Transmission method of joint anti-scattering communication and active communication under symbiotic network

Abstract

The application provides a transmission method of joint anti-scattering communication and active communication under a symbiotic network, and relates to the technical field of wireless communication. A symbiotic radio transmission system exists in the symbiotic network, wherein the symbiotic radio transmission system comprises a base station, a primary user, a plurality of secondary user pairs with anti-scattering communication capability, and a RIS arranged between the base station and the secondary user pairs, and the secondary user pair with anti-scattering communication capability comprises an anti-scattering transmitter and an anti-scattering receiver. In the first stage of the transmission method, a specific power base station does not need to send a radio frequency signal, but the secondary user can use the signal sent by the symbiotic network base station to the original user to perform anti-scattering communication and energy collection. In the case of meeting the normal communication requirements of the system, the transmission power consumption of the base station can be greatly reduced, and the spectrum utilization rate is improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and specifically to a transmission method for joint backscatter communication and active communication in a symbiotic network. Background Technology

[0002] With the rapid growth in the number of Internet of Things (IoT) devices, the problems of spectrum shortage and soaring energy consumption are becoming increasingly prominent. To address this issue, symbiotic networks have emerged as a technology. A symbiotic network is a wireless communication network in which active and passive communication systems coexist. The active communication system is a conventional wireless communication system, while the passive communication system is an ambient backscatter communication (AmBC) system, which transmits data by backscattering the radio frequency signals of the active communication system.

[0003] Existing research on transmission methods for symbiotic networks focuses on throughput optimization, using Time Division Multiple Access (TDMA) for transmission communication modeling and optimization. The transmission method is designed as follows: In the first stage, a dedicated power base station transmits radio frequency (RF) signals to users via TDMA. Each user receives the RF signal in a separate time slot and simultaneously performs energy harvesting and backscatter communication, with the backscatter communication assisted by a Reconfigurable Intelligent Surface (RIS). In the second stage, all users utilize the energy harvested in the first stage to send information to the Access Point (AP) via Space Division Multiple Access (SDMA), i.e., active communication, which is also assisted by RIS.

[0004] However, the above transmission method requires a specific power transmission point to transmit radio frequency signals to the user so that the user can harvest energy and communicate, which leads to certain limitations of the transmission method. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a transmission method for joint backscatter communication and active communication in a symbiotic network, solving the technical problem that existing technologies require a specific power transmission point to transmit radio frequency signals to users.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] This invention provides a transmission method for joint backscatter communication and active communication in a symbiotic network. The symbiotic network contains a symbiotic radio transmission system, which includes a base station, a primary user, multiple secondary user pairs with backscatter communication capabilities, and a RIS (Real-In-Reference System) set between the base station and the secondary user pairs. The secondary user pairs with backscatter communication capabilities include a backscatter transmitter and a backscatter receiver. The transmission method includes two stages.

[0010] The first phase includes:

[0011] The backscatter transmitter uses the signal sent from the base station to the primary user in the native network for backscatter communication and energy harvesting. At the same time, RIS assists communication to suppress the interference of the backscatter transmitter to the native cellular user.

[0012] The second phase includes:

[0013] The backscatter transmitter uses the energy collected in the first stage for active communication, while the RIS performs interference suppression.

[0014] Preferably, the transmission method aims to maximize throughput by setting the base station transmit beamforming, the reflection coefficient matrix of the RIS, the backscattering coefficient and transmit power of the backscattering user, and the time slot allocation for two-stage communication in the symbiotic radio transmission system.

[0015] Preferably, with the goal of maximizing throughput, the base station transmit beamforming, the reflection coefficient matrix of the RIS, the backscattering coefficient and transmit power of the backscattering user, and the time slot allocation for two-stage communication in the coexisting radio transmission system are defined, including:

[0016] S1. Acquire data for each channel, base station beamforming vector, RIS reflection coefficient matrix in two stages, and energy collected by the transmitter;

[0017] S2. Based on the channel data, base station beamforming vector, RIS reflection coefficient matrix in the two stages, and the energy collected by the transmitter, construct the total throughput function of the user and the rate function of the primary user in the two stages.

[0018] S3. Construct a throughput maximization problem model based on the total throughput function of users and the rate function of the main user in the two stages;

[0019] S4. Calculate the base station transmit beamforming that maximizes throughput based on the throughput maximization problem model, the reflection coefficient matrix of RIS, the backscattering coefficient and transmit power of the backscattering user, and the time slot allocation for two-stage communication.

[0020] Preferably, S1 includes:

[0021] The channel data from the base station to the primary user is h0∈C M×1 C M×1 Let h be an M×1 complex matrix, representing the channel data h from the base station to the k-th reflecting transmitter. k ∈C M×1 Channel data G∈C from base station to RIS N×M C N×M Let f0∈C be an N×M complex matrix representing the channel data from RIS to the primary user. N×1 C N×1 Let f represent an N×1 complex matrix, and let f be the channel data from RIS to the k-th reflector transmitter. k ∈C N×1 The channel data z from the k-th reflector transmitter to the primary user k ;

[0022] In the first phase, the base station transmits an independent signal to the primary user, expressed as:

[0023] x c =ws

[0024] In the formula, x c Let w represent the signal sent by the base station to the primary user, w represent the reflected beamforming vector of the base station, and s represent the information sent by the base station to the primary user, satisfying E{|s| 2} = 1, E represents the desired operation, and || represents the modulo operation of complex numbers; RIS assists in transmitting signals from the main user CU and passively enhances the signal energy received by the backscatter transmitter;

[0025] In the first phase, the mixed signal received by the k-th backscatter transmitter from the base station's direct link and the RIS reflection link is represented as:

[0026]

[0027] In the formula, This represents the mixed signal received by the k-th backscatter transmitter from the base station's direct link and the RIS reflection link, where n0 represents the noise received by the backscatter transmitter, and n0 satisfies zero mean and δ variance. 2 Additive white Gaussian noise, where Θ represents the reflection coefficient matrix of the first-stage RIS; In the formula θ n The reflection coefficient phase of the nth element is represented, 1≤n≤N, the function diag() represents the diagonal matrix of the vector, e represents the natural exponent, and the superscript j represents the imaginary unit;

[0028] In the first stage, the received signals of the k-th backscatter receiver and the main user are represented as follows:

[0029]

[0030]

[0031] In the formula, This represents the signal received by the k-th backscatter receiver in the first stage. This represents the signal received by the primary user in the first phase, β. k Let n represent the backscattering coefficient of the k-th backscattering transmitter. k n represents the noise received by the backscatter transmitter. k To satisfy the condition that the mean is zero and the variance is δ 2 Additive white Gaussian noise;

[0032] In the first stage, the energy collected by the k-th backscatter transmitter is represented as:

[0033]

[0034] In the formula, Let τ represent the energy collected by the k-th backscatter transmitter in the first stage. I α represents the communication time in the first phase. k This represents the energy harvesting efficiency of the k-th backscatter transmitter;

[0035] In the second stage, each backscatter transmitter uses the collected energy to actively send information to its corresponding receiver. In this second stage, the received signals from the k-th backscatter receiver and the main user are represented as follows:

[0036]

[0037]

[0038] In the formula, This represents the signal received by the k-th backscatter receiver in the second stage. This indicates the signal received by the primary user in the second phase, representing P. k The transmit power of the k-th backscatter receiver in the second stage, x d,k This represents the signal transmitted by the k-th backscatter transmitter in the second stage. This represents the reflection coefficient matrix of the second-stage RIS; In the formula Let represent the reflection coefficient phase of the nth element, 1≤n≤N, the function diag() represents the diagonal matrix of the vector, e represents the natural exponent, and the superscript j represents the imaginary unit.

[0039] Preferably, the total throughput function of the user and the rate function of the main user in the two stages include:

[0040] The total communication throughput function for users in the first phase is as follows:

[0041]

[0042]

[0043] In the formula: This represents the signal-to-interference-plus-noise ratio (SINR) of the k-th backscatter receiver in the first stage.

[0044] The rate function for the primary user in the first phase is as follows:

[0045]

[0046]

[0047] In the formula This indicates the signal-to-interference-plus-noise ratio (SIR) of the primary user in the first stage;

[0048] The total communication throughput function for users in the second phase is as follows:

[0049]

[0050]

[0051] In the formula, This represents the signal-to-interference-plus-noise ratio (SINR) of the k-th backscatter receiver in the second stage.

[0052] The rate function for the primary user in the second phase is as follows:

[0053]

[0054]

[0055] In the formula, This indicates the signal-to-interference-plus-noise ratio (SIR) for the primary user in the second phase.

[0056] Preferably, the throughput maximization problem model includes an objective function and constraints; wherein the objective function is as follows:

[0057]

[0058] The constraints are as follows:

[0059]

[0060]

[0061]

[0062] C4:τI +τ II =1,

[0063]

[0064]

[0065]

[0066] In the formula, and This represents the primary user's QoS requirements in the first and second phases, respectively, β. k The reflection coefficient of the k-th backscattering transmitter is a number between 0 and 1. This indicates the base station's transmit power limit;

[0067] Constraints C1 and C2 respectively indicate that the data rate of the primary user in the first and second phases must not be lower than [a certain value]. and

[0068] Constraint C3 means that the total energy used for transmission by the k-th backscatter transmitter in the second stage cannot exceed the energy it collects;

[0069] Constraint C4 indicates that the sum of the time slots in the two stages is 1;

[0070] Constraint C5 indicates that the backscattering coefficient of the k-th backscattering transmitter is a number between 0 and 1;

[0071] Constraint C6 indicates that the base station's transmit power must not exceed [a certain value].

[0072] Constraint C7 represents the modulo-1 constraint of the RIS phase.

[0073] Preferably, S4 includes:

[0074] S401. Given the remaining variables, solve the throughput maximization problem using the continuous convex approximation method and the semidefinite relaxation method to obtain suboptimal solutions Θ and β. * and β * ;

[0075] S402, based on the obtained Θ * and β * Solve for the second-order RIS reflection coefficient matrix. And the transmission power P;

[0076] S403, based on the obtained Θ * and β * and P * , By using one-dimensional search, semidefinite relaxation, and geometric mean inequality, suboptimal solutions for time slot allocation and base station transmit beamforming are obtained;

[0077] S404. Repeat steps S401 to S403 until convergence, obtaining the suboptimal solution w. * Θ * , P * β * τ I and τ II This refers to the base station transmit beamforming that maximizes the throughput of the symbiotic radio transmission system, including the RIS reflection coefficient matrix, user transmit power, backscattering coefficient, and time slot allocation in the first and second stages.

[0078] Preferably, S401 includes:

[0079] Given the remaining variables, i.e., w and τ are fixed. I ,τ II P and In the case of defining

[0080]

[0081] The throughput expression in the objective function can be rewritten as follows:

[0082]

[0083] The rate expression for cellular users in the constraints is rewritten as follows:

[0084]

[0085] The throughput maximization problem model is transformed into the following P2 form:

[0086] P2:

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] in

[0093] Using the geometric mean inequality method to apply constraints The process is performed to obtain convex conditions acceptable to the CVX toolbox. The specific operation is as follows:

[0094]

[0095]

[0096] Using mathematical relaxation and continuous convex approximation methods, an auxiliary variable c is introduced. k ,χ k , The objective function is transformed into the following form;

[0097]

[0098]

[0099]

[0100]

[0101] In the formula, Let represent the Taylor expansion point of the i-th iteration. At this point, the non-convex constraints are transformed into convex constraints, and the throughput maximization problem model is rewritten in the following P3 form:

[0102] P3:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] C7:rank(V1)=1

[0110] By relaxing the rank-one constraint rank(V1) = 1 using a positive semidefinite relaxation method, problem P3 is transformed into a convex optimization problem. The convex optimization problem is solved using the CVX tool, and a rank-one solution is obtained through Gaussian randomization. The updated value is then substituted into the convex optimization problem for iteration until convergence, finally yielding the suboptimal solution Θ. * and β * .

[0111] Preferably, S402 includes:

[0112] Fixed Θ * and β * After the values ​​of these two variables, R in the objective function I Regardless of variables, defined

[0113] v2 = [v 2,1 ,…,v 2,N ] H , Will swallow

[0114] The quantity maximization problem model can be transformed into the following form:

[0115] P4:

[0116]

[0117]

[0118]

[0119]

[0120] in

[0121] P4 is a non-convex optimization problem. The following sections will discuss the non-convex objective function and constraints. Processing:

[0122] For the objective function, by introducing the auxiliary variable λ k The objective function is transformed into:

[0123]

[0124] ∑ k′≠k P k′ |g k′k | 2 +δ 2 ≤λ k .

[0125] The objective function described above is in the form of a convexity, therefore the second part of the above equation, log2λ, is... k Its first-order Taylor expansion can be used as a substitute, as follows:

[0126]

[0127] Where λ is the variable k The value of the i-th iteration;

[0128] Will This can be transformed into the following convex constraint condition:

[0129]

[0130] In the formula, μ0 is an introduced auxiliary variable, whose optimal value is... At this point, the optimization problem P4 is transformed into the following convex problem:

[0131] P5:

[0132]

[0133]

[0134] By iteratively solving problem P5, the suboptimal solution P of P and V2 is obtained. * ,

[0135] Preferably, S403 includes:

[0136] By defining W = ww H W is a defined semi-definite relaxed transmit beamforming variable. as well as These are auxiliary variables used to simplify expressions. The simplified optimization problem is as follows:

[0137] P6:

[0138]

[0139]

[0140]

[0141]

[0142] By introducing random variables e k , The objective function is transformed into:

[0143]

[0144]

[0145] Substituting the above objective function into optimization problem P6, we obtain the following optimization problem P7:

[0146] P7:

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154] The suboptimal solution to the optimization problem P7 is obtained by solving the semidefinite relaxation method.

[0155] (III) Beneficial Effects

[0156] This invention provides a transmission method for joint backscatter communication and active communication in a symbiotic network. Compared with the prior art, it has the following advantages:

[0157] In the first stage, the transmission method of this invention does not require a specific power base station to transmit radio frequency signals. Instead, secondary users can utilize signals transmitted from the symbiotic network base station to the primary user for backscatter communication and energy harvesting. While meeting the normal communication requirements of the system, this significantly reduces the base station's transmit power consumption and improves spectrum utilization. Attached Figure Description

[0158] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0159] Figure 1 This is a schematic diagram of the transmission method for joint backscatter communication and active communication under a symbiotic network in an embodiment of the present invention;

[0160] Figure 2 This is a schematic diagram illustrating the convergence behavior of the transmission method and the baseline method in a multi-user scenario according to an embodiment of the present invention.

[0161] Figure 3 A schematic diagram illustrating the relationship between throughput and the number N of RIS reflective elements;

[0162] Figure 4 This is a schematic diagram showing the relationship between throughput and transmit power for all methods under different numbers of base station antennas, where the reflective element is set to N=10. Detailed Implementation

[0163] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0164] This application provides a transmission method for joint backscatter communication and active communication in a symbiotic network, which solves the technical problem of the prior art that requires a specific power transmission point to transmit radio frequency signals to users. By having secondary users directly borrow the signals sent by the symbiotic network base station to the primary users (such as cellular users) to perform backscatter communication and energy harvesting, it is possible to achieve radio frequency signal transmission without the need for a specific power base station.

[0165] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0166] Current transmission methods that combine joint backscatter communication and active communication mainly include the following two approaches:

[0167] The first method is a transmission method combining backscatter and active communication in uplink multi-user cellular networks. The specific design of this method is the one mentioned in the background section. This method optimizes throughput by jointly optimizing the reflection coefficient of the IRS, the base station's transmit / receive beamforming, user power allocation, and time allocation coefficients. For the optimization problem in the model, a solution is designed using mathematical methods such as alternating optimization, continuous convex optimization, semi-definite relaxation, and block gradient descent. This method offers performance improvements compared to pure backscatter and pure active communication methods.

[0168] The second transmission method is similar to the first, also employing a combined backscattering and active communication approach in uplink cellular networks. The difference lies in that all users in the second method utilize TDMA. Furthermore, this second method addresses the energy efficiency problem, specifically maximizing EE (energy efficiency). Since all communication uses TDMA, there is no inter-user interference, making the optimization problem modeled relatively simple, and a closed-form solution can be obtained through differentiation using KKT conditions.

[0169] However, the above two transmission methods, which consider traditional cellular networks, require a specific power transmission point to transmit radio frequency signals to users, enabling them to perform energy harvesting and communication, which has certain limitations. To solve this problem, embodiments of the present invention provide a transmission method for joint backscatter communication and active communication in a symbiotic network. This method does not require a specific power base station to transmit radio frequency signals; instead, secondary users can borrow signals sent by the symbiotic network base station to the primary user (which may be a cellular user, etc.) to perform backscatter communication and energy harvesting.

[0170] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0171] This invention provides a method for transmitting joint backscatter communication and active communication in a symbiotic network. The symbiotic network includes a symbiotic radio transmission system, comprising a base station, a primary user (cellular user CU), multiple secondary user pairs with backscatter communication capabilities, and a RIS (Remote Information System) positioned between the base station and the secondary user pairs. Each secondary user pair with backscatter communication capabilities includes a backscatter transmitter and a backscatter receiver, such as... Figure 1 As shown, Figure 1 The PBS (power base station) in this context refers to the base station. The transmission method comprises two stages.

[0172] The first phase includes:

[0173] The backscatter transmitter uses the signals sent from the base station to the cellular user in the native network for backscatter communication and energy harvesting. At the same time, RIS assists communication to suppress the interference of the backscatter transmitter to the native cellular user.

[0174] The second phase includes:

[0175] The backscatter transmitter uses the energy collected in the first stage for active communication, while the RIS performs interference suppression.

[0176] In the first stage of the transmission method of this invention, a specific power base station is not required to transmit radio frequency signals. Instead, secondary users can utilize signals transmitted from the symbiotic network base station to the primary user for backscatter communication and energy harvesting. While meeting the normal communication requirements of the system, this significantly reduces the base station's transmit power consumption and improves spectrum utilization.

[0177] The transmission method will be explained in detail below:

[0178] In specific implementation, the embodiments of the present invention aim to maximize throughput. The base station transmit beamforming, the reflection coefficient matrix of the RIS, the backscattering coefficients and transmit power of the backscattering users, and the time slot allocation for two-stage communication in the co-occurring radio transmission system described above are obtained through the following method:

[0179] S1. Acquire data for each channel, base station beamforming vector, RIS reflection coefficient matrix in two stages, and energy collected by the transmitter. The specific implementation process is as follows:

[0180] The system acquires channel data from the base station to the primary user, channel data from the base station to each reflective transmitter, channel data from the base station to the RIS (Radio Reflection Array), channel data from the RIS to the primary user (cellular user CU), channel data from the RIS to each reflective transmitter, channel data from each reflective transmitter to the primary user, base station beamforming vector, RIS reflection coefficient matrix, backscattering coefficient and transmit power of each reflective transmitter, and energy collected by each backscattering transmitter. The specific implementation process is as follows:

[0181] The base station is a base station including M transmitting antennas, the RIS includes N reflecting elements, and the main user and the reflecting transmitter are both single antennas.

[0182] The channel data from the base station to the primary user (cellular user CU) is h0∈C M×1 C M×1 Represents an M×1 complex matrix, used to obtain the channel data h from the base station to the k-th secondary user (reflector transmitter). k ∈C M×1 Channel data G∈C from base station to RIS N×M C N×M Let f0∈C be an N×M complex matrix representing the channel data from RIS to the primary user (cellular user CU). N×1 C N×1 Let f be an N×1 complex matrix representing the channel data f from RIS to the k-th secondary user (reflector transmitter). k ∈C N×1 The channel data z from the k-th secondary user (reflector transmitter) to the primary user (cellular user CU). k .

[0183] Considering the scenario of coexisting radio, in the first phase, the base station transmits independent signals to the primary user (cellular user CU), expressed as:

[0184] x c =ws

[0185] In the formula, x cLet w represent the signal sent by the base station to the primary user, w represent the reflected beamforming vector of the base station, and s represent the information sent by the base station to the primary user, satisfying E{|s| 2} = 1, E represents the desired operation, and || represents the modulo operation of complex numbers; RIS assists in transmitting signals from the main user CU and passively enhances the signal energy received by the backscatter transmitter.

[0186] In the first phase, the mixed signal received by the k-th backscatter transmitter from the base station's direct link and the RIS reflection link is represented as:

[0187]

[0188] In the formula, This represents the mixed signal received by the k-th backscatter transmitter from the base station's direct link and the RIS reflection link, where n0 represents the noise received by the backscatter transmitter, and n0 satisfies zero mean and δ variance. 2 Additive white Gaussian noise, where Θ represents the reflection coefficient matrix of the first-stage RIS; In the formula θ n Let represent the reflection coefficient phase of the nth element, 1≤n≤N, the function diag() represents the diagonal matrix of the vector, e represents the natural exponent, and the superscript j represents the imaginary unit.

[0189] Based on the received signal from the k-th backscatter transmitter, the k-th backscatter transmitter simultaneously harvests energy from the received signal and performs backscatter modulation, then sends the modulated signal to the k-th backscatter receiver. Therefore, in the first stage, the received signals from the k-th backscatter receiver and the primary user (CU) are respectively represented as follows:

[0190]

[0191]

[0192] In the formula This represents the signal received by the k-th backscatter receiver in the first stage. This represents the signal received by the primary user (cellular user) in the first phase, β. k Let n represent the backscattering coefficient of the k-th backscattering transmitter. k n represents the noise received by the backscatter transmitter. k To satisfy the condition that the mean is zero and the variance is δ 2 Additive white Gaussian noise.

[0193] In the first stage, the energy collected by the k-th backscatter transmitter can be expressed as:

[0194]

[0195] In the formula, Let τ represent the energy collected by the k-th backscatter transmitter in the first stage. I α represents the communication time in the first phase. k This represents the energy harvesting efficiency of the k-th backscatter transmitter.

[0196] In the second stage, each backscatter transmitter uses the collected energy to actively send information to its corresponding receiver. In this second stage, the received signals from the k-th backscatter receiver and the primary user (CU) are represented as follows:

[0197]

[0198]

[0199] In the formula, This represents the signal received by the k-th backscatter receiver in the second stage. This represents the signal received by the primary user (cellular user) in the second phase, representing P. k The transmit power of the k-th backscatter receiver in the second stage, x d,k This represents the signal transmitted by the k-th backscatter transmitter in the second stage. This represents the reflection coefficient matrix of the second-stage RIS; In the formula Let represent the reflection coefficient phase of the nth element, 1≤n≤N, the function diag() represents the diagonal matrix of the vector, e represents the natural exponent, and the superscript j represents the imaginary unit.

[0200] S2. Based on the channel data obtained in S1, the base station beamforming vector, the reflection coefficient matrix of RIS in the two stages, and the energy collected by the transmitter, construct the total throughput function for users and the rate function for primary users in the two stages. The specific implementation process is as follows:

[0201] The total communication throughput function for users in the first stage (backscatter communication stage) is as follows:

[0202]

[0203]

[0204] In the formula: This represents the signal-to-interference-plus-noise ratio (SIR) of the k-th backscatter receiver in the first stage.

[0205] The rate function for the primary user (cellular user) in the first stage (backscatter communication stage) is as follows:

[0206]

[0207]

[0208] In the formula This represents the signal-to-interference-plus-noise ratio (SIR) for the primary user (cellular user) in the first phase.

[0209] The total communication throughput function for users in the second phase (active communication phase) is as follows:

[0210]

[0211]

[0212] In the formula, This represents the signal-to-interference-plus-noise ratio (SNR) of the k-th backscatter receiver in the second stage.

[0213] The rate function for the primary user (cellular user) in the second phase (active communication phase) is as follows:

[0214]

[0215]

[0216] In the formula, This indicates the signal-to-interference-plus-noise ratio (SIR) for the primary user (cellular user) in the second phase.

[0217] In step S3, a throughput maximization problem model is constructed based on the total throughput function of users and the rate function of the primary user in both stages. The specific implementation process is as follows:

[0218] The throughput maximization problem model includes the objective function and constraints:

[0219] The objective function is as follows:

[0220]

[0221] The constraints are as follows:

[0222]

[0223]

[0224]

[0225] C4:τ I +τ II =1,

[0226]

[0227]

[0228]

[0229] In the formula, and This represents the QoS requirements of primary users (cellular users) in Phase 1 and Phase 2 respectively, β k The reflection coefficient of the k-th backscatter transmitter is a number between 0 and 1. This indicates the base station's transmit power limit;

[0230] Constraints C1 and C2 respectively mean that the data rate of the primary user (cellular user) in the first and second phases shall not be lower than that of the primary user (cellular user). and

[0231] Constraint C3 means that the total energy used for transmission by the k-th backscatter transmitter in the second stage cannot exceed the energy it collects;

[0232] Constraint C4 indicates that the sum of the time slots in the two stages is 1;

[0233] Constraint C5 indicates that the backscattering coefficient of the k-th backscattering transmitter is a number between 0 and 1;

[0234] Constraint C6 indicates that the base station's transmit power must not exceed [a certain value].

[0235] Constraint C7 represents the modulo-1 constraint of the RIS phase.

[0236] S4. Based on the throughput maximization problem model, calculate the base station transmit beamforming that maximizes throughput, the reflection coefficient matrix of RIS, the backscattering coefficients and transmit power of backscattering users, and the time slot allocation for two-stage communication. The specific implementation process is as follows:

[0237] Analysis of the objective function and constraints reveals that, given the base station transmit beamforming w and time slot allocation τ, I ,τ II Transmit power P and the second-stage RIS reflection coefficient matrix In this case, the suboptimal solution to the first-stage RIS reflection coefficient matrix Θ and user backscattering coefficient β can be obtained by solving the problem using the Successive Convex Approximation (SCA) method and the Semi-definite Relaxation (SDR) method. Let the suboptimal solution be Θ. * and β * Given Θ * and β * And given w, τ I ,τ II In the case of solving the second-order RIS reflection coefficient matrix And the suboptimal solution for the transmit power P, assuming the suboptimal solution is... and P * Finally, given Θ * ,β * , P * Solve for the suboptimal solution w of the base station transmit beamforming. * The optimal solution for time slot allocation is obtained through a one-dimensional search. The specific steps are as follows:

[0238] S401. Given the remaining variables, solve the throughput maximization problem using the continuous convex approximation method and the semidefinite relaxation method to obtain suboptimal solutions Θ and β. * and β * Specifically:

[0239] Given the remaining variables, i.e., w and τ are fixed. I ,τ II P and In the case of defining

[0240]

[0241] The throughput expression in the objective function can be rewritten as follows:

[0242]

[0243] The rate expression for cellular users in the constraints is rewritten as follows:

[0244]

[0245] The system throughput maximization problem can be transformed into the following P2 form:

[0246] P2:

[0247]

[0248]

[0249]

[0250]

[0251]

[0252] in

[0253] Due to constraints Given the objective function and the rank-one condition, this problem remains non-convex, making it impossible to directly find the optimal solutions for Θ and β. To solve the optimization problem, methods such as the geometric mean inequality are used to adjust the constraints. The condition is processed to become a convex condition recognized by the CVX toolbox. The specific operation is as follows:

[0254]

[0255]

[0256] Using methods such as mathematical relaxation and continuous convex approximation, an auxiliary variable c is introduced. k ,χ k , The objective function is transformed into the following form;

[0257]

[0258]

[0259]

[0260]

[0261] In the formula, Let represent the Taylor expansion point of the i-th iteration. At this point, the non-convex constraints are transformed into convex constraints, and the system throughput maximization problem can be rewritten in the following P3 form:

[0262] P3:

[0263]

[0264]

[0265]

[0266]

[0267]

[0268]

[0269] C7:rank(V1)=1

[0270] However, due to the constraint rank(V1) = 1, the problem remains non-convex. By relaxing the rank-one constraint using a positive semidefinite relaxation method, problem P3 is transformed into a convex optimization problem. The convex optimization problem is solved using the CVX tool, and a rank-one solution is obtained through Gaussian randomization. The updated value is then substituted into the convex optimization problem for iteration until convergence, finally yielding the suboptimal solution Θ. * and β * .

[0271] S402, based on the obtained Θ * and β * The second-order RIS reflection coefficient matrix is ​​solved using a method similar to that in step S401. And the transmission power P. Specifically:

[0272] In obtaining Θ * and β * After that, that is, after fixing the values ​​of these two variables, R in the objective function I It is independent of variables, therefore it can be simplified. By defining... v2 = [v 2,1 ,…,v 2,N ] H , The original problem can be transformed into the following form:

[0273] P4:

[0274]

[0275]

[0276]

[0277]

[0278] in

[0279] Problem P4 above is a non-convex optimization problem. The following sections will discuss the non-convex objective function and constraints. To process this, firstly, for the objective function, we introduce an auxiliary variable λ. k The objective function can be transformed into

[0280]

[0281] ∑ k′≠k P k′ |g k′k | 2 +δ 2 ≤λ k .

[0282] The objective function described above is in the form of a difference of concave (DC), therefore the second part of the above equation, log2λ, is... k Its first-order Taylor expansion is used as a substitute, and it is expressed as follows:

[0283]

[0284] Where λ is the variable. k The value of the i-th iteration.

[0285] Similarly, for constraints This is processed using the geometric mean inequality. This can be transformed into the following convex constraint condition:

[0286]

[0287] In the formula, μ0 is an introduced auxiliary variable, whose optimal value is... At this point, the optimization problem P4 can be transformed into the following convex problem:

[0288] P5:

[0289]

[0290]

[0291]

[0292] By iteratively solving problem P5, the suboptimal solution P of P and V2 is obtained. * ,

[0293] S403, based on the obtained Θ * and β * and P * , Using methods such as one-dimensional search, semi-definite relaxation, and geometric mean inequality, suboptimal solutions for time slot allocation and base station transmit beamforming are obtained. The optimal solution for time slot allocation is obtained through one-dimensional search, while transmit beamforming is obtained by solving a semi-definite relaxation problem. Specifically:

[0294] By defining W = ww H W is a defined semi-definite relaxed transmit beamforming variable. as well as These are auxiliary variables used to simplify expressions. The simplified optimization problem is as follows:

[0295] P6:

[0296]

[0297]

[0298]

[0299]

[0300] Observing the optimization problem above, only the objective function is non-convex. Therefore, we only need to transform the objective function into a convex one and solve the transformed optimization problem using CVX to obtain the suboptimal solution for W. Referring to the processing methods in steps S402 and S401, we introduce random variables... e k , The objective function can be transformed into:

[0301]

[0302]

[0303] Substituting the above objective function into optimization problem P6, we obtain the following optimization problem P7:

[0304] P7:

[0305]

[0306]

[0307]

[0308]

[0309]

[0310]

[0311]

[0312] By solving optimization problem P7 using a semidefinite relaxation method, a suboptimal solution to W can be obtained. Furthermore, a rank-1 solution to W can be obtained using Gaussian randomization.

[0313] S404. Repeat steps S401 to S403 until convergence. The solution obtained is the suboptimal solution w of the system throughput maximization problem P1. * Θ * , P * β * τ I and τ IIThis refers to the base station transmit beamforming that maximizes system throughput as described in step S4, including the RIS reflection coefficient matrix in the first and second stages, user transmit power and backscattering coefficient, and time slot allocation.

[0314] The effectiveness of the embodiments of the present invention is verified through simulation comparison experiments below.

[0315] 1. Backscatter communication only; 2. Energy harvesting and transmission only; 3. No RIS-assisted communication technology. The simulated network topology is described as a 2D coordinate system, where the base station is at the origin, K backscatter transmitters are distributed within a circle centered at [50,0] with a radius of 5, and corresponding backscatter receivers are distributed within a circle centered at [65,0] with a radius of 5, with a one-to-one correspondence between transmitters and receivers. RIS is deployed between the base station and the backscatter transmitters. The channel uses the Ricean channel model, with a Ricean parameter of 3 and a path loss exponent of 2.2. Unless otherwise specified, the following parameters will be used in the simulation: the number of backscatter transceivers K = 3, the number of base station transmit antennas is 4 or 6, the number of RIS components is between 5 and 50, the maximum transmit power of the base station is between 0 and 40 dBm, the noise power is set to -110 dBm, and the signal-to-dryness ratio constraint is set to... All simulations passed 500 Monte Carlo experiments.

[0316] Figure 2 The convergence behavior of the proposed method and benchmark methods in a multi-user scenario is presented. The results show that all methods monotonically increase with the number of iterations and converge quickly to a stationary point as expected.

[0317] Figure 3 The relationship between throughput and the number of RIS reflective elements N is shown, where the transmit antenna is M=4 and the maximum transmit power of the base station is 30dBm. Figure 3 As shown, the larger the number of reflective elements, the greater the throughput, because a larger number of RIS reflective elements can provide higher multiplexing and passive array gain, which is useful for enhancing signal energy. This illustrates the advantages of the RIS-assisted communication link between the user and the PBS. Furthermore, the HTT scheme with a small number of reflective elements exhibits the worst performance because the energy gained is finite within a coherent time.

[0318] Figure 4The relationship between throughput and transmit power for all methods is shown with varying numbers of base station antennas, where the reflective elements are set to N=10. Throughput is observed to increase with transmit power because higher transmit power leads to a higher signal-to-noise ratio, and a larger number of reflective elements achieves higher array gain. Furthermore, in multi-user systems, the throughput gain from increasing transmit antennas is greater than in single-user systems due to the utilization of more channels provided by the users for information transmission and energy harvesting.

[0319] In summary, compared with existing technologies, it has the following beneficial effects:

[0320] 1. In the first stage of the transmission method of this invention, a specific power base station is not required to transmit radio frequency signals. Instead, secondary users can use signals transmitted from the symbiotic network base station to the primary user for backscatter communication and energy harvesting. While meeting the normal communication requirements of the system, this significantly reduces the base station's transmit power consumption and improves spectrum utilization.

[0321] 2. In the first stage, the embodiment of the present invention considers the SDMA mode, that is, the secondary user can perform energy harvesting and backscatter communication simultaneously in all time slots. Although this design will cause interference to the native users in the symbiotic network, the embodiment of the present invention can suppress the interference caused by the secondary user to the native user as much as possible and enhance the quality of the radio frequency signal received by the secondary user as much as possible by designing the reflection beamforming of the RIS and the reflection beamforming of the base station.

[0322] 3. This invention provides a resource allocation scheme for multi-user scenarios. Since this invention considers the full SDMA mode, there are complex interference terms that need to be addressed when modeling the mathematical optimization problem. This invention transforms the optimization problem using mathematical methods such as convex second-order cones, convex quadratic programming, MM algorithm, semidefinite relaxation, and continuous convex approximation, and designs an efficient resource allocation optimization algorithm.

[0323] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0324] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A transmission method for joint backscatter communication and active communication in a symbiotic network, characterized in that, The symbiotic network contains a symbiotic radio transmission system, which includes a base station, a primary user, multiple secondary user pairs with backscatter communication capabilities, and a RIS set between the base station and the secondary user pairs. The secondary user pairs with backscatter communication capabilities include a backscatter transmitter and a backscatter receiver. The transmission method includes two stages. The first phase includes: The backscatter transmitter uses the signal sent from the base station to the primary user in the native network for backscatter communication and energy harvesting. At the same time, RIS assists communication to suppress the interference of the backscatter transmitter to the native cellular user. The second phase includes: The backscatter transmitter uses the energy collected in the first stage for active communication, while the RIS performs interference suppression. The transmission method, aiming to maximize throughput, sets the base station transmit beamforming, the reflection coefficient matrix of the RIS, the backscattering coefficient and transmit power of the backscattering user, and the time slot allocation for two-stage communication in the coexisting radio transmission system, including: S1. Acquire data for each channel, base station beamforming vector, RIS reflection coefficient matrix in two stages, and energy collected by the transmitter; S2. Based on the channel data, base station beamforming vector, RIS reflection coefficient matrix in the two stages, and the energy collected by the transmitter, construct the total throughput function of the user and the rate function of the primary user in the two stages. S3. Construct a throughput maximization problem model based on the total throughput function of users and the rate function of the main user in the two stages; S4. Calculate the base station transmit beamforming that maximizes throughput based on the throughput maximization problem model, the reflection coefficient matrix of the RIS, the backscattering coefficients and transmit power of backscattering users, and the time slot allocation for two-stage communication; including: S401. Given the remaining variables, solve the throughput maximization problem using the continuous convex approximation method and the semidefinite relaxation method to obtain... and suboptimal solution and ;in, This represents the reflection coefficient matrix of the first-stage RIS. This represents the user backscattering coefficients of the first-stage RIS; S402, based on the obtained and Solve for the second-order RIS reflection coefficient matrix. and transmission power ; S403, based on the obtained and as well as By using one-dimensional search, semidefinite relaxation, and geometric mean inequality, suboptimal solutions for time slot allocation and base station transmit beamforming are obtained. S404. Repeat steps S401 to S403 until convergence, obtaining the suboptimal solution. , , , , , and This refers to the base station transmit beamforming that maximizes the throughput of the symbiotic radio transmission system, including the RIS reflection coefficient matrix, user transmit power and backscattering coefficient, and time slot allocation in the first and second stages; among which, This represents the suboptimal solution for the reflected beamforming vector of the base station. The throughput maximization problem model includes an objective function and constraints; The objective function is as follows: The constraints are as follows: In the formula, and This indicates the primary user's QoS requirements in the first and second phases, respectively. The reflection coefficient of the k-th backscattering transmitter is a number between 0 and 1. This indicates the base station's transmit power limit. This represents the reflected beamforming vector of the base station. This indicates the signal-to-interference-plus-noise ratio (SIR) for the primary user in the first stage. This indicates the signal-to-interference-plus-noise ratio (SIR) for the primary user in the second stage. This represents the total communication throughput of users in the first phase. This represents the total communication throughput of users in the second phase. Indicates time slot allocation, This represents the energy collected by the k-th backscatter transmitter in the first stage; Constraints C1 and C2 respectively indicate that the data rate of the primary user in the first and second phases must not be lower than [a certain value]. and ; Constraint C3 means that the total energy used for transmission by the k-th backscatter transmitter in the second stage cannot exceed the energy it collects; Constraint C4 indicates that the sum of the time slots in the two stages is 1; Indicates the communication time in the first phase; Constraint C5 represents the first... k The backscattering coefficient of a backscattering transmitter is between 0 and 1. Constraint C6 indicates that the base station's transmit power must not exceed [a certain value]. ; Constraint C7 represents the modulo-1 constraint of the RIS phase.

2. The transmission method for joint backscatter communication and active communication in a symbiotic network as described in claim 1, characterized in that, S1 includes: The channel data from the base station to the primary user is , Represents an M×1 complex matrix, used to obtain channel data from the base station to the k-th reflecting transmitter. Channel data from base station to RIS , Represents an N×M complex matrix, representing the channel data from the RIS to the primary user. , Represents an N×1 complex matrix, showing the channel data from RIS to the k-th reflector transmitter. Channel data from the k-th reflector transmitter to the primary user ; In the first phase, the base station transmits an independent signal to the primary user, expressed as: In the formula, This indicates the signal sent by the base station to the primary user. This represents the reflected beamforming vector of the base station. This represents the information sent by the base station to the primary user, and satisfies... , Indicates the desired operation. Represents the modulo operation of complex numbers; RIS assists in transmitting signals from the primary user CU, while passively boosting the signal energy received by the backscatter transmitter; In the first phase, the The mixed signal received by a backscatter transmitter from the base station's direct link and the RIS reflection link is represented as follows: In the formula, This indicates that the k-th backscatter transmitter receives a mixed signal from the base station's direct link and the RIS reflection link. This represents the noise received by the backscatter transmitter. To satisfy the condition that the mean is zero and the variance is... Additive white Gaussian noise, This represents the reflection coefficient matrix of the first-stage RIS; In the formula Indicates the first n The phase of the reflection coefficient of each element, The function diag() represents a diagonal matrix of vectors. e Indicates the natural index, superscript j Represents the imaginary unit; In the first stage, the received signals of the k-th backscatter receiver and the main user are represented as follows: In the formula, This represents the signal received by the k-th backscatter receiver in the first stage. This indicates the signal received by the primary user in the first phase. This represents the backscattering coefficient of the k-th backscattering transmitter. This represents the noise received by the backscatter transmitter. To satisfy the condition that the mean is zero and the variance is... Additive white Gaussian noise; In the first stage, the energy collected by the k-th backscatter transmitter is represented as: In the formula, This represents the energy collected by the k-th backscatter transmitter in the first stage. This indicates the communication time in the first phase. This represents the energy harvesting efficiency of the k-th backscatter transmitter; In the second stage, each backscatter transmitter uses the collected energy to actively send information to its corresponding receiver. In this second stage, the received signals from the k-th backscatter receiver and the main user are represented as follows: In the formula, This represents the signal received by the k-th backscatter receiver in the second stage. This indicates the signal received by the primary user in the second phase. The transmit power of the k-th backscatter receiver in the second stage. This represents the signal transmitted by the k-th backscatter transmitter in the second stage. This represents the reflection coefficient matrix of the second-stage RIS; In the formula This represents the phase of the reflection coefficient of the nth element. .

3. The transmission method for joint backscatter communication and active communication in a symbiotic network as described in claim 2, characterized in that, The total throughput function for users and the rate function for the primary user in the two phases include: The total communication throughput function for users in the first phase is as follows: In the formula: This represents the signal-to-interference-plus-noise ratio (SINR) of the k-th backscatter receiver in the first stage. The rate function for the primary user in the first phase is as follows: ， In the formula This indicates the signal-to-interference-plus-noise ratio (SIR) of the primary user in the first stage; The total communication throughput function for users in the second phase is as follows: In the formula, This represents the signal-to-interference-plus-noise ratio (SINR) of the k-th backscatter receiver in the second stage. The rate function for the primary user in the second phase is as follows: In the formula, This indicates the signal-to-interference-plus-noise ratio (SIR) for the primary user in the second phase.

4. The transmission method for joint backscatter communication and active communication in a symbiotic network as described in claim 1, characterized in that, S401 includes: Given the remaining variables, i.e., fixed , , as well as In the case of defining , , The throughput expression in the objective function can be rewritten as follows: The rate expression for cellular users in the constraints is rewritten as follows: The throughput maximization problem model is transformed into the following P2 form: in , Using the geometric mean inequality method to apply constraints , The process involves obtaining convex conditions acceptable to the CVX toolbox, specifically as follows: Using mathematical relaxation and continuous convex approximation methods, auxiliary variables are introduced. The objective function is transformed into the following form; In the formula, , Indicates the first i At the Taylor expansion point of the next iteration, the non-convex constraints are transformed into convex ones, and the throughput maximization problem model is rewritten in the following P3 form: The rank-one constraint is relaxed using a semidefinite relaxation method. Problem P3 is transformed into a convex optimization problem. The CVX tool is used to solve the convex optimization problem, and a rank-one solution is obtained through Gaussian randomization. The updated value is then substituted into the convex optimization problem and iterated until convergence, finally yielding a suboptimal solution. and .

5. The transmission method for joint backscatter communication and active communication in a symbiotic network as described in claim 4, characterized in that, S402 includes: fixed and After the values ​​of these two variables, the objective function Regardless of variables, defined , The throughput maximization problem model is transformed into the following form: in P4 is a non-convex optimization problem. The following sections will discuss the non-convex objective function and constraints. Processing: For the objective function, by introducing auxiliary variables The objective function is transformed into: The objective function described above is in the form of a convexity, therefore the second part of the above equation... Its first-order Taylor expansion can be used as a substitute, as follows: Here, are variables. The value of the i-th iteration; Will This can be transformed into the following convex constraint condition: In the formula, It is an introduced auxiliary variable, and its optimal value At this point, the optimization problem P4 is transformed into the following convex problem: By iteratively solving problem P5, we obtain... suboptimal solution .

6. The transmission method for joint backscatter communication and active communication in a symbiotic network as described in claim 5, characterized in that, S403 includes: By definition , For the defined semi-definite relaxed transmit beamforming variables, , as well as These are auxiliary variables used to simplify expressions. The simplified optimization problem is as follows: By introducing random variables The objective function is transformed into: Substituting the above objective function into optimization problem P6, we obtain the following optimization problem P7: Solving optimization problem P7 using the semi-definite relaxation method yields the following results: The suboptimal solution.

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