CN113098571B

CN113098571B - Signal detection method, system, base station and storage medium for large-scale MIMO system

Info

Publication number: CN113098571B
Application number: CN202110330579.8A
Authority: CN
Inventors: 许耀华; 袁伟伟; 王翊; 蒋芳; 刘瑜; 柏娜; 宛新文
Original assignee: Anhui University
Current assignee: Anhui University
Priority date: 2021-03-23
Filing date: 2021-03-23
Publication date: 2022-03-01
Anticipated expiration: 2041-03-23
Also published as: CN113098571A

Abstract

The invention provides a large-scale MIMO system signal detection method, a system, a base station and a storage medium, wherein the detection method comprises the steps of carrying out active tabu search detection on a first initial signal to obtain an initial estimation vector; eliminating an interference signal in the first initial signal according to the initial estimation vector to obtain a second initial signal; performing message passing detection on the second initial signal to obtain an output vector estimate; reconstructing the symbol vector according to the output vector estimation to obtain a symbol vector reconstruction value; and performing iterative operation by taking the symbol vector reconstruction value as the input of active tabu search, and outputting the final symbol vector reconstruction value after the iteration is finished as a detection result. The invention can improve the condition that the RTS algorithm has poor performance under high-order modulation and has lower complexity.

Description

Signal detection method, system, base station and storage medium for large-scale MIMO system

Technical Field

The present invention relates to the field of MIMO signal detection technologies, and in particular, to a method, a system, a base station, and a storage medium for detecting signals in a large-scale MIMO system.

Background

In recent years, mobile communication has changed greatly, and rapid popularization of intelligent terminal equipment, rapid addition of wireless data communication services, new internet of things technology services of emerging industries and the like all have higher requirements on data communication. To solve the problem of spectrum resource shortage, a large-scale Multiple Input Multiple Output (MIMO) technology is emerging. MIMO technology provides services to multiple users simultaneously by configuring hundreds of antennas at a base station. Compared with the traditional small-scale MIMO system, the large-scale MIMO system obviously improves the frequency spectrum efficiency and can meet the requirements of high data transmission rate, stable connection, low delay and the like. The massive MIMO technology with extremely high energy efficiency and spectrum utilization rate is widely considered as a key technology of the next generation communication network architecture.

While MIMO brings significant performance gain, the complexity of the detection signal algorithm increases exponentially due to the large-scale number of antennas and the high-dimensional channel matrix, which brings great challenges to the signal detection at the receiving end. The active tabu search algorithm is a heuristic algorithm, has the characteristics of low complexity and good performance, has great applicability in Massive MIMO signal detection, is a research hotspot of large-scale MIMO detection, and still has the problem of poor performance under high-order modulation.

Disclosure of Invention

In view of the above drawbacks of the prior art, an object of the present invention is to provide a method, a system, a base station and a storage medium for detecting signals of a massive MIMO system, which are used to solve the technical problem that the RTS algorithm in the signal detection of the massive MIMO system in the prior art is not good in performance in a high-order modulation system.

To achieve the above and other related objects, the present invention provides a signal detection method for a massive MIMO system, comprising:

performing active tabu search detection on the first initial signal to obtain an initial estimation vector;

eliminating an interference signal in the first initial signal according to the initial estimation vector to obtain a second initial signal;

performing message passing detection on the second initial signal to obtain an output vector estimate;

reconstructing the symbol vector according to the output vector estimation to obtain a symbol vector reconstruction value;

and performing iterative operation by taking the symbol vector reconstruction value as the input of active tabu search, and outputting the final symbol vector reconstruction value after the iteration is finished as a detection result.

In an optional embodiment, the step of canceling the interference signal in the first initial signal according to the initial estimation vector to obtain a second initial signal includes:

acquiring an output bit value according to the initial estimation vector;

acquiring total interference between signals according to the output bit value;

subtracting the total inter-signal interference from the first initial signal to obtain the second initial signal.

In an alternative embodiment, the output bit value is obtained according to

Wherein,

q is the number of points in the constellation diagram, K is the number of users in the uplink of the MIMO system,

for the initial estimated vector

The ith component of (a).

In an optional embodiment, in the step of obtaining the total interference between signals according to the output bit value, the total interference between signals

The expression of (a) is as follows:

wherein,

h is the channel gain matrix.

In an alternative embodiment, the output is based on the direction of the outputThe quantity estimation reconstructs the symbol vector to obtain a symbol vector reconstruction value

The expression of (a) is as follows:

wherein,

is the output vector estimate.

In an optional embodiment, in the step of performing an iteration operation using the symbol vector reconstruction value as an input of the active tabu search, the number of iterations is greater than or equal to three.

In an optional embodiment, in the step of performing an iteration operation using the symbol vector reconstruction value as an input of the proactive tabu search, the number of iterations is three.

To achieve the above and other related objects, the present invention further provides a signal detection system for massive MIMO system, comprising:

the active tabu search module is used for carrying out active tabu search detection on the first initial signal so as to obtain an initial estimation vector;

an interference signal eliminating module, configured to eliminate an interference signal in the first initial signal according to the initial estimation vector to obtain a second initial signal;

a vector estimation obtaining module, configured to perform message passing detection on the second initial signal to obtain an output vector estimation;

a symbol vector reconstruction module, configured to reconstruct a symbol vector according to the output vector estimation to obtain a symbol vector reconstruction value;

and the iteration output module is used for performing iteration operation by taking the symbol vector reconstruction value as the input of active tabu search, and outputting the final symbol vector reconstruction value after the iteration is finished as a detection result.

To achieve the above and other related objects, the present invention also provides a base station, comprising:

the base station body is arranged on the base body and comprises a plurality of receiving antennas and a control unit;

the receiving antenna is used for receiving a symbol vector sent by a user of the MIMO system and transmitting the symbol vector as a first initial signal to the control unit for signal detection; the control unit comprises a processor and a memory coupled to each other, the memory stores program instructions, and the program instructions stored in the memory when executed by the processor implement the massive MIMO system signal detection method according to any one of the above.

To achieve the above and other related objects, the present invention also provides a storage medium containing a program which, when run on a computer, causes the computer to execute the massive MIMO system signal detection method.

The invention discloses a large-scale MIMO system signal detection method, a large-scale MIMO system signal detection system, a base station and a storage medium, and provides an active tabu search and message transfer detection combined algorithm for large-scale MIMO signal detection under high-order modulation.

Drawings

FIG. 1 is a diagram of a massive MIMO system model according to the present invention.

Fig. 2 is a flowchart illustrating a signal detection method of a massive MIMO system according to an embodiment of the invention.

Fig. 3 is a block diagram of a massive MIMO system signal detection system according to an embodiment of the present invention.

Fig. 4 is a block diagram of a control unit according to an embodiment of the invention.

Fig. 5 shows a graph of RTS algorithm performance for different antenna numbers under 4-QAM modulation.

Fig. 6 shows a graph comparing the performance of the RTS algorithm under 16-QAM modulation with 64-QAM modulation.

Fig. 7 is a graph showing the comparison of detection performance of the RTS-MPD algorithm and the RTS algorithm according to the present invention.

Detailed Description

The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention.

Please refer to fig. 1-7. It should be noted that the drawings provided in the present embodiment are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than being drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of each component in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.

Fig. 1 shows a model diagram of a massive MIMO (multiple-input multiple-output) system according to the present invention. Consider a massive MIMO system with K users 1 in the uplink, each user having a transmit antenna 11 communicating with a base station 2 equipped with N receive antennas 21, N being between tens and hundreds of values. And alpha is K/N which is the loading factor of the system (alpha is less than or equal to 1). The system model is shown in fig. 1.

By using

Representing the channel gain matrix at the t-th channel,

denotes the complex channel gain from the jth user antenna (transmitting antenna 11) to the ith base station antenna (receiving antenna 21)

Subject to a mean of 0 and a variance of

The model causes imbalance in received power from user j due to path loss and the like,

corresponding to the full power control case. By using

Indicating that the modulation symbol vector is transmitted in the t-th channel, in which

Represents the modulation symbol transmitted by the jth user. Assuming perfect synchronization, the received vector at the t-th channel of the base station 2 is

Can be written as:

wherein

For noise vectors, to facilitate channel index removal, (1) the real number system model is written as:

y＝Hx+n (2)

wherein

And

respectively representing real and complex parts, and, at the same time,

for the

Wherein

Is the Pulse Amplitude Modulation (PAM) alphabet and represents the points of the homodromous or quadrature components in the complex constellation Θ. Due to the fact that

The constellation size of the transmission symbol is reduced from Q to

Wherein Q is the number of points in the constellation diagram. Now a complex nxk MIMO complex constellation Θ is equivalent to a real 2 nx2K MIMO real constellation

n is that the noise vectors obey independent homography

Average received signal-to-noise ratio of each receiving antenna 21

Wherein E_sRepresenting the average energy of the transmitted symbols.

The principles of the active Tabu Search (RTS) algorithm, the Message Passing Detection (MPD) algorithm, and the RTS-MPD combination algorithm of the present invention will be described below.

A. RTS detection algorithm

For a real number system model, a Maximum Likelihood (ML) detection model can be expressed as

When the prior probabilities of the transmitted bits are the same, the likelihood detection is equivalent to the maximum a posteriori detection, which can be expressed as:

where the complexity of the exact calculation of equations (3) and (4) is exponential to K.

From equation (3), the maximum likelihood model can be written as follows:

wherein,

f (x) is a cost function of the transmitted signal vector.

Suppose g^(m)The value of the cost function representing the optimal solution vector (ML) after m iterations is minimal, L_repRepresents the average length (i.e., average number of iterations) between two repeated solution vectors, N_repRepresenting the repeated times of the solution vector, P representing a tabu period, and lflag ∈ {0,1} being a local minimum mark for judging whether a local minimum point is reached in the current iteration.

The RTS algorithm starts with an initial solution vector, denoted x⁽⁰⁾It may be the output of a known detector ZF (Zero Forcing)/MMSE (Minimum Mean Squared Error) or it may be generated randomly. Let g⁽⁰⁾＝x⁽⁰⁾，L_rep＝0，N_rep＝0，p＝p₀(p₀Is an initial tabu period value, is a positive integer), andthe tabu matrix T is 0, defined

And calculate y_MFAnd the value of R. The steps required at each iteration are given below, taking into account the mth (m) of the RTS algorithm detection process>0) And (4) secondary iteration:

the first step is as follows: the initialization lflag is 0. Is provided with

e＝Z^(m)(u,v)-x^(m)Then x^(m)KM (M is the number of symbol neighbors) vector neighbors of Z^(m)(u, v), u ═ 1,2, …, K, v ═ 1,2, …, ML cost function value f for M (z, v)^(m)(u, v)) is calculated as follows:

wherein,

(u₁,v₁)＝arg min_u，vf(z^(m)(u,v))＝arg min_u,v(u,v) (7)

then, judge move (u)₁,v₁) The conditions that can occur are as follows:

f(z^(m)(u₁,v₁))<f(g^(m)) (8)

T((u₁-1)M+q,v₁)＝0 (9)

move (u) as long as any one of the above conditions is satisfied₁,v₁) It may happen that the current solution vector x is executed^(m)。

To the (u) th₁,v₁) Vector neighbor z^(m)(u₁,v₁) The transfer of (2). In the above formula (9) q is according to

And (4) calculating. If move (u)₁,v₁) If the corresponding vector neighbor does not satisfy the condition in equation (8) and the tabu matrix T does not satisfy the condition in equation (9), then use (u)₂,v₂) Making a condition judgment, wherein

If move (u)₂,v₂) If it cannot occur, the operation is continued (u)_k,v_k) K is 3,4, …, kM, until x^(m)Can be shifted to one vector neighbor direction. If all KM directions to be transferred are tabu, finding out the minimum value in the tabu matrix T, subtracting the minimum value from all elements in the tabu matrix T, and repeating the above steps to judge whether the transfer can be performed. Suppose that the vector to which (u ', v') corresponds is the neighbor z^(m)(u ', v') the cost function value is currently the smallest and a move (u ', v') can occur, then

x^(m+1)＝z^(m)(u′,v′) (11)

The second step is that: and (5) carrying out repeatability test on the solution vector obtained in the first step. N if the newly obtained solution vector and the solution vector obtained from the previous iteration are repeated_rep＝N_rep+1, while calculating and updating l_repThe value of (c). The value of the tabu period P is adjusted to P + 1. But if for a fixed beta the current P value exceeds beta l_repLet P be max (1, P-1), i.e.

If f (x)^(m+1))<f(g^(m)) Some of the tabu values of the tabu matrix and the optimal solution vector are updated according to (13) and (14):

T((u′-1)M+q′,v′)＝T((u′-1)M+q″,v″)＝0 (13)

lflag＝0,g^(m+1)＝x^(m+1) (14)

otherwise, updating is performed according to equations (15) and (16):

T((u′-1)M+q′,v′)＝T((u′-1)M+q″,v″)＝P+1 (15)

lflag＝1,g^(m+1)＝g^(m+1) (16)

the third step: updating the value of the tabu matrix T according to equation (17)

T＝max{T-1,0} (17)

While following f according to formula (18)^(m)A value of (A) is as follows

Wherein R is_u′Represents the u' th column of R. At the moment, judging whether a termination condition is met, if so, terminating the iteration process, and returning a detection result as a final solution vector; and if the termination condition is not met, jumping to the first step and continuing to execute the three steps.

B. MPD detection algorithm

The message delivery detection (MPD) algorithm is a low complexity message delivery method that exploits the phenomenon of channel hardening.

Multiplying H by y ═ Hx + n^TBy dividing by N, we can obtain

Z＝Jx+V (19)

Wherein Z is H^Ty/N,J＝H^TH/N,V＝H^TN/N. The i-th element of Z is

Wherein

And

g_iis assumed to be a obedient mean value mu_iAnd variance of

Gaussian distribution

Wherein

Calculation of E (g)_i) And Var (g)_i) Needs to calculate p_j(s_k) A posteriori probability of (i), i.e. x_jIs formed as s_kE.g., the probability of theta. Symbol x_jIs defined as a log-likelihood ratio of

Wherein

In addition, the method can be used for producing a composite material

Finally, the symbol x_jIs determined as s_kThe maximum probability of (c).

C. Basic principle of RTS-MPD joint algorithm

Defining x as a transmission vector,

Is the output of RTS detector,

Is composed of

The alphabet of modulations and the value of x comes from this set. Considering the mapping between symbols and bits, here we will

The value of each term of (a) is written as a linear combination of bits:

wherein

Q is the number of points in the constellation,

is composed of

The ith component of (a) is known from the RTS detection algorithm described above, and the solution vector of the output of the RTS detection algorithm is a local minimum. Therefore, the first and second electrodes are formed on the substrate,

the following inequalities are satisfied:

wherein

e_iRepresenting the ith column of the feature matrix. Definition h_iThe ith column in which H is represented,

Then the formula (25) can be represented as

In the above formula (26), f_ijIs the element of row i and column j of F, ignores noise under high SNR condition, and can be further developed into

Wherein f is_iLine i of F, in Rayleigh fading channel, F_iiObeying a chi-square distribution with a degree of freedom of 2K and a mean value of N. When i ≠ j, f is known from the central limit theorem_iiAnd a normal distribution with a mean value of 0 and a variance of K/4. Formula (27) may be changed to

In formula (28):

if it is

Then:

fig. 2 is a schematic flow chart of the large-scale MIMO system signal detection method according to the present invention, and the implementation flow of the large-scale MIMO system signal detection method according to the embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

Referring to fig. 2, the signal detection method of the massive MIMO system includes:

step S10, performing active tabu search detection on the first initial signal to obtain an initial estimation vector

Specifically, the single-antenna user 1 transmits a signal x through the transmitting antenna 11, the transmitted signal x is modulated and transmitted to the base station 2 through a channel to generate a received signal y as the first initial signal y, and the received signal y is subjected to RTS detection through the RTS detector to obtain an initial estimation vector

Step S20, according to the initial estimation vector

Eliminating interference signals in the first initial signal to obtain a second initial signal

Specifically, the bit value of the output can be obtained by equation (24)

i＝1，…,2K，

From bit value

The total inter-signal interference (i.e., total inter-signal interference) is derived:

wherein

H is a channel gain matrix; subtracting the total inter-signal interference from the received signal y

To obtain the second initial signal

That is to say

Step S30, for the second initial signal

Performing message passing detection to obtain output vector estimation

Specifically, the second initial signal acquired in step S20 is used

The output vector estimation can be obtained by inputting the output vector to an MPD detection module for MPD detection

Step S40, estimating according to the output vector

Reconstructing the symbol vector x to obtain a symbol vector reconstruction value

Wherein,

step S50, using the symbol vector reconstruction value as the input of active tabu search to carry out iterative operation untilWhen the iteration condition is met, the final symbol vector reconstruction value after the iteration is finished

And outputting the result as a detection result. Specifically, the symbol vector obtained in step S40 is reconstructed into values

The steps of performing steps S10-S40 are repeatedly performed as input to the RTS detector until the iteration ends, wherein the iteration ends based on the number of iterations reaching three or any integer value greater than three.

As shown in fig. 3, the embodiment of the present invention further discloses a large-scale MIMO system signal detection system 3, where the large-scale MIMO system signal detection system 3 includes an active tabu search module 31, an interference signal cancellation module 32, a vector estimation obtaining module 33, a symbol vector reconstruction module 34, and an iterative output module 35. The active tabu search module is used for carrying out active tabu search detection on the first initial signal so as to obtain an initial estimation vector; the interference signal eliminating module 31 is configured to eliminate an interference signal in the first initial signal according to the initial estimation vector to obtain a second initial signal; the vector estimation obtaining module 32 is configured to perform message passing detection on the second initial signal to obtain an output vector estimation; the symbol vector reconstructing module 33 is configured to reconstruct a symbol vector according to the output vector estimation to obtain a symbol vector reconstruction value; the iteration output module 34 is configured to perform iteration operation by using the symbol vector reconstruction value as an input of active tabu search, and output a final symbol vector reconstruction value after iteration as a detection result.

It should be noted that the massive MIMO system signal detection system 3 of the present invention is a system corresponding to the massive MIMO system signal detection method described above, and the functional modules in the massive MIMO system signal detection system 3 correspond to the corresponding steps in the massive MIMO system signal detection method, respectively. The large-scale MIMO system signal detection system 3 of the invention can be mutually matched with the large-scale MIMO system signal detection method for implementation. The relevant technical details mentioned in the large-scale MIMO system signal detection method of the present invention are still valid in the large-scale MIMO system signal detection system 3, and are not described herein again in order to reduce repetition. Accordingly, the related technical details mentioned in the massive MIMO system signal detection system 3 of the present invention can also be applied to the massive MIMO system signal detection method described above.

It should be noted that, in the actual implementation, all or part of the functional modules may be integrated into one physical entity, or may be physically separated. And these units can be implemented entirely in software, invoked by a processing element; or may be implemented entirely in hardware; and part of the units can be realized in the form of calling software by the processing element, and part of the units can be realized in the form of hardware. In addition, all or part of the units can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, the steps of the above method or the above modules may be implemented by hardware integrated logic circuits in the processor 41 elements or instructions in the form of software.

As shown in fig. 4, the MIMO system signal detection method of the present invention may also be implemented by a control unit 4 disposed on a base station (or disposed on another main body), where the control unit 4 includes a memory 43 and a processor 41 connected to each other, and the memory 43 stores program instructions, and the program instructions are executed by the processor 41 to implement the MIMO system signal detection method. It should be noted that, when communication with the outside is required, the control unit 4 further includes a communicator 42, and the communicator 42 is connected to the processor 41.

The processor 41 may be a general-purpose processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; or a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component; the Memory 43 may include a Random Access Memory (RAM), and may further include a Non-volatile Memory (Non-volatile Memory), such as at least one disk Memory.

It should be noted that the control unit 4 in the memory 43 can be implemented in the form of a software functional unit and can be stored in a computer readable storage medium when being sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention.

The present invention may also provide a storage medium storing a program that, when executed by the processor 41, implements the massive MIMO system signal detection method described above; the storage medium includes all forms of non-volatile memory, media and memory devices, including, for example: semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

In order to verify the large-scale MIMO system signal detection method of the present invention, the inventors conducted simulation experiments. The present example shows Matlab monte carlo simulation results. Simulation parameters: selecting QAM as a modulation mode; the antenna sizes are 16 × 16, 32 × 32, and 64 × 64. The transmission channel is a Rayleigh fading channel, the channel characteristic is time-varying, and the noise is additive white Gaussian noise. This section mainly studies the comparison of the bit error rate of the RTS and RTS-MPD algorithms under different modulation orders and different antenna configurations.

Fig. 5 plots the performance of the RTS detection algorithm for antenna configurations of 16 × 16, 32 × 32, and 64 × 64, respectively, for a modulation scheme of 4 QAM. SISO AWGN performance curves were also added for comparison. It can be seen from the figure that RTS has good performance under low-order modulation, and as the number of antennas increases, the performance improvement is obvious, approaching SISO AWGN. Therefore, the RTS is suitable for signal detection in a massive MIMO system with a low modulation order and a large number of antennas.

FIG. 6 is a graph showing the performance of the RTS algorithm under high-order Modulation, and it can be seen from FIG. 5 that the error rate of the RTS algorithm can reach 10 when the signal-to-noise ratio is about 11db under the condition of 4-QAM (Quadrature Amplitude Modulation)^-3However, as can be seen from fig. 6, under 16-QAM high-order modulation, the RTS algorithm has poor performance, and can reach 10 when the signal-to-noise ratio is as high as 30^-3(ii) a In fig. 6, it can also be seen that when the modulation order becomes higher, the detection performance of the RTS algorithm becomes further significantly worse, and the performance of the RTS algorithm under the high-order modulation is very poor.

In the simulation test, test parameters are shown below in order to simply and visually verify the detection performance of the RTS-MPD algorithm, and an experimental simulation comparison diagram of the RTS algorithm and the RTS-MPD algorithm under the conditions of 16QAM and 64QAM modulation is provided, as shown in FIG. 7. As can be seen from the figure, in the case of 16QAM modulation, when BER is 10^-3In time, the RTS-MPD combined algorithm is superior to the RTS algorithm performance, and the performance of 1db is improved approximately; in the case of 64QAM modulation, when BER is 10^-3And meanwhile, the RTS-MPD combined algorithm is superior to RTS algorithm performance, and performance improvement is more obvious and reaches about 2 db.

In summary, the present invention provides a combined algorithm of active tabu search and message transmission detection for large-scale MIMO signal detection under high-order modulation, which combines the MPD algorithm into the RTS algorithm, corrects the error of the last bit of the symbol by using the MPD algorithm, so as to improve the poor performance of the RTS algorithm under high-order modulation and achieve lower complexity.

In the description herein, numerous specific details are provided, such as examples of components and/or methods, to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that an embodiment of the invention can be practiced without one or more of the specific details, or with other apparatus, systems, assemblies, methods, components, materials, parts, and/or the like. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of embodiments of the invention.

Reference throughout this specification to "one embodiment", "an embodiment", or "a specific embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment, and not necessarily all embodiments, of the present invention. Thus, respective appearances of the phrases "in one embodiment", "in an embodiment", or "in a specific embodiment" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics of any specific embodiment of the present invention may be combined in any suitable manner with one or more other embodiments. It is to be understood that other variations and modifications of the embodiments of the invention described and illustrated herein are possible in light of the teachings herein and are to be considered as part of the spirit and scope of the present invention.

It will also be appreciated that one or more of the elements shown in the figures can also be implemented in a more separated or integrated manner, or even removed for inoperability in some circumstances or provided for usefulness in accordance with a particular application.

Additionally, any reference arrows in the drawings/figures should be considered only as exemplary, and not limiting, unless otherwise expressly specified. Further, as used herein, the term "or" is generally intended to mean "and/or" unless otherwise indicated. Combinations of components or steps will also be considered as being noted where terminology is foreseen as rendering the ability to separate or combine is unclear.

As used in the description herein and throughout the claims that follow, "a", "an", and "the" include plural references unless otherwise indicated. Also, as used in the description herein and throughout the claims that follow, unless otherwise indicated, the meaning of "in …" includes "in …" and "on … (on)".

The above description of illustrated embodiments of the invention, including what is described in the abstract of the specification, is not intended to be exhaustive or to limit the invention to the precise forms disclosed herein. While specific embodiments of, and examples for, the invention are described herein for illustrative purposes only, various equivalent modifications are possible within the spirit and scope of the present invention, as those skilled in the relevant art will recognize and appreciate. As indicated, these modifications may be made to the present invention in light of the foregoing description of illustrated embodiments of the present invention and are to be included within the spirit and scope of the present invention.

The systems and methods have been described herein in general terms as the details aid in understanding the invention. Furthermore, various specific details have been given to provide a general understanding of the embodiments of the invention. One skilled in the relevant art will recognize, however, that an embodiment of the invention can be practiced without one or more of the specific details, or with other apparatus, systems, assemblies, methods, components, materials, parts, and/or the like. In other instances, well-known structures, materials, and/or operations are not specifically shown or described in detail to avoid obscuring aspects of embodiments of the invention.

Thus, although the present invention has been described herein with reference to particular embodiments thereof, a latitude of modification, various changes and substitutions are intended in the foregoing disclosures, and it will be appreciated that in some instances some features of the invention will be employed without a corresponding use of other features without departing from the scope and spirit of the invention as set forth. Thus, many modifications may be made to adapt a particular situation or material to the essential scope and spirit of the present invention. It is intended that the invention not be limited to the particular terms used in following claims and/or to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include any and all embodiments and equivalents falling within the scope of the appended claims. Accordingly, the scope of the invention is to be determined solely by the appended claims.

Claims

1. A signal detection method for a massive MIMO system is characterized by comprising the following steps:

acquiring an output bit value according to the initial estimation vector;

acquiring total interference between signals according to the output bit value;

subtracting the total interference between the signals from the first initial signal to obtain a second initial signal;

performing iterative operation by taking the symbol vector reconstruction value as the input of active tabu search, and outputting the final symbol vector reconstruction value after the iteration is finished as a detection result;

wherein the output bit value is obtained according to the following formula

Wherein,

for the initial estimated vector

The ith component of (a);

in the step of obtaining the total interference between the signals according to the output bit value, the total interference between the signals

The expression of (a) is as follows:

wherein,

h is the channel gain matrix.

2. The massive MIMO system signal detection method of claim 1, wherein the step of reconstructing the symbol vector based on the output vector estimate to obtain a symbol vector reconstruction value comprises the step of reconstructing the symbol vector reconstruction value

The expression of (a) is as follows:

wherein,

is the output vector estimate.

3. The massive MIMO system signal detection method according to claim 1 or 2, wherein in the step of performing an iteration operation using the symbol vector reconstructed value as an input of an active tabu search, the number of iterations is three or more.

4. A massive MIMO system signal detection system, comprising:

an interference signal elimination module, configured to obtain an output bit value according to the initial estimation vector, obtain total interference between signals according to the output bit value, and subtract the total interference between signals from the first initial signal to obtain a second initial signal, where the output bit value is obtained according to the following equation

Wherein,

for the initial estimated vector

The ith component of (a);

The expression of (a) is as follows:

wherein,

h is a channel gain matrix;

5. A base station, characterized in that the base station comprises:

the base station comprises a base station body, a plurality of receiving antennas and a control unit, wherein the plurality of receiving antennas and the control unit are arranged on the base station body;

the receiving antenna is used for receiving a symbol vector sent by a user of the MIMO system and transmitting the symbol vector as a first initial signal to the control unit for signal detection; the control unit comprises a processor and a memory coupled to each other, the memory storing a computer program, the computer program stored in the memory when executed by the processor implementing the massive MIMO system signal detection method of any one of claims 1 to 3.

6. A storage medium characterized by comprising a program which, when run on a computer, causes the computer to execute the massive MIMO system signal detection method according to any one of claims 1 to 3.