CN110381003B - Multi-user signal detection method aiming at peak-to-average ratio suppression in SCMA-OFDM system - Google Patents

Multi-user signal detection method aiming at peak-to-average ratio suppression in SCMA-OFDM system Download PDF

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CN110381003B
CN110381003B CN201910676615.9A CN201910676615A CN110381003B CN 110381003 B CN110381003 B CN 110381003B CN 201910676615 A CN201910676615 A CN 201910676615A CN 110381003 B CN110381003 B CN 110381003B
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杨霖
张晓宁
岳光荣
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2614—Peak power aspects
    • H04L27/2621—Reduction thereof using phase offsets between subcarriers
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2697—Multicarrier modulation systems in combination with other modulation techniques

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a multi-user signal detection method aiming at peak-to-average power ratio suppression in a SCMA-OFDM system. The invention applies the selective mapping method to the transmitting end of the SCMA-OFDM system, and successfully reduces the PAPR of the signal. In consideration of sparsity of an SCMA-OFDM signal, the invention utilizes a message transfer algorithm to decode at a receiving end, and can adjust the transmission probability of each code word in each iteration process of the message transfer algorithm to finally and accurately recover the transmission signal under the condition of not transmitting sideband information after the characteristics of a phase alternative sequence and the characteristics of an SCMA codebook used are synthesized. Therefore, the invention realizes blind detection aiming at the selection mapping algorithm and does not bring obvious improvement of the computational complexity.

Description

Multi-user signal detection method aiming at peak-to-average ratio suppression in SCMA-OFDM system
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a multi-user signal detection method aiming at peak-to-average power ratio suppression in a SCMA-OFDM system.
Background
Driven by the rapid development of mobile internet and internet of things services, a large number of terminal device accesses and extreme user experiences in the future provide new requirements and huge challenges for mobile communication. Therefore, in order to meet the demand of future communication, a Fifth Generation mobile communication technology (5G) has come, wherein a wireless air interface technology is an important field of current 5G research, and a multiple access technology is a hot issue of research. In the face of these challenges and development vision in 5G, the conventional orthogonal Multiple Access technology has not been able to meet the requirements of future mobile communication development, so some Non-orthogonal Multiple Access (NOMA) technologies have been proposed. The SCMA technology is used as a non-orthogonal air interface technology for 5G wireless mobile communication, a non-orthogonal sparse coding superposition technology is adopted, and the SCMA technology can support more user connections under the same time-frequency resource condition. The invention adopts OFDM technology, and carries the SCMA signal block on the orthogonal sub-carrier of OFDM to transmit data. The OFDM technique has a problem of high PAPR of a signal, which causes nonlinear distortion of the signal when the signal is amplified at the transmitting end, and finally deteriorates BER performance of the system. Therefore, it is important how to reduce PAPR of SCMA-OFDM signal and improve performance of the system.
Disclosure of Invention
The invention aims to solve the problem of high PAPR of SCMA-OFDM signals, and adopts a method of distortion-free PAPR suppression, such as: the mapping algorithm and the partial sequence transmission algorithm are selected to reduce the PAPR of the SCMA-OFDM signal, so that the signal is not further distorted or is distorted as little as possible when passing through a signal amplifier, and a blind detection algorithm based on a message transfer algorithm is provided aiming at the PAPR restraining method, so that the calculation complexity of a receiving end is not obviously increased while sideband information is prevented from being transmitted, and the information transmission efficiency of the system is improved.
The technical scheme of the invention is as follows:
a multi-user signal detection method in an SCMA-OFDM system is based on a message transfer algorithm and used for multi-user signal detection in an uplink SCMA-OFDM system, a system model is assumed that J users share K resources in an SCMA signal block, one SCMA-OFDM signal occupies N orthogonal subcarriers, namely the SCMA-OFDM signal comprises N/K SCMA signal blocks, the number of constellation points adopted by each user is M, and the number of alternative sequences of a selective mapping algorithm adopted by the system is S.
The blind detection method comprises the following steps:
s1, setting parameters of the SCMA-OFDM system including user number J, time frequency resource K, codebook size M, alternative sequence number of selective mapping algorithm adopted by the system as S, and setting maximum iteration number as tmax；
S2, generating alternative phase sequence according to alternative sequence number S of selective mapping algorithm adopted by system
Figure BDA0002143500340000021
And finally determines the signal x with the smallest PAPR for transmissionj。
S3, initializing the message delivery probability value as:
Figure BDA0002143500340000022
wherein the content of the first and second substances,
Figure BDA0002143500340000023
refers to the user node u in the factor graphjTo the resource node rkWherein t is the number of iterations, xj＝(x1,j,x2,j…,xK,j)T∈CKRepresenting the codeword transmitted by the jth user;
s4, setting an initial value t of iteration times to be 1;
s5, updating the message of the resource node, and r for all resource nodeskAnd (3) calculating:
Figure BDA0002143500340000024
wherein, ykIs a received signal
Figure BDA0002143500340000025
The signal received by the k-th time-frequency resource,
Figure BDA0002143500340000026
refers to the user node u in the factor graphpTo the resource node rkMessage update procedure of xikRepresenting the set of users, ξ, connected to a resource node k in a factor graphk\ j represents the set after user j is removed;
s6, updating the message of user node, and updating all user nodes ujAnd (3) calculating:
Figure BDA0002143500340000027
s7, calculating the transmission probability of each code word according to the information obtained in the step S5
S7.1, calculating the code word sending probability when using the forward codebook:
Figure BDA0002143500340000031
s7.2, calculating the code word sending probability when the reverse codebook is used:
Figure BDA0002143500340000032
s8, estimating the phase candidate sequence according to the codeword transmission probability obtained in step S7, specifically:
s8.1, calculating a transmission probability value of each phase candidate sequence on each SCMA block, where i represents the ith SCMA block (the candidate phase sequence may also be estimated using euclidean distance):
Figure BDA0002143500340000033
wherein p iss,lIs an alternative sequence
Figure BDA0002143500340000034
Of the first element and ps,l∈(-1,1)。
S8.2, estimating alternative sequences used for processing signals at a transmitting end by using the probability value obtained in the step S8.1:
Figure BDA0002143500340000035
s9, using the alternative sequence information obtained in the step S8 for the next iteration of the message passing algorithm:
s9.1, correcting information in the user node:
Figure BDA0002143500340000036
Figure BDA0002143500340000037
s9.2, correcting the codebook:
Figure BDA0002143500340000038
Figure BDA0002143500340000041
s10, message iteration updating: judging whether t exceeds the maximum iteration number set in the step S1, if t is less than or equal to tmaxThen the flow proceeds to step S5, if t>tmaxIf yes, the iterative update of the message is terminated and the step S11 is entered;
s11, completing multi-user detection by using the updated probability distribution, and outputting the soft decision of the user as follows:
Figure BDA0002143500340000042
wherein ζjIs a set ζ representing a resource node connected to a user j in a factor graphj\ k then represents the removal of the set of resource nodes k.
The invention is based on that at the transmitting end of SCMA-OFDM system, the PAPR of the signal processed by the selective mapping method is effectively suppressed, but in order to accurately recover the transmitted signal at the receiving end, the selected alternative sequence must be transmitted together as sideband information, which results in the reduction of transmission efficiency. Therefore, there are many studies on blind detection algorithms for the selective mapping method, which estimate the selected candidate sequence at the receiving end, and although the transmission of side information is avoided, the blind detection algorithms also cause an increase in decoding complexity. The message passing algorithm is a low-complexity decoding algorithm provided based on the sparsity of an SCMA codebook, the algorithm can continuously calculate and update the sending probability of each code word in the iteration process, the invention estimates the selected phase alternative sequence at a receiving end by utilizing the sending probability of each code word calculated by the algorithm, and synchronously and effectively recovers the sending signal when the message passing algorithm reaches the maximum iteration times.
The invention has the beneficial effects that: the selective mapping method is applied to the transmitting end of the SCMA-OFDM system, and the PAPR of the signal is successfully reduced. In consideration of sparsity of an SCMA-OFDM signal, the invention utilizes a message transfer algorithm to decode at a receiving end, and can adjust the transmission probability of each code word in each iteration process of the message transfer algorithm to finally and accurately recover the transmission signal under the condition of not transmitting sideband information after the characteristics of a phase alternative sequence and the characteristics of an SCMA codebook used are synthesized. Therefore, the invention realizes blind detection aiming at the selection mapping algorithm and does not bring obvious improvement of the computational complexity.
Drawings
FIG. 1 is a block diagram of an uplink SCMA-OFDM system (taking the selective mapping algorithm as an example);
FIG. 2 is a schematic diagram of a SCMA-OFDM system receiving end blind detection algorithm;
FIG. 3 is a SCMA encoding schematic;
figure 4 is a SCMA factor graph.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the accompanying drawings and examples.
Considering an uplink SCMA-OFDM communication system model, where K frequency domain resource blocks are occupied by J users, a schematic block diagram is shown in fig. 1. Firstly, mapping bit data into a codeword to be transmitted according to a SCMA codebook one-to-one mapping principle of a bit data stream generated by a user, wherein the SCMA coding principle is shown in fig. 2; secondly, a plurality of groups of serial code words are converted into parallel data through serial-parallel conversion, the parallel data are in one-to-one correspondence to a plurality of orthogonal subcarriers of the SCMA-OFDM system, frequency domain signals are converted into time domain signals through N-point IFFT conversion, then the parallel time domain signals are converted into serial data through parallel-serial conversion, and finally the serial data are modulated onto carriers to be sent out. At a signal transmitting end, an SCMA (single chip multiple access) directly maps bit stream data into a multidimensional code word in a codebook, and the process combines two processes of modulation and spread spectrum in the traditional orthogonal multiple access scheme, so that a more efficient coding mode is obtained. At the receiving end, the signals received by the base station are the superposition of codewords sent by J different users, and because of the sparsity of the codewords, the base station end adopts an MPA algorithm to realize low-complexity detection and decoding.
Assuming that all users are synchronized, the signal received at the base station can be expressed as:
Figure BDA0002143500340000051
wherein y ═ y1,y2,…,yK)TRepresenting signals received at the base station, xj＝(x1,j,x2,j…,xK,j)TIndicating the code word sent by the jth user, hj＝(h1,j,h2,j…,hK,j)TIndicating channel state information between the base station and the jth user, n ═ n (n)1,n2,…,nK)TRepresents Additive White Gaussian Noise (AWGN) and obeys a distribution n-CN (0, sigma)2I)。
Since the SCMA-OFDM system uses OFDM multi-carrier to carry SCMA data for transmission, the SCMA-OFDM signal still has the problem of too high PAPR of the signal, so in the transmitting end of fig. 1, we can use a selective mapping algorithm to suppress the PAPR of the signal below a desired value.
The specific implementation steps of the selection mapping algorithm are as follows:
1. firstly, S mutually independent phase candidate sequences p are generateds＝(ps,1,ps,2,...,ps,N)T。
2. Will send a signal xjAnd alternative sequences
Figure BDA0002143500340000052
After multiplication, a new set of combined signals is obtained:
Xj,s＝(xj,1ps,1,xj,2ps,2,...,xj,Nps,N)T(s＝1,2...S)
3. the frequency domain signal is converted into a time domain signal by IFFT and then the PAPR of each joint signal is calculated:
Figure BDA0002143500340000061
Figure BDA0002143500340000062
5. according to the PAPR data calculated in step 4, selecting a joint signal with the smallest PAPR for transmission, where the signal may be represented as:
Figure BDA0002143500340000063
the PAPR of the SCMA-OFDM signal processed by the selective mapping algorithm may be described by using a Complementary Cumulative Distribution Function (CCDF), which is expressed as follows:
Pr(PAPR>PAPR0)＝p,0≤p≤1
when the number N of carriers is large enough, according to the central limit theorem, the real part and the imaginary part of the sampling point of the transmitted signal approximately satisfy gaussian distribution, the amplitude of the sampling point satisfies rayleigh distribution, and the probability density can be expressed as:
Figure BDA0002143500340000064
and the power distribution of the sampling signal obeys χ2Distribution, and mean 0 χ of unit variance2The power spectral density of the distribution can be expressed as p ═ e-yWe define
Figure BDA0002143500340000065
To represent the sparseness of the SCMA system, the CCDF of the SCMA-OFDM signal after processing by the selective mapping algorithm is as follows:
Pr(PAPR>PAPR0)＝[1-(1-e-PAPR 0)BN]S
as can be seen from the above equation, the PAPR of the signal at the transmitting end is effectively suppressed after the selective mapping algorithm is processed, but the following problems occur: selected phase candidate sequences for decoding at the receiving end using a message passing algorithm
Figure BDA0002143500340000066
Must be transmitted to the transmitting end together as sideband information, which certainly results in a reduction in transmission efficiency. Therefore, a plurality of blind detection algorithms aiming at the selective mapping algorithm are provided, wherein one of the blind detection algorithms is realized at a receiving end by using an ML algorithm, and the process is as follows:
Figure BDA0002143500340000071
although the above scheme can achieve blind detection, the attendant high computational complexity increase is unacceptable for any communication system. Considering that an SCMA receiving end adopts a message transfer algorithm which needs to continuously calculate and update information of user nodes in an iteration process, and an SCMA codebook has sparsity and certain symmetry, the invention provides a blind detection algorithm based on the message transfer algorithm to avoid transmitting sideband information and accurately recover a transmitted signal on the premise of not greatly increasing complexity.
Examples
In this embodiment, a Matlab simulation platform is used for the experiment.
The object of the present example is achieved by the steps of:
and S1, setting SCMA system parameters. The system parameters in this example are as follows: the number of users J equals 6, the number of time-frequency resources K equals 4, the number of subcarriers of a multicarrier N equals 256, the simulation channel equals AWGN, and the simulation times equals 106Maximum number of iterations tmax6, the selected candidate sequence number S is 4, the candidate sequence elements are {1, -1}, and the adopted codebook is shown in the following table:
Figure BDA0002143500340000072
Figure BDA0002143500340000073
Figure BDA0002143500340000074
Figure BDA0002143500340000081
Figure BDA0002143500340000082
Figure BDA0002143500340000083
s2, randomly generating S mutually independent alternative phase sequences p with the length of NsWherein the elements are {1, -1 }.
S3, multiplying the generated transmission signals by the S phase bit sequences respectively, that is:
Xj,s＝(xj,1ps,1,xj,2ps,2,...,xj,Nps,N)T(s＝1,2...S)
s4, performing IFFT operation on the joint signal obtained in step S3, and converting the frequency domain signal into a time domain signal:
Figure BDA0002143500340000084
and S5, calculating the PAPRs of the S combined signals, and selecting the signal with the minimum PAPR to pass through a Gaussian channel simulation model.
S6, initializing the message delivery probability value as:
Figure BDA0002143500340000085
wherein the content of the first and second substances,
Figure BDA0002143500340000086
refers to the user node u in the factor graphjTo the resource node rkWherein t is the number of iterations, xj＝(x1,j,x2,j…,xK,j)T∈CKRepresenting the codeword transmitted by the jth user;
s7, setting an initial value t of iteration times to be 1;
s8, message iteration updating: judging whether t exceeds the maximum iteration number set in the step S1, if t is less than or equal to tmaxThen the flow proceeds to step S9, if t>tmaxIf yes, the iterative update of the message is terminated and the step S15 is entered;
s9, updating the message of the resource node according to the factor graph F (t) (figure 3), and r all the resource nodeskAnd (3) calculating:
Figure BDA0002143500340000091
wherein, ykIs the signal received by the kth time-frequency resource of the received signal Y,
Figure BDA0002143500340000092
refers to the user node u in the factor graphpTo the resource node rkMessage update procedure of xikRepresenting the set of users, ξ, connected to a resource node k in a factor graphk\ j represents the set after user j is removed;
s10, updating the message of the user node according to the factor graph F (t) (figure 3), and updating all the user nodes ujAnd (3) calculating:
Figure BDA0002143500340000093
s11, calculating the transmission probability of each code word according to the information obtained in the step S10
S11.1, calculating the code word sending probability when using the forward codebook:
Figure BDA0002143500340000094
s11.2, calculating the code word sending probability when the reverse codebook is used:
Figure BDA0002143500340000095
s12, estimating the phase candidate sequence according to the codeword transmission probability obtained in step S10, specifically:
s12.1, calculating a transmission probability value of each phase alternative sequence on each SCMA block, wherein i represents the ith SCMA block:
Figure BDA0002143500340000096
s12.2, estimating alternative sequences used for processing signals at a transmitting end by using the probability value obtained in the step S12.1:
Figure BDA0002143500340000101
s13, using the alternative sequence information obtained in the step S8 for the next iteration of the message passing algorithm:
s13.1, correcting information in the user node:
Figure BDA0002143500340000102
Figure BDA0002143500340000103
s13.2, correcting the codebook:
Figure BDA0002143500340000104
Figure BDA0002143500340000105
s14, updating iteration times: t +1 and returns to step S8;
s15, completing multi-user detection by using the updated probability distribution, and outputting the soft decision of the user as follows:
Figure BDA0002143500340000106
wherein ζjIs a set ζ representing a resource node connected to a user j in a factor graphj\ k then represents the removal of the set of resource nodes k.
The method of the invention is adopted to carry out simulation test. We compare the BER performance of the proposed decoding algorithm with the conventional decoding algorithm for transmission side information. It can be seen that the BER performance of the proposed algorithm is only slightly lost in the case of low signal-to-noise ratio compared to the decoding algorithm for transmitting side information, and there is no difference between the BER performance of the proposed algorithm and the BER performance of the decoding algorithm for transmitting side information in the case of high signal-to-noise ratio. Since the algorithm estimates the candidate sequence more and more accurately as the signal-to-noise ratio increases, the performance of the algorithm should be theoretically equivalent to a decoding algorithm that transmits sideband information when the candidate sequence is estimated without errors. Compared with the algorithm which uses the ML mode to carry out blind detection, the algorithm provided by the invention has the advantage that the estimation process of the alternative sequence is synchronously carried out in the algorithm decoding process of the message passing process, so the calculation complexity of the algorithm is not obviously increased compared with the traditional message passing algorithm.

Claims (1)

  1. A multi-user signal detection method aiming at peak-to-average ratio suppression in a SCMA-OFDM system is characterized in that a system model is that J users share K resources in one SCMA signal block, one SCMA-OFDM signal occupies N orthogonal subcarriers, namely one SCMA-OFDM signal comprises N/K SCMA signal blocks, the number of constellation points adopted by each user is M, and the number of alternative sequences of a selective mapping algorithm adopted by the system is S; the detection method comprises the following steps:
    s1, setting parameters of the SCMA-OFDM system including user number J, time frequency resource K, codebook size M, alternative sequence number of selective mapping algorithm adopted by the system as S, and setting maximum iteration number as tmax；
    S2, generating an alternative phase sequence p according to the alternative sequence number S of the selective mapping algorithm adopted by the systems＝(ps,1,ps,2,...,ps,N)TAnd finally determines the signal x with the smallest PAPR for transmissionj；
    S3, initializing the message delivery probability value as:
    Figure FDA0002143500330000011
    wherein the content of the first and second substances,
    Figure FDA0002143500330000012
    refers to the user node u in the factor graphjTo the resource node rkWherein t is the number of iterations, xj＝(x1,j,x2,j…,xK,j)T∈CKRepresenting the codeword transmitted by the jth user;
    s4, setting an initial value t of iteration times to be 1;
    s5, updating the message of the resource node, and r for all resource nodeskAnd (3) calculating:
    Figure FDA0002143500330000013
    wherein, ykIs a received signal
    Figure FDA0002143500330000014
    The signal received by the k-th time-frequency resource,
    Figure FDA0002143500330000015
    refers to the user node u in the factor graphpTo the resource node rkMessage update procedure of xikRepresenting the set of users, ξ, connected to a resource node k in a factor graphk\ j represents the set after user j is removed;
    s6, updating the message of user node, and updating all user nodes ujAnd (3) calculating:
    Figure FDA0002143500330000016
    s7, calculating the transmission probability of each code word according to the information obtained in the step S5
    S7.1, calculating the code word sending probability when using the forward codebook:
    Figure FDA0002143500330000021
    s7.2, calculating the code word sending probability when the reverse codebook is used:
    Figure FDA0002143500330000022
    s8, estimating the phase candidate sequence according to the codeword transmission probability obtained in step S7, specifically:
    s8.1, calculating a transmission probability value of each phase alternative sequence on each SCMA block, wherein i represents the ith SCMA block:
    Figure FDA0002143500330000023
    s8.2, estimating alternative sequences used for processing signals at a transmitting end by using the probability value obtained in the step S8.1:
    Figure FDA0002143500330000024
    s9, using the alternative sequence information obtained in the step S8 for the next iteration of the message passing algorithm:
    s9.1, correcting information in the user node:
    Figure FDA0002143500330000025
    Figure FDA0002143500330000026
    s9.2, correcting the codebook:
    Figure FDA0002143500330000027
    Figure FDA0002143500330000028
    s10, message iteration updating: judging whether t exceeds the maximum iteration number set in the step S1, if t is less than or equal to tmaxThen the flow proceeds to step S5, if t>tmaxIf yes, the iterative update of the message is terminated and the step S11 is entered;
    s11, completing multi-user detection by using the updated probability distribution, and outputting the soft decision of the user as follows:
    Figure FDA0002143500330000031
    wherein ζjIs a set ζ representing a resource node connected to a user j in a factor graphj\ k then represents the removal of the set of resource nodes k.
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