Resource optimization distribution system and method of wireless energy transfer communication perception integrated network

Abstract

The invention discloses a resource optimization distribution system and method of a wireless energy transfer communication perception integrated network, and belongs to the technical field of wireless communication. The system comprises an intelligent metasurface RIS which is arranged between a base station and a terminal device; the base station is used for radar sensing of a plurality of device targets during a downlink period and wireless power transmission of a plurality of terminal devices with the assistance of the RIS; the plurality of terminal devices are used for transmitting data back to the base station by using the collected energy through a non-orthogonal multiple access strategy in an uplink; wherein joint optimization is carried out on an energy beam forming matrix, a radar beam forming matrix, RIS phase shift matrixes in a downlink and an uplink and transmission time slot distribution, so that the total achievable throughput of equipment in uplink transmission is maximized. According to the ISAC system based on the WPCN under the assistance of the RIS, a new solution is provided for wireless network construction, and the achievable throughput of the system can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a resource optimization allocation system and method for a wireless energy transmission, communication and perception integrated network. Background Art

[0002] With the growing demand for IoT applications and the explosive growth in the number of smart devices connected to wireless networks, spectrum has become a scarce resource. The great potential of integrated communication and sensing (ISAC) in improving spectrum efficiency makes it a promising technology in B5G and 6G wireless networks. ISAC is committed to using the same hardware and spectrum resources to perform the functions of communication and radar sensing at the same time, thereby significantly improving spectrum and hardware efficiency. In addition to the shortage of spectrum resources, the charging problem of terminal devices is also a severe challenge faced in the actual deployment of IoT networks. Data transmission between devices requires continuous power supply to maintain connection, and when devices are deployed on a large scale, wired charging or manual battery replacement may not be feasible. Wireless power transmission (WPT) technology based on RF energy harvesting has been used as a cost-effective alternative for battery replacement or charging. Existing RF energy harvesting research can be further divided into two main paradigms, namely synchronous wireless information and power transmission (SWIPT) and wireless powered communication network (WPCN). In low-power applications such as sensor devices, WPCN has obvious advantages in reducing operating costs and improving data transmission robustness. Therefore, this paper selects WPCN as a research paradigm for WPT. Considering that in actual communication networks, building obstruction will seriously affect the transmission efficiency of signals. The intelligent metasurface (RIS) composed of a series of passive reflective units can create a favorable wireless propagation environment. Integrating RIS into the system can effectively deal with the obstruction problem.

[0003] At present, the industry and academia have conducted extensive research on the combination of RIS or WPT technology with ISAC, but the following problems still exist: 1) At present, the research in related fields mainly focuses on RIS-assisted ISAC systems or ISAC systems supported by WPT technology, but few studies explore the feasibility and potential advantages of combining RIS, WPT and ISAC, and few studies involve the optimization performance of the entire system. Considering the potential advantages of ISAC and WPT in solving spectrum resource constraints and power requirements of energy-constrained devices, respectively, and the outstanding performance of RIS in dealing with building occlusion, the study of ISAC systems combining RIS and WPT is of great significance to the construction of future wireless networks. 2) At present, most of the research on RIS transforms the optimization problem of the RIS phase shift matrix into a semi-definite programming (SDP) problem through the semi-definite relaxation (SDR) technology, and then uses convex optimization tools to solve it. However, when the rank of the optimization result is not one, it may cause a significant drop in performance. In order to achieve better system performance, it is necessary to propose an optimization algorithm with better performance for the RIS phase shift matrix. Summary of the invention

[0004] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the purpose of the present invention is to provide a resource optimization allocation system and method for a wireless energy transmission, communication and perception integrated network.

[0005] The first technical solution adopted by the present invention is:

[0006] A resource optimization allocation system for a wireless energy transmission, communication and perception integrated network, comprising:

[0007] Intelligent metasurface RIS, deployed between the base station and the terminal device;

[0008] A base station for radar sensing of multiple device targets during downlink and wireless power transmission to multiple end devices with the assistance of RIS;

[0009] A plurality of terminal devices are used to transmit data back to a base station in an uplink using the harvested energy through a non-orthogonal multiple access strategy;

[0010] The energy beamforming matrix, radar beamforming matrix, RIS phase shift matrix in downlink and uplink, and transmission time slot allocation are jointly optimized to maximize the total achievable throughput of the equipment in uplink transmission.

[0011] Furthermore, the joint optimization step includes two stages:

[0012] In the first stage, the optimal energy and radar beamforming matrix and RIS phase shift matrix in the downlink are obtained according to the SCA penalty-based algorithm and the RCG algorithm, respectively, so as to maximize the total collection energy of the terminal device.

[0013] In the second stage, the polar point method and RCG penalty-based algorithm are used to obtain the optimal transmission time slot allocation and RIS phase shift matrix in the uplink, respectively, to maximize the total achievable throughput of the terminal equipment.

[0014] The second technical solution adopted by the present invention is:

[0015] A method for optimizing resource allocation in a wireless energy transmission, communication and perception integrated network comprises the following steps:

[0016] Design a communication perception integrated system assisted by RIS; the system includes a base station, RIS and multiple terminal devices;

[0017] Construct channel model, base station transmission signal model and total transmission time slot model according to the communication perception integrated system;

[0018] Determine the optimization problem based on the constructed model;

[0019] Based on the optimization problem, the energy beamforming matrix, radar beamforming matrix, RIS phase shift matrix in downlink and uplink, and transmission time slot allocation are jointly optimized to maximize the total throughput of terminal devices in uplink transmission.

[0020] Furthermore, the channel model is constructed as follows:

[0021] Assuming that all wireless power channels are affected by large-scale path loss and small-scale fading, and experience independent Rician fading, the channels between RIS and the base station and between RIS and the kth device are modeled as:

[0022]

[0023] Among them, L r,b and L r,k They are the path loss models between RIS and the base station and between RIS and the kth device respectively; and represents the Rice fading component.

[0024] Furthermore, the base station transmission signal model is constructed as follows:

[0025] The base station has the dual functions of wireless power transmission and radar sensing. The signal vector emitted by the base station is Including RF energy signal vector s c and radar detection signal vector s r , which respectively satisfy and

[0026] Assume that the RF energy signal is uncorrelated with the radar detection signal, that is, Among them, T represents transpose and H represents conjugate transpose;

[0027] Let W c and W r represent the energy beamforming matrix and the radar beamforming matrix respectively, then the composite signal transmitted by the base station is expressed as:

[0028] x=W c s c +W r s r

[0029] Among them, W c and W r satisfy P0 is the maximum transmit power of the base station.

[0030] Furthermore, the total transmission time slot model is constructed as follows:

[0031] The total transmission time slot is divided into two phases, namely wireless power transmission and radar sensing in the downlink transmission phase, and wireless information transmission in the uplink transmission phase; in the downlink transmission phase with a duration of α0T, the base station will sense multiple targets and send RF energy signals to the terminal devices with the assistance of RIS; in the uplink transmission phase with a duration of α1t, all terminal devices use the collected energy to transmit data to the base station; where T represents the total transmission time slot, α0 and α1 satisfy α0+α1≤1;

[0032] The first stage: The base station senses multiple targets and sends RF energy signals to the devices with the assistance of RIS. The energy signal received by the kth terminal device is:

[0033]

[0034] In the formula, is the equivalent channel from the base station to the kth terminal device under the assistance of RIS, Φ0 represents the RIS phase shift matrix in the downlink; W k,c W c The energy beamforming vector corresponding to the kth terminal device, s k,c Forc The RF energy signal vector corresponding to the kth terminal device; W k′,c W c The energy beamforming vector corresponding to the k′th terminal device, s k′,c For c where W corresponds to the RF energy signal vector of the k′th terminal device, K is the total number of terminal devices; m,r W r The radar beamforming vector corresponding to the mth target, s m,r For r The radar detection signal vector corresponding to the mth target, M is the total number of targets; n k represents the additive white Gaussian noise received by the kth terminal device;

[0035] Since RF energy harvesting is a nonlinear process, it will enter the saturation region when the received power increases to a certain value; therefore, the energy collected by the kth terminal device is expressed as:

[0036]

[0037] Where μ0 is the energy conversion efficiency in the linear state, p th Represents the battery saturation power of each terminal device, is the RF power received by the terminal device;

[0038] For radar sensing: In actual application scenarios, the radar echo received by the base station includes signals reflected from sensing targets and non-sensing targets, where non-sensing targets are regarded as echo interference sources; assuming there are J interference sources, the radar echo received by the base station can be expressed as:

[0039]

[0040] Among them, θ l and are the directions of the lth target and the jth interference source respectively; L r,λ is the round-trip path loss, μ λ is the complex reflection coefficient; n r is the additive white Gaussian noise received by the base station; a is the transmission time slot allocation;

[0041] The second stage: With the assistance of RIS, the terminal device uses the collected energy to transmit data back to the base station in the uplink using a non-orthogonal multiple access strategy; assuming that the kth device will eliminate the interference of devices with lower channel gain than itself, the total achievable throughput of K devices is expressed as:

[0042]

[0043] Where Φ1 represents the RIS phase shift matrix in the uplink; q k represents the transmission power of the kth device in the uplink; δ is the standard deviation of the additive white Gaussian noise.

[0044] Furthermore, determining the optimization problem according to the constructed model includes:

[0045] By jointly optimizing the energy beamforming matrix W c , radar beamforming matrix W r , RIS phase shift matrices Φ0 and Φ1 in the downlink and uplink, and the transmission time slot allocation a to maximize the total achievable throughput; the mathematical expression of the optimization problem is (P1):

[0046]

[0047] C3:|(Φ0) nn |=1,n=1,…,N,

[0048] C4:|(Φ1) nn |=1,n=1,…,N,

[0049] C5:α0+α1≤1,α0≥0,α1≥0.

[0050] Among them, constraint C1 guarantees the minimum perceived SINR; constraint C2 represents the total transmit power limit of the base station; constraints C3 and C4 represent the unit modulus limit on the RIS phase shift in downlink and uplink transmission respectively; constraint C5 is the total transmission time slot limit.

[0051] Furthermore, the energy beamforming matrix, the radar beamforming matrix, the RIS phase shift matrix in the downlink and uplink, and the transmission time slot allocation are jointly optimized, including:

[0052] In the first stage, the optimal energy and radar beamforming matrix and RIS phase shift matrix in the downlink are obtained according to the SCA penalty-based algorithm and the RCG algorithm, respectively, so as to maximize the total collection energy of the terminal device.

[0053] In the second stage, the polar point method and RCG penalty-based algorithm are used to obtain the optimal transmission time slot allocation and RIS phase shift matrix in the uplink, respectively, to maximize the total achievable throughput of the terminal equipment.

[0054] Furthermore, the first stage specifically includes:

[0055] In this stage, the goal is to maximize the total collection energy of the device in the downlink while satisfying the perception quality constraint by jointly optimizing the energy beamforming matrix, the radar beamforming matrix, and the RIS phase shift matrix in the downlink. The optimization problem in this stage is expressed as (P2):

[0056]

[0057] stC1,C2,C3.

[0058] Due to the non-convex constraints C1 and C3 and the coupled optimization parameters, the optimization problem (P2) is further decomposed into two sub-problems and solved by an iterative optimization algorithm:

[0059] 1) Optimize the energy beamforming matrix and the radar beamforming matrix: Under the premise of fixing the RIS phase shift matrix in the downlink, the optimization problem is expressed as (P3):

[0060]

[0061] Among them, constraint C6 is obtained by linear transformation of constraint C2;

[0062] 2) Optimize the RIS phase shift matrix in the downlink: Under the premise of fixing the beamforming matrix and the radar beamforming matrix, the optimization problem is expressed as (P4):

[0063]

[0064] stC3.

[0066] Furthermore, the second stage specifically includes:

[0067] Based on the optimization results of the energy beamforming matrix, radar beamforming matrix and RIS phase shift matrix obtained in stage 1, the optimization problem (P1) is simplified to (P5):

[0068]

[0069] stC4,C5,

[0070]

[0071] Among them, constraint C12 ensures that the throughput of each device is greater than a certain value, r min is the minimum throughput of each terminal device; since the constraints C4 and C12 are non-convex and the optimization parameters are coupled, the optimization problem (P5) is further decomposed into two sub-problems and solved by an iterative optimization algorithm:

[0072] 1) Optimize the transmission time slot allocation: Under the premise of fixing the RIS phase shift matrix in the uplink, the optimization problem is expressed as:

[0073]

[0074] stC12.

[0075] in, Since the objective function of this optimization problem is continuous and can be differentiated within α0∈[0,1), the conditional optimal value is obtained according to the extreme point method:

[0076] Considering the constraint C12, the optimal α0 and α1 should also satisfy:

[0077]

[0078] Therefore, the final optimization results of α0 and α1 are derived as follows:

[0079]

[0080] in, is the maximum downlink transmission time of the kth terminal device, is the minimum uplink transmission time of the kth terminal device;

[0081] 2) Optimize the RIS phase shift matrix in the uplink: Under the premise of fixed transmission time slot allocation, the optimization problem is expressed as (P6):

[0082]

[0083] stC4,C12.

[0084] In order to solve the non-convexity problem of constraint C12, we define and Then constraint C12 is equivalent to:

[0085]

[0086] in, as well as By changing the constraint C12 into a penalty term and adding it to the objective function of (P6), we get the optimization problem (P6)0:

[0087]

[0088] stC13:|z n |=1,n=1,…,N.

[0089] in, is the penalty factor; for a given The optimization problem (P6)0 is solved using the Riemann conjugate gradient algorithm (RCG algorithm).

[0090] The beneficial effects of the present invention are as follows: the present invention proposes a WPCN-based ISAC system framework assisted by RIS, in which the base station first performs radar perception of multiple targets during the downlink, and performs wireless power transmission to multiple energy-limited devices assisted by RIS, and then all devices use the collected energy in the uplink to transmit data back to the base station through a non-orthogonal multiple access strategy. Based on this framework, an efficient resource configuration method is designed to maximize the total achievable throughput of the device in the uplink transmission by jointly optimizing the energy beamforming matrix, the radar beamforming matrix, the RIS phase shift matrix in the downlink and uplink, and the transmission time slot allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0092] Figure 1 Schematic diagram of a transmission protocol in an embodiment of the present invention;

[0093] Figure 2 It is a schematic diagram of a radar transmission beam diagram when the present invention is adopted in an ISAC system based on WPCN assisted by RIS in an embodiment of the present invention;

[0094] Figure 3 It is a schematic diagram of the total energy harvested by all devices in the ISAC system based on WPCN assisted by RIS in an embodiment of the present invention when the present invention, the semi-positive definite programming algorithm solution and the fixed-point iterative algorithm solution are respectively adopted;

[0095] Figure 4 It is a schematic diagram of the sum of the rates of information sent by all devices in the ISAC system based on WPCN assisted by RIS in an embodiment of the present invention, respectively using the present invention, the semi-positive definite programming algorithm solution, and the fixed-point iterative algorithm solution. DETAILED DESCRIPTION

[0096] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0097] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0098] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0099] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0100] In order to overcome the building shading problem in the ISAC system combined with WPT technology and the equipment power supply problem faced by the RIS-assisted ISAC system, this paper selects WPCN in WPT technology as a research example and designs an ISAC system based on WPCN assisted by RIS. In order to solve the non-convex optimization problem of maximizing the total achievable throughput of the equipment in uplink transmission, a joint optimization algorithm for energy beamforming matrix, radar beamforming matrix, RIS phase shift matrix in downlink and uplink, and transmission time slot allocation is designed.

[0101] The technical solution of the present invention is explained in detail below in conjunction with the accompanying drawings and specific embodiments.

[0102] (1) System model

[0103] In the WPCN-based ISAC system assisted by RIS considered in this embodiment, K terminal devices (hereinafter referred to as devices) are evenly distributed in a circle with a radius ranging from 30 meters to 50 meters centered on the base station, and the RIS is about 21 meters away from the base station. The RIS with N reflective elements can be modeled as a uniform planar array, while the base station is an M-element uniform linear array. The base station is able to sense information such as the speed and position of the near-end target. However, due to the obstruction of the building, there is no direct communication between the base station and the device. Therefore, a RIS is deployed between the base station and the device to establish a wireless power transmission and wireless information transmission link. In order to extend the operating life of the device, each device is equipped with a dedicated rectifier to obtain energy from the RF energy signal emitted by the base station for subsequent data transmission.

[0104] a) Channel Modeling

[0105] Assume that all wireless power channels are affected by large-scale path loss and small-scale fading, and experience independent Rician fading. The channels between RIS and the base station and between RIS and the kth device are modeled as:

[0106]

[0107] Among them, L r,b and L r,k They are the path loss models between RIS and the base station and between RIS and the kth device respectively. and represents the Rician fading components, which satisfy:

[0108]

[0109] Among them, β∈{β b ,β k} represents the Rice coefficient of the corresponding channel, and its value is set to 2dB. and represents the line-of-sight (Los) component of the corresponding channel, where:

[0110]

[0111] represents the array steering vector, and denote the azimuth and elevation of the line-of-sight (Los) path associated with RIS, respectively, is the departure angle relative to the base station. G NLOS and g NLOS They represent the non-line-of-sight (NLoS) components of the corresponding channels, and each element is an independent and identically distributed circularly symmetric complex Gaussian random variable with mean zero and variance 1.

[0112] b) Base station transmission signal model

[0113] In order to achieve the dual functions of wireless power transmission and radar sensing, the base station transmits a signal vector This includes the RF energy signal vector s c =[s 1,c ,…,s K,c ] T and radar detection signal vector s r =[s 1,r ,…,s M,r ] T , which respectively satisfy and In addition, in order to reduce the interference between signals, it is assumed that the RF energy signal is uncorrelated with the radar detection signal, that is, Let W c =[w 1,c ,…,w K,c ]∈C M×K and W r =[w 1,r ,…,w M,r ]∈C M×M They represent the energy beamforming matrix and the radar beamforming matrix respectively. Then, the composite signal transmitted by the base station can be expressed as:

[0114] x=W c s c +W r s r (5)

[0115] Where W c and W r satisfy P is the maximum transmit power of the base station.

[0116] c) Total transmission time slot model

[0117] like Figure 1 As shown, the total transmission time slot is divided into two stages, namely wireless power transmission and radar sensing in the downlink transmission stage and wireless information transmission in the uplink transmission stage. Specifically, in the downlink transmission stage with a duration of α0T, the base station will sense multiple targets and send RF energy signals to the devices with the assistance of RIS. Then, in the uplink transmission stage with a duration of α1T, all devices use the collected energy to transmit data to the base station. Where T represents the total transmission time slot, α0 and α1 satisfy α0+α1≤1. Specifically:

[0118] The first stage: The base station will sense multiple targets and send RF energy signals to the devices with the assistance of RIS. The energy signal received by the kth device can be written as:

[0119]

[0120] in, is the equivalent channel from the base station to the kth device under the assistance of RIS. Φ0 represents the RIS phase shift matrix in the downlink, which can be expressed as in and The present invention focuses on the influence of RIS phase shift on system performance, so is 1. k ~CN(0,δ 2 ) represents the additive white Gaussian noise (AWGN) received by the kth device. Since RF energy harvesting is a nonlinear process, it will enter the saturation region when the received power increases to a certain value. Therefore, the energy collected by the kth device can be expressed as:

[0121]

[0122] Where μ0 is the energy conversion efficiency in the linear state, p th Represents the battery saturation power of each device, is the RF power received by the device, expressed as:

[0123]

[0124] For radar sensing: In actual application scenarios, the radar echo received by the base station includes signals reflected from sensing targets and non-sensing targets, the latter of which are regarded as echo interference sources. Assuming there are J interference sources, the radar echo received by the base station can be expressed as:

[0125]

[0126] Among them, θ l and are the directions of the lth target and the jth interference source respectively. This includes the round-trip path loss L r,λ and the complex reflection coefficient μ λ .n r ~CN(0,δ 2 I N ) is the additive white Gaussian noise (AWGN) received by the base station. The perceived signal-to-interference-and-noise ratio (SINR) of the lth target received by the base station can be expressed as:

[0127]

[0128] Among them, H l =λ l a(θ l)a(θ l ) H and Since the perceived SINR determines the perceived detection and positioning performance, it is necessary to ensure that the perceived SINR is greater than the threshold, that is,

[0129] The second stage: With the assistance of RIS, the device uses the collected energy to transmit the data back to the base station in the uplink using a non-orthogonal multiple access strategy. Assume that the equivalent channel gain satisfies |G H Φ1g r,1 | 2 ≥…≥|G H Φ1g r,K | 2 , then the kth device will eliminate the interference of devices whose channel gain is lower than its own. At this time, the total achievable throughput of K devices is expressed as:

[0130]

[0131] in, represents the RIS phase shift matrix in the uplink, and Respectively represent the phase shift and amplitude reflection coefficient of the nth reflection element during the duration of wireless information transmission. Same, set is 1. k represents the transmit power of the kth device in the uplink. Assuming that the processing cost of the device, such as the energy consumption of generating and encoding information, is negligible, the transmit power of the kth device can be expressed as:

[0132] q k =E k / α1T (12)

[0133] (2) Optimization Problem

[0134] After completing the above definition, this embodiment aims to optimize the energy beamforming matrix W by joint optimization. c , radar beamforming matrix W r , RIS phase shift matrices Φ0 and Φ1 in the downlink and uplink, and the transmission time slot allocation α to maximize the total achievable throughput. The mathematical expression of the optimization problem is (P1):

[0135]

[0136] C3:|(Φ0) nn |=1,n=1,…,N,

[0137] C4:|(Φ1) nn |=1,n=1,…,N,

[0138] C5:α0+α1≤1,α0≥0,α1≥0. (13)

[0139] Constraint C1 guarantees the minimum perceived SINR. Constraint C2 represents the total transmit power limit of the base station. Constraints C3 and C4 represent unit modulus limits on the RIS phase shift in downlink and uplink transmissions, respectively. Constraint C5 is the total transmission time slot limit.

[0140] (3) Joint resource optimization allocation method

[0141] In the optimization problem (P1) under consideration, there are five parameters to be optimized: energy beamforming matrix, radar beamforming matrix, RIS phase shift matrix in downlink and uplink, and transmission time slot allocation. Among them, since C1, C3 and C4 are non-convex constraints, and the optimization parameters are highly coupled, in order to effectively obtain these parameters, an embodiment of the present invention proposes a two-stage optimization method. In stage one, according to the penalty-based continuous convex approximation algorithm (SCA penalty-based algorithm) and the Riemann conjugate gradient algorithm (RCG algorithm), the optimal energy and radar beamforming matrix and RIS phase shift matrix in the downlink are obtained respectively, so as to maximize the total collection energy of the device; in stage two, through the polar point method (polar point method) and the penalty-based Riemann conjugate gradient algorithm (RCG penalty-based algorithm), the optimal transmission time slot allocation and RIS phase shift matrix in the uplink are obtained respectively to maximize the total achievable throughput of the device. The specific process is as follows:

[0142] a) Phase 1: Maximizing the total harvested energy of the device

[0143] In this stage, the goal is to maximize the total harvested energy of the device in the downlink while satisfying the perception quality constraints by jointly optimizing the energy beamforming matrix, the radar beamforming matrix, and the RIS phase shift matrix in the downlink. Since the transmission time slot allocation is fixed, this problem can be equivalent to maximizing the total harvested RF power. Therefore, the optimization problem in this stage can be expressed as (P2):

[0144]

[0145] stC1,C2,C3.(14)

[0146] Due to the non-convex constraints C1 and C3 and the coupled optimization parameters, the optimization problem (P2) can be further decomposed into two sub-problems and solved by an iterative optimization algorithm. Specifically, the following steps are performed:

[0147] Optimize the energy beamforming matrix and radar beamforming matrix: Under the premise of fixing the RIS phase shift matrix in the downlink, the optimization problem can be expressed as (P3):

[0148]

[0149] Among them, constraint C6 can be obtained by linearly transforming constraint C2. In order to solve the non-convexity problem of constraint C1, it is transformed as follows:

[0150]

[0151] definition and Then expression (16) can be simplified as:

[0152]

[0153] Similarly, the objective function of the optimization problem (P3) can be rewritten as:

[0154]

[0155] in, and Therefore, the optimization problem (P3) can be reformulated as (P3)0:

[0156]

[0157] The optimization problem (P3)0 can be transformed into a convex semidefinite programming (SDP) problem by using the semidefinite relaxation (SDR) technique, and then solved using convex optimization tools such as CVX. However, the semidefinite relaxation (SDR) technique ignores the rank-one constraints during the solution process, which may result in a significant performance degradation. In order to solve this problem, the present invention converts the rank-one constraints C9 and C10 into penalty terms, and then restates the objective function of the optimization problem (P3)0 as follows:

[0158]

[0159] Among them, κ>0 is the punishment factor, M e (·) represents the maximum eigenvalue of the matrix. However, and The objective function is made non-convex and can be transformed into a convex function using the continuous convex approximation (SCA) algorithm. and The first-order Taylor expansion at the nth iteration is, and The upper bounds of can be expressed as:

[0160]

[0161] in, and They are and The eigenvector corresponding to the maximum eigenvalue. Finally, the optimization problem (P3)0 can be written as (P3)1:

[0162]

[0163] stC7,C8.(23)

[0164] Problem (P3) 1 is a quadratic semidefinite programming (QSDP) problem, which can be solved by CVX. After the algorithm converges, the optimized w can be obtained according to the eigenvalue decomposition (EVD) k,c and w m,r , which is expressed as follows:

[0165]

[0166] Optimize the RIS phase shift matrix in the downlink: Under the premise of fixing the beamforming matrix and the radar beamforming matrix, the optimization problem can be expressed as (P4):

[0167]

[0168] stC3.(26)

[0169] In order to deal with the non-convex constraint C3, the present invention defines the unit modulus constraint as a Riemann manifold and applies manifold optimization techniques, such as the Riemann conjugate gradient algorithm (RCG algorithm), to solve the problem (P4).

[0170] First, transform the optimization problem (P4) into (P4)0:

[0171]

[0172] st C11:|y n |=1,n=1,…,N. (27)

[0173] in, and Then, the specific steps of the RCG algorithm are:

[0174] Step 1: Define the manifold. Unit norm constraint |y n |=1,n∈{1,…,N},form a manifold Every point on it satisfies constraint C11.

[0175] Step 2: Define the tangent space. For the manifold The point set y of the previous k-th iteration k , tangent space From the tangential direction through y k Each tangent vector in this space represents the tangent vector from y k The direction to move towards the optimal solution. Up k The tangent space at is represented as:

[0176]

[0177] Step 3: Find the search direction. After defining the tangent space, the tangent vector (direction) where the objective function increases the steepest is called the Riemann gradient, which is expressed as the Euclidean gradient in the tangent space. Orthogonal projection on y k The Riemann gradient calculation formula at is:

[0178]

[0179] in, Represents y k With the Riemann gradient, we can apply the conjugate gradient method used to determine the search direction in Euclidean space to the manifold space to find the search direction. In Euclidean space, the conjugate gradient method satisfies:

[0180]

[0181] Among them, η k Yes k The search direction at k-1 is the Polak-Ribiere parameter. However, in manifold space, η k and η k-1 Located in two different tangent spaces and In the example above, they cannot be directly converted to each other. To solve this problem, a conversion operation is required, which is defined as:

[0182]

[0183] The search direction can be changed from the original tangent space Mapping to current tangent space Based on (30) and (31), the update rule of the search direction in the manifold space can be modeled as:

[0184]

[0185] Step 4: Perform retraction. The updated points may leave the previous manifold space, so a retraction is required. The operation maps points from tangent space back to manifold space and is defined as:

[0186]

[0187] Among them, l k is the search step size of Armijo backtracking.

[0188] According to the above steps, the RCG algorithm is executed until ||grad f(y k )||≤10 -6 .

[0189] Iterative optimization: The energy beamforming matrix, radar beamforming matrix, and RIS phase shift matrix in the downlink are iteratively optimized by combining the SCA penalty-based algorithm and the RCG algorithm until the target value of the optimization problem (P2) converges.

[0190] b) Phase 2: Maximizing the total achievable throughput of the device

[0191] Based on the optimization results of the energy beamforming matrix, radar beamforming matrix and RIS phase shift matrix obtained in stage 1, the optimization problem (P1) can be simplified to (P5):

[0192]

[0193] stC4,C5,

[0194]

[0195] Among them, constraint C12 ensures that the throughput of each device is greater than a certain value, r min is the minimum throughput of each device. Since the constraints C4 and C12 are non-convex and the optimization parameters are coupled, the present invention further decomposes the optimization problem (P5) into two sub-problems and solves them through an iterative optimization algorithm. The specific steps are:

[0196] Optimizing transmission time slot allocation: Under the premise of fixing the RIS phase shift matrix in the uplink, the optimization problem can be expressed as:

[0197]

[0198] stC12.(35)

[0199] in, Since the objective function of this optimization problem is continuous and differentiable within α0∈[0,1), the conditional optimal value can be obtained using the polar point method: That is, through It turns out that:

[0200]

[0201] in, W(·) means that for a complex number x, x=W(x)e W(x) Lambert-W function. At the same time, considering the constraint C12, the optimal α0 and α1 should also satisfy:

[0202]

[0203] Therefore, the final optimization results of α0 and α1 can be derived as follows:

[0204]

[0205] Optimize the RIS phase shift matrix in the uplink: Under the premise of fixed transmission time slot allocation, the optimization problem can be expressed as (P6):

[0206]

[0207] stC4,C12.(40)

[0208] In order to solve the non-convexity problem of constraint C12, we define and Then constraint C12 is equivalent to:

[0209]

[0210] in, as well as Based on expression (41), the present invention converts constraint condition C12 into a penalty term and adds it to the objective function of (P6), thereby obtaining the optimization problem (P6)0:

[0211]

[0212] st C13:|z n |=1,n=1,…,N. (42)

[0213] in, is the penalty factor. The optimization problem (P6)0 can be solved using the Riemann conjugate gradient algorithm (RCG algorithm). Then, by Gradually reduce when Small enough and The whole solution process ends when .

[0214] Iterative optimization: The polar point method and the RCG penalty-based algorithm are combined to iteratively optimize the transmission time slot allocation and the RIS phase shift matrix in the uplink until the target value of the optimization problem (P5) converges.

[0215] (4) Simulation Experiment Results

[0216] In this embodiment, simulation analysis verifies the effectiveness of the proposed two-stage resource optimization configuration method in improving the achievable throughput of the system, and compares it with the semi-definite programming algorithm and the fixed-point iterative algorithm. In the semi-definite programming algorithm, the optimization of the energy beamforming matrix and the radar beamforming matrix is ​​to convert the corresponding optimization problem into a semi-definite programming problem (SDP) through semi-definite relaxation (SDR), and then solve it, while the optimization of other parameters adopts the method mentioned in the present invention. In the fixed-point iterative algorithm, the optimization of the RIS phase shift matrix in the downlink and uplink adopts the fixed-point iterative algorithm, and the optimization of other parameters adopts the method mentioned in the present invention.

[0217] The relevant parameters of the simulation experiment are set as follows: the total number of devices is 4, the number of RF chains is 4, and the base station is equipped with 8 antenna arrays. The RIS is a uniform planar array composed of 32 elements. The number of interference sources is 12, the energy conversion efficiency is 0.5, and the device battery saturation power is 0.1W.

[0218] Figure 2 The state of the system radar transmission beam pattern in the angle space when the resource optimization allocation scheme proposed in the present invention is adopted in the ISAC system based on WPCN assisted by the RIS under consideration is shown. When the number of targets is 3, the targets are located in the direction of {-60°, 0°, 60°}; when the number of targets is 6, the targets are located in the direction of {-70°, -40°, -10°, 10°, 40°, 70°}. The radar transmission beam pattern is defined as: It represents the radar detection signal gain of the base station in the direction θ. It can be seen that when the number of targets is 3 and 6, the beam reaches the main peak in the target direction. The results show that the radar beam obtained by the optimization algorithm proposed in the present invention can produce satisfactory perception performance while suppressing interference in the clutter direction.

[0219] Figure 3The total energy harvested by all devices when the resource optimization allocation scheme proposed by the present invention is adopted in the ISAC system based on WPCN under the assistance of RIS is shown, and compared with the scheme based on semi-positive programming algorithm and the scheme based on fixed-point iteration algorithm. It can be seen from the simulation diagram that the total energy harvested by all devices under the two-stage resource optimization configuration scheme proposed by the present invention is significantly better than the other two schemes, which shows that resource optimization allocation is crucial to improving the energy harvesting performance of devices.

[0220] Figure 4 The paper shows the sum of the rates at which the base station receives information sent by all devices when the resource optimization allocation scheme proposed by the present invention is adopted in the ISAC system based on WPCN under the assistance of RIS, and compares it with the scheme based on semi-positive programming algorithm and the scheme based on fixed-point iteration algorithm. It can be seen from the simulation diagram that the total rate of information sent by the device under the two-stage resource optimization configuration scheme proposed by the present invention is significantly better than the other two schemes, which shows that resource optimization allocation is crucial to improving the total rate of information sent by the device.

[0221] (5) Advantages and beneficial effects

[0222] At present, the research in related fields mainly focuses on ISAC systems assisted by RIS or ISAC systems supported by WPT technology, but there are few studies on the feasibility and potential advantages of combining RIS, WPT and ISAC, and few studies involve the optimization performance of the entire system. At the same time, most of the current research on RIS converts the optimization problem of the RIS phase shift matrix into a semi-definite programming (SDP) problem through semi-definite relaxation (SDR) technology, and then uses convex optimization tools to solve it. However, when the rank of the optimization result is not one, it may cause a significant decline in performance. Taking into account the research status of the prior art, the present invention proposes a RIS-assisted WPCN-based ISAC system framework and a corresponding resource configuration method. In general, the present invention has the following advantages:

[0223] 1) The present invention proposes an ISAC system based on WPCN assisted by RIS, which provides a new solution for the construction of future wireless networks and conducts research on the optimization performance of the system.

[0224] 2) In order to avoid the performance loss of the semi-definite programming (SDP) problem when the rank of the optimization result is not one, the present invention proposes a Riemann conjugate gradient (RCG) algorithm, which can obtain a better RIS phase shift result through iterative calculation.

[0225] 3) On the basis of the proposed system, in order to improve the total achievable throughput of the system, the present invention proposes a method for jointly optimizing the energy beamforming matrix, radar beamforming matrix, RIS phase shift matrix in the downlink and uplink, and transmission time slot allocation while ensuring the radar perception performance, which can effectively improve the achievable throughput performance of the system.

[0226] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0227] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0228] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A resource optimization allocation system for a wireless energy transmission, communication and perception integrated network, characterized in that: include: Intelligent metasurface RIS, deployed between the base station and the terminal device; A base station for radar sensing of multiple device targets during downlink and wireless power transmission to multiple end devices with the assistance of RIS; A plurality of terminal devices are used to transmit data back to a base station in an uplink using the harvested energy through a non-orthogonal multiple access strategy; The energy beamforming matrix, radar beamforming matrix, RIS phase shift matrix in downlink and uplink, and transmission time slot allocation are jointly optimized to maximize the total achievable throughput of the equipment in uplink transmission.

2. According to claim 1, a resource optimization allocation system for a wireless energy transmission, communication and perception integrated network is characterized in that: The joint optimization process consists of two stages: In the first stage, the optimal energy and radar beamforming matrix and RIS phase shift matrix in the downlink are obtained according to the continuous convex approximation algorithm based on punishment and the Riemann conjugate gradient algorithm, respectively, so as to maximize the total collection energy of the terminal equipment; in the second stage, the optimal transmission time slot allocation and RIS phase shift matrix in the uplink are obtained by the pole method and the Riemann conjugate gradient algorithm based on punishment, respectively, to maximize the total achievable throughput of the terminal equipment.

3. A method for optimizing resource allocation in a wireless energy transmission, communication and perception integrated network, characterized in that: The following steps are involved: Design a communication and perception integrated system assisted by RIS; The system includes a base station, a RIS and multiple terminal devices; Construct channel model, base station transmission signal model and total transmission time slot model according to the communication perception integrated system; Determine the optimization problem based on the constructed model; Based on the optimization problem, the energy beamforming matrix, radar beamforming matrix, RIS phase shift matrix in downlink and uplink, and transmission time slot allocation are jointly optimized to maximize the total throughput of terminal devices in uplink transmission.

4. The method for optimizing resource allocation of a wireless energy transmission, communication and perception integrated network according to claim 3 is characterized in that: The channel model is constructed as follows: Assuming that all wireless power channels are affected by large-scale path loss and small-scale fading, and experience independent Rician fading, the channels between RIS and the base station and between RIS and the kth device are modeled as: Among them, L r,b and L r,k They are the path loss models between RIS and the base station and between RIS and the kth device respectively; and represents the Rice fading component.

5. The method for optimizing resource allocation of a wireless energy transmission, communication and perception integrated network according to claim 3 is characterized in that: The base station transmission signal model is constructed as follows: The base station has the dual functions of wireless power transmission and radar sensing. The signal vector emitted by the base station is Including RF energy signal vector s c and radar detection signal vector s r ; Assume that the RF energy signal is uncorrelated with the radar detection signal, that is, Among them, T represents transpose and H represents conjugate transpose; Let W c and W r represent the energy beamforming matrix and the radar beamforming matrix respectively, then the composite signal transmitted by the base station is expressed as: x=W c s c +W r s r Among them, W c and W r satisfy P0 is the maximum transmit power of the base station.

6. The method for optimizing resource allocation of a wireless energy transmission, communication and perception integrated network according to claim 3, characterized in that: The total transmission time slot model is constructed as follows: The total transmission time slot is divided into two phases, namely wireless power transmission and radar sensing in the downlink transmission phase, and wireless information transmission in the uplink transmission phase; in the downlink transmission phase with a duration of α0t, the base station will sense multiple targets and send RF energy signals to the terminal device with the assistance of RIS; In the uplink transmission phase with a duration of α1t, all terminal devices use the collected energy to transmit data to the base station; where t represents the total transmission time slot, and α0 and α1 satisfy α0+α1≤1; the first stage: the base station senses multiple targets and sends RF energy signals to the devices with the assistance of RIS; the energy signal received by the kth terminal device is: In the formula, is the equivalent channel from the base station to the kth terminal device under the assistance of RIS, Φ0 represents the RIS phase shift matrix in the downlink; W k,c W c The energy beamforming vector corresponding to the kth terminal device, s k,c For c The RF energy signal vector corresponding to the kth terminal device; W k′,c W c The energy beamforming vector corresponding to the k′th terminal device, s k′,c For c where W corresponds to the RF energy signal vector of the k′th terminal device, K is the total number of terminal devices; m,r W r The radar beamforming vector corresponding to the mth target, s m,r For r The radar detection signal vector corresponding to the mth target, M is the total number of targets; n k represents the additive white Gaussian noise received by the kth terminal device; Since RF energy harvesting is a nonlinear process, it will enter the saturation region when the received power increases to a certain value; therefore, the energy collected by the kth terminal device is expressed as: Where μ0 is the energy conversion efficiency in the linear state, p th Represents the battery saturation power of each terminal device, is the RF power received by the terminal device; For radar sensing: the radar echo received by the base station includes signals reflected from sensing targets and non-sensing targets, where non-sensing targets are regarded as echo interference sources; assuming there are J interference sources, the radar echo received by the base station can be expressed as: Among them, θ l and are the directions of the lth target and the jth interference source respectively; L r,λ is the round-trip path loss, μ λ is the complex reflection coefficient; n r is the additive white Gaussian noise received by the base station; a is the transmission time slot allocation; the second stage: with the assistance of RIS, the terminal device uses the collected energy to transmit the data back to the base station in the uplink using a non-orthogonal multiple access strategy; assuming that the kth device will eliminate the interference of devices with lower channel gain than itself, the total achievable throughput of K devices is expressed as: Where Φ1 represents the RIS phase shift matrix in the uplink; q k represents the transmission power of the kth device in the uplink; δ is the standard deviation of the additive white Gaussian noise.

7. The method for optimizing resource allocation of a wireless energy transmission, communication and perception integrated network according to claim 3, characterized in that: Determining the optimization problem according to the constructed model includes: By jointly optimizing the energy beamforming matrix W c , radar beamforming matrix W r , RIS phase shift matrices Φ0 and Φ1 in the downlink and uplink, and the transmission time slot allocation a to maximize the total achievable throughput; the mathematical expression of the optimization problem is (P1): C3:|(Φ0) nn |=1,n=1,…,N, C4:|(Φ1) nn |=1,n=1,…,N, C5:α0+α1≤1,α0≥0,α1≥0. Among them, constraint C1 guarantees the minimum perceived SINR; constraint C2 represents the total transmit power limit of the base station; constraints C3 and C4 represent the unit modulus limit on the RIS phase shift in downlink and uplink transmission respectively; constraint C5 is the total transmission time slot limit.

8. The method for optimizing resource allocation of a wireless energy transmission, communication and perception integrated network according to claim 7, characterized in that: The joint optimization of the energy beamforming matrix, the radar beamforming matrix, the RIS phase shift matrix in the downlink and uplink, and the transmission time slot allocation includes: In the first stage, the optimal energy and radar beamforming matrix and RIS phase shift matrix in the downlink are obtained according to the continuous convex approximation algorithm based on punishment and the Riemann conjugate gradient algorithm, respectively, so as to maximize the total collection energy of the terminal equipment; in the second stage, the optimal transmission time slot allocation and RIS phase shift matrix in the uplink are obtained by the pole method and the Riemann conjugate gradient algorithm based on punishment, respectively, to maximize the total achievable throughput of the terminal equipment.

9. The method for optimizing resource allocation of a wireless energy transmission, communication and perception integrated network according to claim 8, characterized in that: The first stage specifically includes: In this stage, the goal is to maximize the total collection energy of the device in the downlink while satisfying the perception quality constraint by jointly optimizing the energy beamforming matrix, the radar beamforming matrix, and the RIS phase shift matrix in the downlink. The optimization problem in this stage is expressed as (P2): stC1,C2,C3. Due to the non-convex constraints C1 and C3 and the coupled optimization parameters, the optimization problem (P2) is further decomposed into two sub-problems and solved by an iterative optimization algorithm: 1) Optimize the energy beamforming matrix and the radar beamforming matrix: Under the premise of fixing the RIS phase shift matrix in the downlink, the optimization problem is expressed as (P3): Among them, constraint condition C6 is obtained by linear transformation of constraint condition C2; 2) Optimize the RIS phase shift matrix in the downlink: Under the premise of fixing the beamforming matrix and the radar beamforming matrix, the optimization problem is expressed as (P4):

10. The method for optimizing resource allocation of a wireless energy transmission, communication and perception integrated network according to claim 9, characterized in that: The second stage specifically includes: Based on the optimization results of the energy beamforming matrix, radar beamforming matrix and RIS phase shift matrix obtained in stage 1, the optimization problem (P1) is simplified to (P5): stC4,C5, Among them, constraint C12 ensures that the throughput of each device is greater than a certain value, r min is the minimum throughput of each terminal device; since the constraints C4 and C12 are non-convex and the optimization parameters are coupled, the optimization problem (P5) is further decomposed into two sub-problems and solved by an iterative optimization algorithm: 1) Optimize the transmission time slot allocation: Under the premise of fixing the RIS phase shift matrix in the uplink, the optimization problem is expressed as: stC12. in, Since the objective function of this optimization problem is continuous and can be differentiated within α0∈[0,1), the conditional optimal value is obtained according to the extreme point method: Considering the constraint C12, the optimal α0 and α1 should also satisfy: Therefore, the final optimization results of α0 and α1 are derived as follows: in, is the maximum downlink transmission time of the kth terminal device, is the minimum uplink transmission time of the kth terminal device; 2) Optimize the RIS phase shift matrix in the uplink: Under the premise of fixed transmission time slot allocation, the optimization problem is expressed as (P6): stC4,C12. In order to solve the non-convexity problem of constraint C12, we define and Then constraint C12 is equivalent to: in, as well as By changing the constraint C12 into a penalty term and adding it to the objective function of (P6), we get the optimization problem (P6)0: s.t.C13:|z n |=1,n=1,…,N. in, is the penalty factor; for a given The optimization problem (P6)0 is solved using the Riemann conjugate gradient algorithm.

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