ja) 2016-12-29 2017-08-31 信号を復調するための方法および装置
EP17886385.8A EP3565208B1 (en) 2016-12-29 2017-08-31 Method and apparatus for demodulating signal

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201611248829.9A CN108259402B (zh) 2016-12-29 2016-12-29 一种信号解调的方法及装置
CN201611248829.9 2016-12-29

Publications (1)

Publication Number Publication Date
WO2018120896A1 true WO2018120896A1 (zh) 2018-07-05

Family

ID=62707771

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/099938 WO2018120896A1 (zh) 2016-12-29 2017-08-31 一种信号解调的方法及装置

Country Status (5)

Country Link
US (1) US10530525B1 (zh)
EP (1) EP3565208B1 (zh)
JP (1) JP7016368B2 (zh)
CN (1) CN108259402B (zh)
WO (1) WO2018120896A1 (zh)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019140562A1 (en) * 2018-01-17 2019-07-25 Qualcomm Incorporated Techniques and apparatuses for demodulation reference signal and phase rotation for sub-physical resource block allocation with two tone modulation
CN111479285B (zh) * 2019-01-23 2021-05-07 大唐移动通信设备有限公司 离散度确定方法及装置
CN115664561B (zh) * 2022-10-25 2024-04-02 中国科学院长春光学精密机械与物理研究所 极性度量的相位噪声通信检测方法、通信设备及介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101626357A (zh) * 2009-09-22 2010-01-13 北京理工大学 一种基于最大似然估计的mpsk系统载波同步方法
CN101854226A (zh) * 2008-12-30 2010-10-06 英特尔公司 用于为对数似然映射器优化比例因子的方法和广播接收器
CN103378921A (zh) * 2012-04-17 2013-10-30 华为技术有限公司 信号解调方法和装置
CN104871465A (zh) * 2012-12-20 2015-08-26 高通股份有限公司 用于降低相位噪声的系统和方法
WO2016035895A1 (en) * 2014-09-03 2016-03-10 Mitsubishi Electric Corporation System and method for recovering carrier phase in optical communications

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7469106B2 (en) * 2004-02-17 2008-12-23 Nortel Networks Limited Reference phase and amplitude estimation for coherent optical receiver
JP2007074618A (ja) 2005-09-09 2007-03-22 Sony Corp 無線通信装置及び無線通信方法、並びにコンピュータ・プログラム
US8213548B2 (en) 2006-04-04 2012-07-03 Qualcomm Incorporated Methods and apparatus for dynamic packet reordering
US7822069B2 (en) 2006-05-22 2010-10-26 Qualcomm Incorporated Phase correction for OFDM and MIMO transmissions
US8040985B2 (en) * 2007-10-09 2011-10-18 Provigent Ltd Decoding of forward error correction codes in the presence of phase noise
US20110158333A1 (en) 2008-09-12 2011-06-30 Hiroshi Nakano Radio communication system, radio communication method, and communication device
JP5100797B2 (ja) 2010-08-09 2012-12-19 株式会社九州テン デジタル無線信号の復調回路およびそれを用いた無線受信装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101854226A (zh) * 2008-12-30 2010-10-06 英特尔公司 用于为对数似然映射器优化比例因子的方法和广播接收器
CN101626357A (zh) * 2009-09-22 2010-01-13 北京理工大学 一种基于最大似然估计的mpsk系统载波同步方法
CN103378921A (zh) * 2012-04-17 2013-10-30 华为技术有限公司 信号解调方法和装置
CN104871465A (zh) * 2012-12-20 2015-08-26 高通股份有限公司 用于降低相位噪声的系统和方法
WO2016035895A1 (en) * 2014-09-03 2016-03-10 Mitsubishi Electric Corporation System and method for recovering carrier phase in optical communications

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3565208A4

Also Published As

Publication number Publication date
CN108259402A (zh) 2018-07-06
US20190393982A1 (en) 2019-12-26
JP7016368B2 (ja) 2022-02-04
EP3565208A4 (en) 2019-12-18
JP2020503786A (ja) 2020-01-30
US10530525B1 (en) 2020-01-07
EP3565208A1 (en) 2019-11-06
CN108259402B (zh) 2019-08-16
EP3565208B1 (en) 2022-05-11

Similar Documents

Publication Publication Date Title
WO2015103936A1 (zh) 降低功耗的方法与装置
US11134379B2 (en) Identity authentication method, device, and system
US8340202B2 (en) Method and apparatus for efficient soft modulation for gray-mapped QAM symbols
US20150331771A1 (en) Simulating Burst Errors in Mobile Data Communication Network System Level Simulations
WO2018120896A1 (zh) 一种信号解调的方法及装置
CN103929391B (zh) 一种频率校准方法及装置
WO2017181763A1 (zh) 载波相位恢复方法、装置及存储介质
US20130107802A1 (en) Downlink time difference determination in frame asynchronous systems
WO2015149709A1 (zh) 一种噪声功率估计方法及网络侧设备
KR20110068377A (ko) 무선 통신 시스템에서 비가우시안 채널을 기반으로 한 연판정 정보 생성 방법 및 장치
WO2020238867A1 (zh) 一种相位噪声抑制方法及装置
US9509362B2 (en) Method and apparatus for handling modified constellation mapping using a soft demapper
US10587388B2 (en) Method and apparatus for uplink control channel detection
TWI684375B (zh) 通訊方法、終端設備和網路設備
WO2017049633A1 (zh) 信干噪比的计算方法、装置和接收机
CN106712918A (zh) 无线通信资源分配方法及装置
US20220407485A1 (en) Digital variable gain adjustment on baseband chip
CN114697169B (zh) 一种载波相位测量方法及装置
CN111082891B (zh) 调整无线通信网络的处理算法的方法以及接收装置
US20230042213A1 (en) Methods and systems for optimizing computation of log-likelihood ratio (llr) for decoding modulated symbols
CN111181882B (zh) 无线通信系统的相位噪声补偿方法及系统
US10892876B2 (en) Methods and apparatus for dynamic acknowledgement list selection in detection of uplink control channel formats
CN112954718B (zh) Naics能力上报及naics参数配置方法、装置、用户设备及基站
CN107040330A (zh) 一种信号检测方法及装置
US9692556B1 (en) Communication system with I/Q imbalance adjustment mechanism and method of operation thereof

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17886385

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2019535884

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2017886385

Country of ref document: EP

Effective date: 20190729