No-cell symbiotic radio system optimization method based on limited-length block coding

Abstract

The invention relates to the technical field of symbiotic radio, in particular to a cell-free symbiotic radio system optimization method based on limited long block coding, which comprises the following steps: constructing a cell-free SR system which is composed of a receiver, a plurality of APs and a BD; the APs cooperatively transmit a main signal to the receiver, and the BD simultaneously reflects the main signal from the APs to realize transmission of own information; the problem of the actual reachable rate of the secondary transmission based on limited long block coding and the maximum primary transmission rate under the condition of meeting the secondary transmission service quality constraint condition is solved; a simulation result verifies that the provided beam forming design scheme can effectively improve the main transmission rate, and the main transmission rate is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of symbiotic radio technology, and in particular to a method for optimizing a non-cell symbiotic radio system based on finite-length block coding. Background Art

[0002] With the rapid development of communication technology and the full deployment of 5G technology, 5G will promote the development of the industrial Internet and drive the manufacturing industry towards a new era of intelligent manufacturing. Cell-free technology and symbiotic radio have great research value as key network architectures and information transmission technologies in the future 6G era. They have received great attention in intelligent manufacturing and industrial automation, Internet of Vehicles, wireless communications in high-density environments, virtual reality, and so on.

[0003] Most current studies use the traditional Shannon capacity to evaluate cell-free assisted SR systems, which is based on the assumption that the data transmission packet is nearly infinite in length. However, as a low-power IoT device, the backscatter device can only use limited power to modulate its own signal. Therefore, it is not practical to regard BD as an infinite-length block coded device.

[0004] In recent years, finite-length block coding technology has become one of the hot topics in wireless communication research. However, there are still some challenges. For example, when cell-free is combined with symbiotic radio, the previous method of calculating the sub-transmission rate does not fully consider the actual situation. Therefore, finite-length block coding is introduced to calculate the actual rate of sub-transmission. Summary of the invention

[0005] The object of the present invention is to provide a method for optimizing a non-cell coexistence radio system based on finite length block coding to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A method for optimizing a non-cell coexistence radio system based on finite length block coding, the method comprising:

[0008] S100, constructing a cell-free SR system; the cell-free SR system is composed of M APs equipped with N antennas, a single-antenna BD and a single-antenna receiver; wherein the M APs send signals to the receiver in a coordinated manner to achieve efficient primary information transmission, and after receiving the signal from the APs, the BD transmits a secondary signal containing its own information to the receiver by modulating and reflecting the signal to achieve secondary transmission;

[0009] The above-mentioned APs generally refer to wireless access points, which are access points of a wireless network, commonly known as "hotspots"; BD stands for backscatter devices, which are low-power IoT devices that use limited power to modulate their own signals;

[0010] S200, based on the cell-free SR system, analyze the actual achievable rate of the secondary transmission based on finite length block coding and the maximum primary transmission rate under the condition of satisfying the secondary transmission quality of service constraint;

[0011] S300. Use simulation results to evaluate the performance of the cell-free SR system with finite-length block coding.

[0012] Preferably, S100 includes:

[0013] S101. Set the mth AP to be AP m , where m∈{1,2,…,m}; get AP respectively m To BD and from AP m Signal to the receiver and and the channel coefficient q from BD to the receiver;

[0014] Based on this, it is confirmed that during the nth main symbol period, AP m The transmitted signal and the received signal of the receiver:

[0015] Considering the PSR transmission format of the SR system, which has the same primary and secondary symbol periods, in the nth primary symbol period, AP m The transmitted signal is: x m (n) = w m s(n);

[0016] Where s(n) represents the main signal, which satisfies the complex Gaussian distribution with mean 0 and variance 1; w m Indicates AP m The transmit beamforming vector satisfies ||w m ||≤P m , P m Indicates AP m The maximum transmit power allowed in the location;

[0017] Define the reflection coefficient of BD as α, α∈[0,1], then the received signal of the receiver is:

[0018]

[0019] Where c(n) represents the transmission symbol of BD, z(n) represents additive Gaussian white noise;

[0020] In order to facilitate calculation, the received signal of the receiver is transformed into

[0021]

[0022] Among them, g H ws(n) represents the direct link signal from M APs to the receiver, Represents the reflected link signal passing through BD;

[0023] S102: Receiver receiving signal Perform signal decoding to confirm the transmission rate of the reflection link signal;

[0024] S103: Analyze the process of solving the reflection link signal transmission rate.

[0025] Preferably, S102 includes:

[0026] A1. Get the received signal y(n) of the receiver. The receiver first decodes s(n) and regards the signal c(n) from the reflection link as interference. Then the signal-to-interference-to-noise ratio of the decoded s(n) is: σ represents noise;

[0027] Due to interference It contains two complex Gaussian signals s(n) and c(n) and their multiplication operation, so the interference term presents the characteristics of non-Gaussian distribution; based on this feature, considering the worst case of decoding s(n), the corresponding lower limit of the achievable rate can be obtained: R s =log2(1+γ s );

[0028] A2. After decoding the main transmission signal s(n), the first term in the received signal y(n) is removed using the successive interference cancellation technique to obtain the intermediate signal:

[0029] A3. When decoding c(n), the fast-changing channel of c(n) can be expressed as Then the achievable rate of decoding c(n) is:

[0030] Among them, ò represents the error probability, and 0≤ò≤1, In β, Y represents the code block length of the finite length block code, Q -1 (x) is the inverse of the Gaussian function Q(x),

[0031] The main signal s(n) obeys a circularly symmetric complex Gaussian distribution, and the square of the envelope of the main signal s(n) obeys an exponential distribution, and its probability density function is further expressed as f(x) = e -x ,x>0, where x=|s(n)|2 .

[0032] Preferably, S103 includes:

[0033] B1. Get the achievable rate R of decoding c(n) c , split it into two parts, represented by C and D respectively:

[0034] in, x=|s(n)| 2 ;

[0035] B2, based on the secondary transmission achievable rate R c Part C of the formula:

[0036] It is known that the square of the envelope of s(n) follows an exponential distribution, so the closed-form expression of part C is:

[0037] in,

[0038] B3, based on the secondary transmission achievable rate R c Part D of the formula:

[0039] Since the The integral of is difficult to solve, so consider approximate processing. When the integral encounters a<1, then but

[0040] At this time, the secondary transmission rate R is obtained c for

[0041] Preferably, S200 includes:

[0042] Define the optimization problem: Under a given secondary transmission rate QoS constraint, maximize the primary transmission rate by optimizing the beamforming vector at each AP;

[0043] Based on this, we get

[0044] Among them, constraint C1 represents the QoS constraint of the secondary transmission, and C2 represents AP m Transmit power constraints;

[0045] R th represents the secondary transmission rate threshold, w m Indicates AP m The transmit beamforming vector, P m Indicates AP m The maximum transmit power allowed in the location.

[0046] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned method for optimizing a non-cell coexistence radio system based on finite-length block coding.

[0047] A computer device comprises a memory, a processor and a computer program stored in the memory and running on the processor. When the processor executes the program, the steps in the above-mentioned method for optimizing a non-cell coexistence radio system based on finite-length block coding are implemented.

[0048] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0049] The present invention considers a cell-free SR system based on finite-length block coding, aiming to solve the performance optimization problem when combining cell-free and symbiotic radio. Specifically, the main transmission rate of the system can reach the maximum value through beam design; by studying the actual achievable rate of secondary transmission based on finite-length block coding and the maximum main transmission rate under the condition of satisfying the secondary transmission quality of service constraint, it is verified that the proposed beamforming design scheme can effectively improve the main transmission rate; specifically, the longer the block coding length, the more ideal the secondary transmission performance can be obtained; in addition, the proposed beamforming scheme is significantly better than the random beamforming scheme and the channel merging beamforming scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 It is a system schematic diagram of a method for optimizing a non-cell symbiotic radio system based on finite-length block coding according to the present invention;

[0052] Figure 2 is the influence diagram of different power constraints of the present invention;

[0053] Figure 3 It is the effect diagram of the finite block length on the experiment of the present invention;

[0054] Figure 4 This is a performance comparison diagram of the finite-length block coding and the infinite-length block coding of the present invention;

[0055] Figure 5 It is a comparison diagram of the beamforming solution proposed in the present invention and two reference solutions. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figure 1-Figure 5 , the present invention provides a technical solution:

[0058] Embodiment 1:

[0059] A method for optimizing a non-cell coexistence radio system based on finite length block coding, the method comprising:

[0060] S100, constructing a cell-free SR system; the cell-free SR system is composed of M APs equipped with N antennas, a single-antenna BD and a single-antenna receiver; wherein the M APs send signals to the receiver in a coordinated manner to achieve efficient primary information transmission, and after receiving the signal from the APs, the BD transmits a secondary signal containing its own information to the receiver by modulating and reflecting the signal to achieve secondary transmission;

[0061] The above-mentioned APs generally refer to wireless access points, which are access points of a wireless network, commonly known as "hotspots"; BD stands for backscatter devices, which are low-power IoT devices that use limited power to modulate their own signals;

[0062] Preferably, S100 includes:

[0063] S101. Set the mth AP to be AP m , where m∈{1,2,…,m}; get AP respectively m To BD and from AP m Signal to the receiver and and the channel coefficient q from BD to the receiver;

[0064] Based on this, it is confirmed that during the nth main symbol period, AP m The transmitted signal and the received signal of the receiver:

[0065] Considering the PSR transmission format of the SR system, which has the same primary and secondary symbol periods, in the nth primary symbol period, AP m The transmitted signal is: x m (n) = w ms (n);

[0066] Where s(n) represents the main signal, which satisfies the complex Gaussian distribution with mean 0 and variance 1; w m Indicates AP m The transmit beamforming vector satisfies ||w m ||≤P m , P m Indicates AP m The maximum transmit power allowed in the location;

[0067] Define the reflection coefficient of BD as α, α∈[0,1], then the received signal of the receiver is:

[0068]

[0069] Where c(n) represents the transmission symbol of BD, z(n) represents additive Gaussian white noise;

[0070] In order to facilitate calculation, the received signal of the receiver is transformed into

[0071]

[0072] Among them, g H ws(n) represents the direct link signal from M APs to the receiver, Represents the reflected link signal passing through BD;

[0073] S102: Receiver receiving signal Perform signal decoding to confirm the transmission rate of the reflection link signal;

[0074] Preferably, S102 includes:

[0075] A1. Get the received signal y(n) of the receiver. The receiver first decodes s(n) and regards the signal c(n) from the reflection link as interference. Then the signal-to-interference-to-noise ratio of the decoded s(n) is: σ represents noise;

[0076] Due to interference It contains two complex Gaussian signals s(n) and c(n) and their multiplication operation, so the interference term presents the characteristics of non-Gaussian distribution; based on this feature, considering the worst case of decoding s(n), the corresponding lower limit of the achievable rate can be obtained: R s =log2(1+γ s );

[0077] A2. After decoding the main transmission signal s(n), the first term in the received signal y(n) is removed using the successive interference cancellation technique to obtain the intermediate signal:

[0078] A3. When decoding c(n), the fast-changing channel of c(n) can be expressed as Then the achievable rate of decoding c(n) is:

[0079] Among them, ò represents the error probability, and 0≤ò≤1, In β, Y represents the code block length of the finite length block code, Q -1 (x) is the inverse of the Gaussian function Q(x),

[0080] The main signal s(n) obeys a circularly symmetric complex Gaussian distribution, and the square of the envelope of the main signal s(n) obeys an exponential distribution, and its probability density function is further expressed as f(x) = e -x ,x>0, where x=|s(n)| 2 .

[0081] S103, analyzing the process of solving the reflection link signal transmission rate;

[0082] Preferably, S103 includes:

[0083] B1. Get the achievable rate R of decoding c(n) c , split it into two parts, represented by C and D respectively:

[0084] in, x=s(n) 2 ;

[0085] B2, based on the secondary transmission achievable rate R c Part C of the formula:

[0086] It is known that the square of the envelope of s(n) follows an exponential distribution, so the closed-form expression of part C is:

[0087] in,

[0088] B3, based on the secondary transmission achievable rate R c Part D of the formula:

[0089] Since the The integral of is difficult to solve, so consider approximate processing. When the integral encounters a known shape when |a|<1, then but

[0090] At this time, the secondary transmission rate R is obtained c for

[0091] S200, based on the cell-free SR system, analyze the actual achievable rate of the secondary transmission based on finite length block coding and the maximum primary transmission rate under the condition of satisfying the secondary transmission quality of service constraint;

[0092] Preferably, S200 includes:

[0093] Define the optimization problem: Under a given secondary transmission rate QoS constraint, maximize the primary transmission rate by optimizing the beamforming vector at each AP;

[0094] Based on this, we get

[0095] Among them, constraint C1 represents the QoS constraint of the secondary transmission, and C2 represents AP m Transmit power constraints;

[0096] R th represents the secondary transmission rate threshold, w m Indicates AP m The transmit beamforming vector, P m Indicates AP m The maximum transmit power allowed in the location.

[0097] S300, using simulation results to evaluate the performance of a cell-free SR system with finite-length block coding;

[0098] Preferably, S300 includes:

[0099] The performance of the cell-free SR system based on finite-length block coding is evaluated through simulation results.

[0100] The following Cartesian coordinate system is established, where the BD is located at (0m, 0m), the receiver is located at (5m, 0m), and M APs are randomly distributed in a circle with a radius of 100m and a center at (0m, 0m). It is assumed that all channel attenuation consists of large-scale attenuation and small-scale attenuation. The small-scale channel coefficient is the same circular complex Gaussian random variable with a mean of zero and a unit variance of 1. The large-scale path loss depends on the distance between any two nodes and can be expressed as PL = d m - θ , d m Indicates the distance between each node. AP m The distance to BD is AP m The distance to the receiver is The distance from BD to the receiver is d REC2BD Then, set AP m The path loss exponent to BD is set to Set up AP mPath loss exponent to receiver Assume that the path loss exponent from BD to the receiver is θ REC2BD =2.0. In addition, the noise power is set to -120db, and the power constraint P m Set 0.1W, code block length Y to 200, and block error probability ò to 10 -3 , each AP has 4 antennas, the number of APs is set to 4, and the secondary transmission rate threshold R th 1bps / Hz;

[0101] Two comparison schemes are proposed:

[0102] (1) Random beamforming scheme: randomly generate a given beamforming vector;

[0103] (2) Channel merging beamforming scheme: Based on the current channel state information, the beamforming vector is selected with the goal of maximizing the received signal power. The details are as follows:

[0104] Figure 2 Depicts the different transmit power constraints P m Under this condition, the reachable rate region of the primary transmission rate and the secondary transmission rate and their relationship. By adjusting the transmission power constraint P m As a control variable, experiments were conducted using 0.05 times, 0.1 times, and 0.15 times the transmission power. Figure 2 It can be observed that the maximum value of the primary transmission rate remains basically stable under different transmit power constraints, indicating that it is less sensitive to power changes. However, as the transmit power constraint increases, the secondary transmission rate gradually increases, and this change is consistent with the coupling characteristics of the secondary transmission rate. Specifically, when the transmit power decreases, the feasible solution space of the transmit beamforming vector will be restricted, resulting in a narrowing of the SINR value range of the secondary transmission rate. Since the secondary transmission rate and SINR are monotonically related, the rate region of the secondary transmission rate also decreases accordingly. On the contrary, when the transmit power increases, the SINR value space of the secondary transmission rate becomes larger, thereby increasing its rate region accordingly. In summary, the transmit power constraint significantly affects the rate region of the secondary transmission rate.

[0105] Figure 3 The achievable rate regions and their corresponding relationships of the primary transmission rate and the secondary transmission rate under different block length conditions are depicted. Figure 3 It can be observed that when the primary transmission rate remains constant, the secondary transmission rate also shows an increasing trend as the block length increases. The explanation for this phenomenon is as follows: at the same primary transmission rate, the transmit beamforming vector is fixed, which means that the signal to noise ratio of the secondary transmission rate is constant. According to the secondary transmission rate R cFrom the formula, we can see that the secondary transmission rate is a decreasing function of β. In addition, as the block length increases, the value of parameter β actually decreases, so the secondary transmission rate increases accordingly. In summary, while keeping the primary transmission rate unchanged, increasing the block length will increase the secondary transmission rate.

[0106] Figure 4 The effects of finite-length block coding and infinite-length block coding on the main transmission rate under different transmission power conditions are compared. Figure 4 It can be clearly seen that under the same transmission power, the primary transmission rate achieved by infinite length block coding is significantly higher than that of finite length block coding. The following is an explanation for this phenomenon: When the block length is infinite, β will approach 0. According to the secondary transmission rate R c The formula is divided into two parts, C and D, and the secondary transmission rate increases. Therefore, constraint C1 is easier to meet, so that more transmission energy is used to transmit the main information, which increases the rate of the main transmission. When the power is 0.6W, the difference in the main transmission rate of the two coding schemes is 1.2bps / Hz.

[0107] Figure 5 The figure shows the comparison of the impact of the proposed beamforming scheme, random beamforming scheme and channel merging beamforming scheme on the main transmission rate under different transmit powers. Figure 5 It can be found that as the transmit power increases, the main transmission rates of the three schemes will increase accordingly. This is because increasing the transmit power can effectively increase the signal power at the receiver, thereby increasing the main transmission rate. At the same time, the proposed beamforming scheme is significantly better than the random beamforming scheme. Specifically, when the transmit power is 0.6W, compared with the random beamforming scheme, the proposed beamforming scheme can increase the main transmission rate by about 3.37bps / Hz, and compared with the channel merging beamforming scheme, the proposed beamforming can increase it by 1.84bps / Hz.

[0108] The present invention proposes a cell-free SR system with finite-length block coding, which has multiple distributed APs sending information to the receiver simultaneously and supports the secondary information transmission of BD. In order to be more practical, the signal of BD is designed to be finite-length block coded. Firstly, the feasibility of the defined optimization problem is analyzed; then, in order to solve the non-convexity of the problem, an iterative algorithm based on SCA is designed to solve it. The simulation results show that the finite-length block coding scheme with a larger block length can obtain a more accurate secondary transmission rate; compared with the random beamforming scheme and the channel ratio beamforming scheme, the proposed beamforming scheme can significantly improve the main transmission rate of the system.

[0109] Embodiment 2:

[0110] The computer-readable storage medium of this embodiment stores a computer program thereon, and when the program is executed by a processor, the steps in the method for optimizing a non-cell coexistence radio system based on finite-length block coding of embodiment 1 are implemented.

[0111] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0112] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0113] Embodiment 3:

[0114] The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for optimizing a non-cell coexistence radio system based on finite-length block coding of Embodiment 1 are implemented.

[0115] In this embodiment, the processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, readily available programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0116] Those skilled in the art will appreciate that the disclosed content of the embodiments may be provided as methods, systems, or computer program products. Therefore, the present solution may adopt the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Moreover, the present solution may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0117] The present solution is described with reference to the method according to the embodiment of the present solution and the flowchart and / or block diagram of the computer program product. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions; these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 one or more processes and / or methods Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 one or more processes and / or methods Figure 1 A function specified in one or more boxes.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 one or more processes and / or methods Figure 1 The steps for the functions specified in one or more boxes.

[0120] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0121] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing a non-cell coexistence radio system based on finite length block coding, characterized in that: The method comprises: S100, constructing a cell-free SR system; the cell-free SR system is composed of M APs equipped with N antennas, a single-antenna BD and a single-antenna receiver; wherein the M APs send signals to the receiver in a coordinated manner to achieve efficient primary information transmission, and after receiving the signal from the APs, the BD transmits a secondary signal containing its own information to the receiver by modulating and reflecting the signal to achieve secondary transmission; S200, based on the cell-free SR system, analyze the actual achievable rate of the secondary transmission based on finite length block coding and the maximum primary transmission rate under the condition of satisfying the secondary transmission quality of service constraint; S300. Use simulation results to evaluate the performance of the cell-free SR system with finite-length block coding.

2. The method for optimizing a non-cell coexistence radio system based on finite length block coding according to claim 1, characterized in that: The S100 includes: S101. Set the mth AP to be AP m , where m∈{1, 2, …, m}; get AP respectively m To BD and from AP m Signal to the receiver and and the channel coefficient q from BD to the receiver; Based on this, it is confirmed that during the nth main symbol period, AP m The transmitted signal and the received signal of the receiver: Considering the PSR transmission format of the SR system, which has the same primary and secondary symbol periods, in the nth primary symbol period, AP m The transmitted signal is: x m (n) = W mm s(n); Where s(n) represents the main signal, which satisfies the complex Gaussian distribution with mean 0 and variance 1; w m Indicates AP m The transmit beamforming vector satisfies ||w m ||≤P m , P m Indicates AP m The maximum transmit power allowed in the location; Define the reflection coefficient of BD as α, α∈[0,1], then the received signal of the receiver is: Where c(n) represents the transmission symbol of BD, z(n) represents additive Gaussian white noise; In order to facilitate calculation, the received signal of the receiver is transformed into Among them, g H ws(n) represents the direct link signal from M APs to the receiver, Represents the reflected link signal passing through BD; S102: Receiver receiving signal Perform signal decoding to confirm the transmission rate of the reflection link signal; S103: Analyze the process of solving the reflection link signal transmission rate.

3. The method for optimizing a non-cell coexistence radio system based on finite length block coding according to claim 2, characterized in that: The S102 includes: A1. Get the received signal y(n) of the receiver. The receiver first decodes s(n) and regards the signal c(n) from the reflection link as interference. Then the signal-to-interference-to-noise ratio of the decoded s(n) is: σ represents noise; Due to interference It contains two complex Gaussian signals s(n) and c(n) and their multiplication operation, so the interference term presents the characteristics of non-Gaussian distribution; based on this feature, considering the worst case of decoding s(n), the corresponding lower limit of the achievable rate can be obtained: R s =log2(1+γ s ); A2. After decoding the main transmission signal s(n), the first term in the received signal y(n) is removed using the successive interference cancellation technique to obtain the intermediate signal: A3. When decoding c(n), the fast-changing channel of c(n) can be expressed as The achievable rate of decoding c(n) is: in, represents the error probability, and 0≤ò≤1, In β, Y represents the code block length of the finite length block code, Q -1 (x) is the inverse of the Gaussian function Q(x), 4. The method for optimizing a non-cell coexistence radio system based on finite length block coding according to claim 2, characterized in that: The S103 includes: B1. Get the achievable rate R of decoding c(n) c , split it into two parts, represented by C and D respectively: in, x=|s(n)| 2 ; B2, based on the secondary transmission achievable rate R c Part C of the formula: The closed form expression of part C is: in, B3, based on the secondary transmission achievable rate R c Part D of the formula: Since the The integral of is difficult to solve, so consider approximate processing. When the integral encounters a known shape when |a|<1, then but At this time, the secondary transmission rate R is obtained c for 5. The method for optimizing a non-cell coexistence radio system based on finite length block coding according to claim 1, characterized in that: The S200 includes: Define the optimization problem: Under a given secondary transmission rate QoS constraint, maximize the primary transmission rate by optimizing the beamforming vector at each AP; Based on this, we get Among them, constraint C1 represents the QoS constraint of the secondary transmission, and C2 represents AP m Transmit power constraints; R th represents the secondary transmission rate threshold, w m Indicates AP m The transmit beamforming vector, P m Indicates AP m The maximum transmit power allowed in the location.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for optimizing a non-cell coexistence radio system based on finite-length block coding according to any one of claims 1 to 5 are implemented.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps in the method for optimizing a non-cell coexistence radio system based on finite-length block coding according to any one of claims 1 to 5 are implemented.

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