Multi-user detection method and device based on compressed sensing under MUSA (multi user application architecture) system
阅读说明：本技术 Musa系统下基于压缩感知的多用户检测方法及装置 (Multi-user detection method and device based on compressed sensing under MUSA (multi user application architecture) system ) 是由 陈发堂 石贝贝 李小文 王丹 王华华 邓青 于 2019-10-28 设计创作，主要内容包括：本发明公开了MUSA系统下基于压缩感知的多用户检测方法及装置,所述方法包括将发送端用户分为活跃用户和不活跃用户,不活跃用户发送的符号设置为0,活跃用户发送的数据流调制成复数符号；然后对所有用户的符号进行扩展,并从相同的时频资源上发送；将接收端接收到的信号y采用验证误差正交匹配追踪算法进行检测,从而确定用户的稀疏度。本发明基于正交匹配追踪算法利用验证误差作为迭代停止条件,在迭代次数等于用户稀疏度时能够达到最小值来估计稀疏度,在稀疏度未知的情况下可对用户行为和数据的进行联合检测,相比传统的正交匹配追踪方法,本发明无需事先已知用户行为,灵活性更高,实用性更强,更适应于免调度的上行通信系统。(The invention discloses a multi-user detection method and a device based on compressed sensing under an MUSA (multi user architecture) system, wherein the method comprises the steps of dividing a user at a sending end into an active user and an inactive user, setting a symbol sent by the inactive user to be 0, and modulating a data stream sent by the active user into a plurality of symbols; then, the symbols of all users are expanded and sent from the same time frequency resource; and detecting the signal y received by the receiving end by adopting a verification error orthogonal matching tracking algorithm, thereby determining the sparsity of the user. The method utilizes the verification error as an iteration stop condition based on the orthogonal matching pursuit algorithm, can reach the minimum value to estimate the sparsity when the iteration times are equal to the sparsity of the user, and can carry out combined detection on the user behavior and the data under the condition that the sparsity is unknown.)
1. A orthogonal matching pursuit multi-user detection method adopting verification errors under an MUSA system comprises the steps that users at a sending end are divided into active users and inactive users, symbols sent by the inactive users are set to be 0, and data streams sent by the active users are modulated into complex symbols; then, the symbols of all users are expanded and sent from the same time frequency resource; detecting a signal y received by a receiving end by adopting a verification error orthogonal matching tracking algorithm, thereby determining the sparsity of a user and obtaining user data;
the verification error orthogonal matching tracking algorithm is characterized by comprising the following steps of:
s1, initializing iteration parameter i as 0, residual r0Y, error e0Y and atomic index set
S2, selecting an atom from the equivalent channel matrix, i.e.
s3, expanding the atom index set Γ of the ith iteration by using the atoms selected in the step S2i＝Γi-1∪u；
S4, calculating approximate solution of ith iteration of user signal x by using least square method
S5, calculating the residual r of the user signal x in the ith iterationiAnd error e of the ith iterationi；
S6, verifying errors, if the error obtained by the ith iterative computation is larger than the error obtained by the (i-1) th iterative computation, stopping iteration and turning to S7, otherwise, turning to S2;
s7, ending the process and outputting the user signal obtained by the i-1 st iteration
Wherein, akIs the kth column of the equivalent channel matrix a, where K is 1, 2., K is the total number of users in the system; r isiRepresenting the residual of the ith iteration; e.g. of the typeiError representing the ith iteration; gamma-shapediThe set of atomic indices representing the ith iteration.
2. The method of claim 1, wherein the signal received by the receiver is represented as:
y＝Ax+z
wherein y ═ y1,y2,...,yN)TA is an N × K-dimensional equivalent channel matrix including spreading sequences and channel gains; n represents the spreading sequence length of each user; x ═ x1,x2,...,xK)TM is the number of non-zero elements in x and represents the number of active users in the MUSA system; z is (z)1,z2,...,zn)TZ is the mean 0 and the variance σ2Gaussian noise.
3. The method of claim 2, wherein the spreading sequence is selected from Toeplitz matrix.
4. The method of claim 1, wherein the calculating the current approximate solution of the user signal x by using the least square method comprises:
wherein the content of the first and second substances,
5. The method of claim 4 wherein the orthogonal matching pursuit multi-user detection with verification error under MUSA system is characterized in that,
the residual calculation formula for the user signal x includes:
the formula for calculating the error of the user signal x includes:
wherein the content of the first and second substances,
6. The method of claim 1, wherein the step of comparing the error obtained from the current iteration with the error obtained from the previous iteration includes comparing the error obtained from the current iteration with a two-norm if the error obtained from the ith iteration is greater than the error obtained from the i-1 st iterationThe error two norm obtained by iterative computation, i.e. | | ei||2＞||ei-1||2Then the iteration stops going to S7, otherwise it goes to S2.
7. An apparatus for tracking multiple users by orthogonal matching with verification error under the MUSA system, the apparatus comprising:
a transmitting antenna: for transmitting user data;
a user allocation module: for dividing users into active users and inactive users;
the modulator: the symbol used for sending inactive users is set to 0, and the data stream sent by the active users is modulated;
a sequence spreading module: for performing sequence spreading on the modulated data;
a receiver: for receiving the modulated spread user data;
verifying an error quadrature matching tracking module: the method and the device are used for detecting the user data received by the receiver and jointly detecting the user behavior and data.
8. The apparatus of claim 7, wherein the validation error orthogonal matching pursuit module comprises an iterator, a least squares calculation module, a residual calculation module, an error calculation module, an atomic index calculation module, a validation module, and a signal output unit;
the iterator is used for generating iteration parameters;
the least square method calculation module is used for calculating and calculating the current approximate solution of the user signal;
the residual error calculation module is used for calculating the residual error of the current iteration;
the error calculation module is used for calculating the error of the current iteration;
the atomic index calculation module is used for acquiring an atomic index set of the current iteration;
the verification module is used for verifying whether the iteration process needs to be ended or not;
the signal output is used for outputting the detected user signal.
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to an uplink scheduling-free link multi-user detection technology of an MUSA (multi-user access system); in particular to a multi-user detection method and a device based on compressed sensing under an MUSA system.
Background
Multiple access technology is a key revolutionary technology that distinguishes different mobile communication systems. The first generation mobile communication to the fourth generation mobile communication all adopt an orthogonal multiple access scheme, in the orthogonal multiple access system, the number of users accessing the system is limited, and is difficult to meet application scenes such as 5G mass machine connection, and the like, but the non-orthogonal multiple access technology has larger system uplink throughput, the number of users accessing the system also has higher gain compared with the orthogonal multiple access scheme, and the gains of the throughput and the number of users accessing the system are both exchanged by the complexity of a receiver. The MUSA (Multiple user shared access) technology is used as one of non-orthogonal Multiple access, a scheduling-free strategy is adopted, and a large number of potential users in a system can be randomly and suddenly accessed, so that user signals have sparsity, and a compressed sensing theory can be introduced to recover sparse signals. Although the scheduling-free strategy reduces signaling overhead and omits a complex scheduling process, judging whether a user is active or not is a difficult problem before detecting data for a receiver. The conventional MMSE-SIC receiver can only detect user data under the condition of known user behavior, and obviously, the conventional MMSE-SIC receiver is no longer applicable in a scheduling-free uplink system.
Active users and inactive users exist in the multi-user shared access system, and statistical data shows that even in a busy period, the number of the active users is far smaller than the total number of the users in the system, so that user signals have sparsity, and the detection of user behaviors can be carried out by considering a compressed sensing algorithm with rich operation. An Orthogonal Matching Pursuit (OMP) algorithm is used as a representative of a greedy algorithm, sparsity of user signals needs to be known, the sparsity of the user signals is used as an iteration number ending algorithm of the greedy algorithm, but the sparsity of the user signals is not well obtained in practice, and particularly under an uplink scheduling-free condition. This makes the algorithm difficult to use in a practical MUSA system, which needs further improvement.
Disclosure of Invention
The invention provides a method for verifying the error, and determines the iteration times by verifying whether the error reaches the minimum value, thereby determining the user sparsity, solving the problem that the OMP algorithm is difficult to be used in practice, and leading the OMP algorithm to be more flexible and have stronger practicability.
Based on the problems in the prior art, the orthogonal matching pursuit algorithm is improved, so that the method does not depend on the sparsity of the user as the finish condition of the greedy algorithm, can carry out combined detection on the activity and the data of the user under the condition that the sparsity is unknown, and is more suitable for an actual communication system; therefore, the invention provides a multi-user detection method and device based on compressed sensing under an MUSA system.
A orthogonal matching pursuit multi-user detection method for an MUSA system by adopting verification errors can comprise the steps that users at a sending end are divided into active users and inactive users, symbols sent by the inactive users are set to be 0, and data streams sent by the active users are modulated into complex symbols; then, the symbols of all users are expanded and sent from the same time frequency resource; detecting a signal y received by a receiving end by adopting a verification error orthogonal matching tracking algorithm, thereby determining the sparsity of a user;
the verification error orthogonal matching pursuit algorithm comprises the following steps:
s1, initializing iteration parameter i as 0, residual r0Y, error e0Y and atomic index set
s3, expanding the atom index set Γ of the ith iteration by using the atoms selected in the step S2i＝Γi-1∪u；
S4, calculating approximate solution of ith iteration of user signal x by using least square method
S5, calculating the residual r of the user signal x in the ith iterationiAnd the calculated error e of the ith iterationi；
S6, verifying errors, stopping iteration and turning to S7 if the error obtained by the ith iteration calculation is larger than the error obtained by the iteration calculation of the (i-1) th iteration, and otherwise, turning to S2;
s7, ending the process and outputting user signals
Wherein, akIs the kth column of the equivalent channel matrix a, where K is 1, 2., K is the total number of users in the system; r isiRepresenting the residual of the ith iteration; e.g. of the typeiError representing the ith iteration; gamma-shapediThe set of atomic indices representing the ith iteration.
Further, in the
Further, in
y＝Ax+z
wherein y ═ y1,y2,...,yN)TA is an N × K-dimensional equivalent channel matrix including spreading sequences and channel gains; n represents the spreading sequence length of each user; x ═ x1,x2,...,xK)TThe number of non-zero elements in x is M, and M represents the number of inactive users in the MUSA system; z is (z)1,z2,...,zn)TZ is the mean 0 and the variance σ2Gaussian noise.
Further, the spreading sequence is selected from a Topritz matrix.
Further, the calculating the current approximate solution of the user signal x by using the least square method includes:
wherein A isΓiRepresenting the column index of gamma in the equivalent channel matrix AiA sub-matrix of (a); t denotes a transposed symbol.
Further, the residual calculation formula of the user signal x includes:
the formula for calculating the error of the user signal x includes:
wherein A isΓiRepresenting the equivalent channel matrix A with column index of gammaiA sub-matrix of (a); t represents a transposed symbol; q () represents a hard decision.
Further, selecting a relatively best atom from the equivalent channel matrix, i.e. selecting the atom with the greatest correlation with the residual error, and formulaIn the formula, the symbol < > represents the inner product operation, argmax represents | < ak,riAnd > get the corresponding k value when the maximum value is obtained.
If the error obtained by the current iterative computation is larger than the error obtained by the previous iterative computation, comparing the current iterative computation and the previous iterative computation by using a two-norm, and specifically, if the error obtained by the ith iterative computation is larger than the error obtained by the iterative computation of the (i-1) th iteration, namely, | | ei||2＞||ei-1||2Then the iteration stops going to S7, otherwise it goes to S2.
Wherein | | | purple hair2And represents a two-norm. When the number of iterations equals the sparsity, the error reaches a minimum. Therefore, if the error obtained by the current iterative computation is larger than the error obtained by the previous iterative computation, the iteration should be ended, i.e., if | | | ei||2＞||ei-1||2The algorithm stops.
In addition, the invention also provides an orthogonal matching pursuit multi-user detection device adopting verification errors under the MUSA system, which comprises:
a transmitting antenna: for transmitting user data;
a user allocation module: for dividing users into active users and inactive users;
the modulator: the symbol used for sending inactive users is set to 0, and the data stream sent by the active users is modulated;
a sequence spreading module: for performing sequence spreading on the modulated data;
a receiver: for receiving the modulated spread user data;
verifying an error quadrature matching tracking module: the method and the device are used for detecting the user data received by the receiver and jointly detecting the user behavior and data.
Further, the verification error orthogonal matching tracking module comprises an iterator, a least square method calculation module, a residual error calculation module, an atom index calculation module, a verification module and a signal output device;
the iterator is used for generating iteration parameters;
the least square method calculation module is used for calculating and calculating the current approximate solution of the user signal;
the residual error calculation module is used for calculating the residual error of the current iteration;
the error calculation module is used for calculating the error of the current iteration;
the atomic index calculation module is used for acquiring an atomic index set of the current iteration;
the verification module is used for verifying whether the iteration process needs to be ended or not;
the signal output is used for outputting the detected user signal.
The invention has the beneficial effects that:
the invention discloses an orthogonal matching pursuit multi-user detection method and device for an MUSA system by adopting a verification error. The traditional OMP algorithm needs the sparsity of a known user to detect, but is not well realized in practice, and particularly under the condition of uplink scheduling-free, the OMP algorithm is slightly ineffective in the practical system application. The invention can carry out the joint detection of the user activity and the data under the condition of unknown sparsity, can provide higher detection reliability, and is more suitable for an actual MUSA scheduling-free system.
Drawings
FIG. 1 is a block diagram of an uplink schedule-free MUSA system employed by the present invention;
FIG. 2 is a diagram of a complex constellation of user symbols;
FIG. 3 is a flow chart of a multi-user detection method of the present invention;
FIG. 4 is a variation curve of residual error and error under different iteration times;
FIG. 5 is a graph of the performance of the OMP algorithm and the present invention;
fig. 6 is a graph of performance under the influence of both SNR and the number of active users.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly and completely apparent, the technical solutions in the embodiments of the present invention are described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In this embodiment, taking fig. 1 as an example, the number of potential users in the MUSA system is K, the number of active users is M, data streams of the active users are modulated after being encoded, modulated symbols are taken from a subset consisting of non-zero elements of a constellation set of complex symbols in fig. 2, and symbols sent by inactive users are set to 0; k users independently and randomly select a complex spreading sequence to spread the symbols, and then the complex spreading sequence and the symbols are jointly superposed on N orthogonal OFDM subcarriers for transmission. After the data is transmitted through the channel, noise is passively generated, and the receiving end judges the behavior of each user through multi-user detection and detects data.
In one embodiment, the encoding process uses Turbo coding, and the modulation process selects a Quadrature Phase Shift Keying (QPSK) modulation scheme.
In an embodiment, as shown in fig. 3, the process of the receiving end determining the behavior of each user through multi-user detection and detecting data may include:
s1, initializing iteration parameter i as 0, residual r0Y, error e0Y and atomic index set
S2, selecting an atom from the equivalent channel matrix, i.e.
s3, expanding the atom index set Γ of the ith iteration by using the atoms selected in the step S2i＝Γi-1∪u；
S4, calculating approximate solution of ith iteration of user signal x by using least square method
S5, calculating the residual r of the user signal x in the ith iterationiAnd error e of the ith iterationi；
S6, verifying errors, if the error obtained by the ith iterative computation is larger than the error obtained by the (i-1) th iterative computation, stopping iteration and turning to S7, otherwise, turning to S2;
s7, ending the process and outputting the user signal obtained by the i-1 st iteration
In combination with the orthogonal matching tracking multi-user detection device adopting the verification error under the MUSA system provided by the invention, firstly, the transmitting antenna is utilized to transmit user data; meanwhile, a user allocation module is utilized to divide users into active users and inactive users, a modulator is used to set a symbol sent by an inactive user to be 0, and a data stream sent by the active user is modulated.
Preferably, the modulator is a QPSK modulator and the demodulator is a QPSK demodulator.
As a preferred implementation, the data needs to be sequence-spread after modulation, and the sequence spreading module in this embodiment is a module containing various sequence spreading functions or sequence spreading matrices, such as a module containing a toeplitz matrix, or a module containing a gaussian random matrix, a partial fourier matrix, or a bernoulli matrix.
Further, a user at a receiving end receives the modulated and expanded user data through a receiver, and an iterator is adopted to generate iteration parameters; forming a new atom index set by using an atom index calculation module; calculating the residual error of the current iteration by using a residual error calculation module, and calculating the error of the current iteration by using an error calculation module; and verifying whether the iteration needs to be finished by using the verification module, if the iteration does not need to be finished, outputting a signal '1' to the iterator, adding 1 to the iteration parameter of the iterator, continuously utilizing the other modules to carry out the iteration process until the verification module finds that the iteration needs to be finished, outputting a signal '0', and outputting a user signal by using the signal output device.
In this embodiment, the iterator functions primarily to allow the loop to proceed, and thus may be a combination of an adder and a flip-flop, which triggers the adder to add 1 in response to the output signal "1" when the verification module outputs the signal "1", and which directly outputs the user signal calculated during the last iteration in response to the output signal "0" when the verification module outputs the signal "0".
As a supplementary implementation, the user signal is calculated by a least squares calculation module, which may represent a digital processor including at least a least squares method.
Of course, other calculation modules can be used for calculation, such as a gradient descent method calculation module and a gauss-newton method calculation module.
In another embodiment, the present embodiment mainly transforms a loop process of a flow to form a new iteration mode, which specifically includes:
the received signal y is represented as:
y＝Ax+z
wherein y ═ y1,y2,...,yN)TA is an equivalent channel matrix of dimension N × K including spreading sequences and channel gains, and the spreading sequences are selected from the toeplitz matrix. x ═ x1,x2,...,xK)TThe number of non-zero elements in x is M, and z is (z)1,z2,...,zn)TZ is the mean 0 and the variance σ2Gaussian noise.
And 2, initializing iteration parameters. i is 0, residual r0Y, error e0Y, atomic index set
Wherein a iskIs the kth column of the equivalent channel matrix a, K being the total number of users in the system. Formula (II)
And 6, calculating residual errors and errors.
And 7, verifying the error. If enew||2＞||ei-1||2The iteration is stopped and step 9 is passed to, otherwise step 8 is passed to.
According to the variation trend of the residual error and the error in fig. 4, the residual error decreases with the increase of the number of iterations, and the number of iterations when the error reaches the minimum value is just equal to the user sparsity, so that the characteristic can be used as the iteration end condition.
And 9, ending. Outputting user signals
In the embodiment, when data is output, the current user data is directly output, instead of the user data generated in the last iteration, so that the data storage space is saved.
In one embodiment, the invention divides the user at the sending end into an active user and an inactive user, the symbol sent by the inactive user is set to be 0, and the data stream sent by the active user is modulated; then, the symbols of all users are expanded and sent from the same time frequency resource; and detecting the signal y received by the receiving end by adopting a verification error orthogonal matching tracking algorithm, thereby determining the sparsity of the user. The method utilizes the verification error as an iteration stop condition based on the orthogonal matching pursuit algorithm, can reach the minimum value to estimate the sparsity when the iteration times are equal to the sparsity of the user, and can carry out combined detection on the user behavior and the data under the condition that the sparsity is unknown.
In this embodiment, Matlab is used to perform simulation and comparative analysis on the OMP algorithm and the VE-OMP algorithm (VE-OMP for short) in combination with specific data, the simulation parameter settings are shown in table 1, and the performance simulation results are shown in fig. 5. And performing performance simulation on the VE-OMP algorithm under the condition of changing two factors of the signal-to-noise ratio and the number of active users, wherein the simulation result is shown in FIG. 6. As can be seen from fig. 5, the VE-OMP algorithm of the embodiment of the present invention can achieve performance equivalent to that of the OMP algorithm, but the sparsity of the OMP algorithm must be known, and the VE-OMP algorithm can detect user behavior and data under the condition that the sparsity is unknown, so that the VE-OMP algorithm has a higher practical application value. As can be seen from fig. 6, when the number of users is constant, the symbol error rate decreases with increasing SNR, and when the signal-to-noise ratio is constant, the symbol error rate increases with increasing number of users. Therefore, in practical application, the influence of the two factors on the performance should be considered comprehensively. The number of active users in an actual system generally does not exceed 10% of the total number of potential users of the system, under the simulation condition of the invention, the total number of users is 20, the number of general active users is 2, and the symbol error rate can be controlled to be 10 when the SNR is 10-310-4And the invention can provide better detection reliability and can adapt to 5G massive connection scenes.
Table 1 simulation parameter settings
The invention determines the iteration times by verifying whether the error reaches the minimum value, thereby determining the user sparsity. A scheduling-free strategy is adopted in a non-orthogonal multiple access system, a user can randomly and suddenly access the system, and although the scheduling-free strategy omits a complex scheduling process, judging whether the user is active or not is a difficult problem before detecting data for a receiver. The invention provides a method for jointly detecting user behaviors and data by combining a compressed sensing detector. The traditional OMP algorithm needs the sparsity of a known user to detect, but is not well realized in practice, and particularly under the condition of uplink scheduling-free, the OMP algorithm is slightly ineffective in the practical system application. Even if the existing sparsity adaptive matching and tracking algorithm is applied to a noisy MUSA system, the iteration stop threshold value cannot be determined to be an optimal value due to the fact that noise is unknown, and improvement is still needed in practical use. The invention determines the iteration times by verifying whether the error reaches the minimum value, can carry out the joint detection of the user activity and the data under the condition of unknown sparsity, can achieve the detection performance equivalent to that of an OMP algorithm, is more flexible than the OMP algorithm in the actual application, and is more suitable for the actual MUSA scheduling-free system.
It is to be understood that some of the features of the apparatus and method of the present invention may be mutually incorporated and will not be described in detail herein for the sake of brevity.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: ROM, RAM, magnetic or optical disks, and the like.
The above-mentioned embodiments, which further illustrate the objects, technical solutions and advantages of the present invention, should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
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