RIS-assisted millimeter wave wireless energy transmission network resource optimization system and method

Abstract

The present invention discloses a resource optimization system and method for a RIS-assisted millimeter-wave wireless energy transmission network, belonging to the field of wireless communication technology. The system includes: a base station for transmitting a radio frequency energy signal during downlink transmission; an intelligent metasurface (RIS) for reflecting the radio frequency energy signal so that a terminal device receives the radio frequency energy signal and obtains energy; a plurality of terminal devices, wherein the plurality of terminal devices are divided into several groups, and the terminal devices in each group are configured to use the received energy to transmit data to a data receiver using a non-orthogonal multiple access strategy during uplink transmission; and a data receiver for receiving data uploaded by each terminal device. The system jointly optimizes the device grouping, base station transmit power allocation, energy beamforming, and RIS phase shift matrix in downlink transmission, and the device transmit power allocation, RIS phase shift matrix, and transmission time slot allocation in uplink transmission to improve the total achievable throughput of the terminal device in uplink transmission.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a resource optimization system and method for a RIS-assisted millimeter wave wireless energy transmission network. Background Art

[0002] Wireless power communication networks (WPCNs) involve two steps: downlink energy transmission and uplink information transmission. Devices use the energy harvested during downlink transmission to transmit uplink information. However, with the rapid increase in the number of devices, spectrum resource shortages have become a major challenge for WPCNs. mmWave, with its abundant spectrum resources, has been proposed as a key candidate technology to address this issue.

[0003] To overcome spectrum resource shortages and building shadowing issues in WPCN, academic research has extensively focused on WPCN systems based on the mmWave band and WPCN systems assisted by intelligent metasurfaces (RIS). However, current research on WPCN systems primarily focuses on applying mmWave to WPCN systems or using RIS to enhance WPCN transmission performance. However, the combination of mmWave and WPCN faces challenges such as low energy transmission efficiency and limited coverage, while RIS-assisted WPCN systems are constrained by limited spectrum resources. Therefore, it is necessary to consider combining WPCN, mmWave, and RIS. Furthermore, the transmission architecture considered in existing WPCN systems mostly involves hybrid access points (APs) transmitting RF energy signals and receiving information signals in the downlink and uplink, respectively. In this architecture, signal interference and network congestion can occur when hybrid APs transmit and receive signals. Furthermore, current improvements in throughput for RIS-assisted WPCN systems primarily rely on optimizing RIS phase shifts, transmission slot allocation, and active beamforming, with little focus on optimizing device grouping. Summary of the Invention

[0004] In order to at least to some extent solve one of the technical problems existing in the prior art, the object of the present invention is to provide a resource optimization system and method for a RIS-assisted millimeter wave wireless energy transmission network.

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

[0006] A resource optimization system for a RIS-assisted millimeter wave wireless energy transmission network, comprising:

[0007] a base station for transmitting radio frequency energy signals during downlink transmission;

[0008] Intelligent metasurface RIS, used to reflect radio frequency energy signals so that terminal devices can obtain energy after receiving the radio frequency energy signals;

[0009] a plurality of terminal devices, wherein the plurality of terminal devices are divided into a plurality of groups, and the terminal devices in each group are configured to utilize the received energy to transmit data to a data receiver using a non-orthogonal multiple access strategy during an uplink transmission period;

[0010] A data receiver, used to receive data uploaded by each terminal device;

[0011] Among them, the device grouping, base station transmit power allocation, energy beamforming and RIS phase shift matrix in downlink transmission, as well as the device transmit power allocation, RIS phase shift matrix and transmission time slot allocation in uplink transmission are jointly optimized to improve the total achievable throughput of terminal devices in uplink transmission.

[0012] Furthermore, the joint optimization step includes two stages:

[0013] In the first stage, based on the channel correlation of terminal devices, an algorithm combining Kmeans++ clustering algorithm and exclusion theory is used to obtain optimized device grouping;

[0014] In phase two, based on the device grouping information obtained in phase one, an iterative optimization algorithm is used to obtain the optimal values ​​of the base station's transmit power allocation, energy beamforming, and the RIS phase shift matrix in downlink transmission, thereby maximizing the total collected energy of all devices. The total achievable throughput of the devices is then maximized by iteratively optimizing the devices' transmit power allocation, transmission time slot allocation, and the RIS phase shift matrix in uplink transmission.

[0015] The second technical solution adopted by the present invention is:

[0016] A resource optimization method for a RIS-assisted millimeter wave wireless energy transmission network includes the following steps:

[0017] Design a RIS-assisted mmWave band WPCN system, which includes a base station, RIS, a data receiver, and multiple terminal devices.

[0018] Based on the mmWave frequency band WPCN system, mmWave channel modeling, energy collection model in downlink transmission, and information transmission model in uplink transmission are constructed;

[0019] Determine the energy harvesting optimization problem based on the constructed model;

[0020] Based on the optimization problem, the device grouping, base station transmit power allocation, energy beamforming and RIS phase shift matrix in downlink transmission, as well as the device transmit power allocation, RIS phase shift matrix and transmission time slot allocation in uplink transmission are jointly optimized to improve the total achievable throughput of terminal devices in uplink transmission.

[0021] Furthermore, the mmWave channel modeling is constructed as follows:

[0022] The mmWave channel is characterized by a line-of-sight (LoS) link and several non-line-of-sight (NLoS) links. The mmWave channel between the kth device in the mth group and the base station or RIS is modeled as:

[0023]

[0024] Where a() and a′() represent array steering vectors; θ m,k 、φ m,k (β m,k )and denote the departure angle and azimuth (elevation) of the line-of-sight link and the lth non-line-of-sight link associated with the kth device in the mth group, respectively; γ g,k,m , γ r,k,m and They represent the line-of-sight of the mth group of corresponding RF links and the gain of the lth non-line-of-sight complex path respectively; d g,k,m d r,k,m represents the Euclidean distance from the base station or RIS to the kth device in the mth group, and α is the path loss exponent;

[0025] The channel matrix between the base station and RIS is modeled as:

[0026]

[0027] Among them, φ(β) and φ l (β l ) are the azimuth (elevation) angles of the line-of-sight link and the first non-line-of-sight link associated with the RIS, θ and θ l are the departure angles associated with the base station, and d represents the Euclidean distance from the base station to the RIS.

[0028] Furthermore, the energy harvesting model in the downlink transmission is constructed as follows:

[0029] The base station sends radio frequency energy signals to K devices in the downlink transmission time slot α0T; therefore, the energy signal received by the kth device in the mth group is written as:

[0030]

[0031] Among them, W m , W m′ denote the energy beamforming vectors pointing to the mth group and the m′th group respectively; is the equivalent channel from the base station to the kth device in the mth group, Φ0 represents the RIS phase shift matrix in the downlink, G represents the channel matrix between the base station and the RIS, and H represents the conjugate transpose; u m′,k′ is the scheduling variable of the k′th device; nk represents the additive white Gaussian noise received by the kth device; s k 、s k′ are the RF energy signal vectors of the kth and k′th devices respectively; p m 、p m′ are the powers of the mth and m′th RF links respectively;

[0032] The energy collected by the kth device in the mth group is expressed as:

[0033]

[0034] Among them, η0 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.

[0035] Furthermore, the information transmission model in the uplink transmission is constructed as follows:

[0036] With the assistance of RIS, the terminal devices use the collected energy to upload their own data using a non-orthogonal multiple access strategy. Assuming that the kth terminal device can eliminate the interference of devices with lower channel gain than itself, the total achievable throughput of K terminal devices is expressed as:

[0037]

[0038] Wherein, α1T is the duration of the uplink transmission phase; represents the channel vector between k terminal devices and the data receiver, represents the channel vector between k terminal devices and RIS, Φ1 represents the RIS phase shift matrix in the uplink, and H represents the channel matrix between RIS and the data receiver; q k Indicates the transmit power of the kth device in the uplink.

[0039] Furthermore, determining the energy harvesting optimization problem based on the constructed model includes:

[0040] By jointly optimizing the device group u, the base station's transmit power allocation P, the transmit power allocation q of each device in wireless information transmission, the RIS phase shift matrices Φ0 and Φ1 in the downlink and uplink, and the energy beamforming vector W of each group m and the transmission time slot allocation α to maximize the total achievable throughput; the mathematical expression of the optimization problem is (P1):

[0041]

[0042] C6:|(Φ0) bb|=1,b=1,…,M R

[0043] C7:|(Φ1) bb |=1,b=1,…,M R ,

[0044] C8:α0+α1≤1,α0≥0,α1≥0.

[0045] Among them, the constraints in the optimization problem (P1) are divided into two categories: in the downlink transmission phase, C1 is the device scheduling variable restriction, C2 indicates that a device can be associated with at most one RF link (can only be divided into one group), C3 represents the energy beamforming vector constraint for each group, C4 is the total transmit power constraint of the base station, and C6 represents the unit modulus constraint of the RIS phase shift in wireless power transmission; for the uplink transmission phase, C5 is the transmit power constraint of each device, C7 represents the unit modulus constraint of the RIS phase shift used for wireless information transmission, and C8 represents the constraint of the total transmission time slot.

[0046] Furthermore, the joint optimization of device grouping, base station transmit power allocation, energy beamforming, and RIS phase shift matrix in downlink transmission, and device transmit power allocation, RIS phase shift matrix, and transmission time slot allocation in uplink transmission includes:

[0047] In the first stage, based on the channel correlation of terminal devices, an algorithm combining Kmeans++ clustering algorithm and exclusion theory is used to obtain optimized device grouping;

[0048] In phase two, based on the device grouping information obtained in phase one, an iterative optimization algorithm is used to obtain the optimal values ​​of the base station's transmit power allocation, energy beamforming, and the RIS phase shift matrix in downlink transmission, thereby maximizing the total collected energy of all devices. The total achievable throughput of the devices is then maximized by iteratively optimizing the devices' transmit power allocation, transmission time slot allocation, and the RIS phase shift matrix in uplink transmission.

[0049] Furthermore, the first stage specifically includes:

[0050] The total number of terminal devices in a group is set to be the same as the number of RF chains N. Due to the spatial directionality of mmWave beams, the grouping principle aims to maximize the correlation between devices in the same group while minimizing the correlation between devices in different groups. The expression for the channel correlation (NCC) between two terminal devices is:

[0051]

[0052] Among them, D i 、D j Indicates terminal equipment, gi 、g j They represent the equivalent millimeter wave channels between the base station and the i-th device and between the base station and the j-th device in downlink transmission respectively;

[0053] Initial grouping stage: The two terminal devices with the lowest channel correlation values ​​are selected as the central devices of the first and second groups, respectively denoted as Ω1 and Ω2, and recorded in the central device set Ω; then, among the remaining devices, the terminal device with the smallest sum of channel correlations with the determined central device is selected as the central device Ω of the mth group. m ;

[0054] Fine-tuning grouping stage: When the channel correlation between two devices is lower than the overall average normalized channel correlation (SANCC), the two devices are defined as having an exclusion relationship, and the preliminary grouping results are fine-tuned based on the exclusion relationship.

[0055] Furthermore, the second stage specifically includes:

[0056] Based on the terminal device grouping results obtained in phase 1, the optimization process of other parameters is divided into two sub-phases;

[0057] In the first sub-stage, an iterative optimization algorithm is used to obtain the optimal value of the base station's transmit power allocation, energy beamforming, and the RIS phase shift matrix in downlink transmission, thereby maximizing the total collected energy of the terminal device.

[0058] In the second sub-stage, the total achievable throughput of the device is maximized by iteratively optimizing the terminal device's transmit power allocation, transmission time slot allocation, and RIS phase shift matrix in uplink transmission.

[0059] The present invention provides a RIS-assisted mmWave frequency band WPCN system, in which devices first receive radio frequency energy signals transmitted by a base station and reflected by the RIS during downlink transmission. Subsequently, with the assistance of the RIS, the devices use the collected energy to upload their respective data on the uplink using a non-orthogonal multiple access strategy. To improve the overall achievable throughput of devices in uplink transmission, the present invention proposes a method for jointly optimizing device grouping, base station transmit power allocation, energy beamforming, and the RIS phase shift matrix in downlink transmission, as well as device transmit power allocation, the RIS phase shift matrix, and transmission time slot allocation in uplink transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. 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 any creative work.

[0061] Figure 1 Schematic diagram of the structure (system transmission model) of the RIS-assisted mmWave band WPCN system in an embodiment of the present invention;

[0062] Figure 2 Schematic diagram of the total energy harvested by all devices in the RIS-assisted mmWave band WPCN system in an embodiment of the present invention when the present invention, the grouping algorithm solution based on channel gain correlation, and the solution without deploying the smart metasurface are respectively adopted;

[0063] Figure 3 Schematic diagram of the total information sending rate of all devices in the RIS-assisted mmWave band WPCN system in an embodiment of the present invention when the present invention and the grouping algorithm scheme based on channel gain correlation are respectively adopted and when the smart metasurface scheme is not deployed. DETAILED DESCRIPTION

[0064] The embodiments of the present invention are described in detail below, examples of which 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 limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0065] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They 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. Therefore, they cannot be understood as limitations on the present invention.

[0066] In the description of the present invention, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0067] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting 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.

[0068] To overcome the limited propagation range and spectrum resource shortage issues faced by mmWave-based WPCN systems and RIS-assisted WPCN systems, respectively, a RIS-assisted mmWave WPCN system was designed, combining the characteristics of each technology. To address the non-convex optimization problem of maximizing the achievable throughput of devices in downlink transmission, a joint optimization algorithm was designed for device grouping, base station transmit power allocation, energy beamforming, device transmit power allocation, transmission time slot allocation, and the RIS phase shift matrix.

[0069] The technical solution of the present invention is explained in detail below with reference to the accompanying drawings and specific embodiments.

[0070] (1) System model

[0071] See also Figure 1 In the system model considered in the embodiment of the present invention, K terminals (hereinafter referred to as devices) are evenly distributed in a circle with a radius of 20 meters and a base station as the center. The data receiver is 15 meters away from the base station, and the RIS is located at the center between the base station and the data receiver, with a height of 2 meters. R The RIS of reflective elements can be modeled as a uniform planar array, and the base station is M P The data receiver is M D uniform linear array of elements.

[0072] Base stations have a continuous energy supply, while energy-constrained devices do not. Therefore, wireless power communication networks use a "harvest energy first, then transmit information" transmission protocol. Specifically, during the downlink transmission time slot α0T, the base station generates multiple energy beams to power the device. The device then uses the harvested energy to transmit information back to the data receiver during the uplink transmission time slot α1T. T represents the total transmission time slot, and α0 and α1 satisfy α0+α1≤1.

[0073] a) mmWave channel modeling

[0074] The mmWave channel can be characterized by a line-of-sight (LoS) link and several non-line-of-sight (NLoS) links. The mmWave channel between the kth device in the mth group and the base station or RIS can be modeled as:

[0075]

[0076] in,

[0077]

[0078] Represents the array steering vector. and θ m,k ,φ m,k (β m,k )and denote the departure angle and azimuth (elevation) of the line-of-sight link and the lth non-line-of-sight link associated with the kth device in the mth group, respectively. and They represent the line-of-sight (LOS) and non-line-of-sight (NLOS) complex path gains of the mth group of corresponding RF links, respectively. represents the Euclidean distance from the base station or RIS to the kth device in the mth group, and α is the path loss exponent. The channel matrix between the base station and RIS can be modeled as:

[0079]

[0080] Among them, φ(β) and φ l (β l ) are the azimuth (elevation) angles of the line-of-sight link and the first non-line-of-sight link associated with the RIS, θ and θ l are the departure angles relative to the base station, and d represents the Euclidean distance from the base station to the RIS. The channel vector h between the kth device and the data receiver d,k The expression can refer to (1), and the expression of the channel matrix H between RIS and data receiver can refer to (5).

[0081] According to the transmission protocol, the entire transmission process can be divided into two stages: energy collection in downlink transmission and information transmission in uplink transmission.

[0082] b) Energy harvesting model in downlink transmission

[0083] In the first stage, the base station transmits in downlink time slot α0 T The radio frequency energy signal is sent to K devices in the mth group. Therefore, the energy signal received by the kth device in the mth group can be written as:

[0084]

[0085] Among them, w m , denote the energy beamforming vectors pointing to the mth group and the m′th group respectively. is the equivalent channel from the base station to the kth device in the mth group, Φ0 represents the RIS phase shift matrix in the downlink, which can be expressed as in and Denote the phase shift and amplitude reflection coefficient of the bth reflector element respectively. This paper focuses on the effect of RIS phase shift on system performance, so is 1. u represents the additive white Gaussian noise (AWGN) received by the kth device. m′,k′ is the scheduling variable of the k′th device. If the k′th device is in the k′th group, set u m′,k′ =1; otherwise u m′,k′ = 0. The energy collected by the kth device in the mth group can be expressed as:

[0086]

[0087] Among them, η0 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:

[0088]

[0089] c) Information transmission model in uplink transmission

[0090] In the second stage, with the assistance of RIS, the devices use the collected energy to transmit their data back to the base station in the uplink using a non-orthogonal multiple access strategy. Assume that the equivalent channel gain satisfies Then the kth device will eliminate the interference of devices with lower channel gain than itself. The total achievable throughput of K devices can be expressed as:

[0091]

[0092] in, represents the RIS phase shift matrix in the uplink, and Represent the phase shift and amplitude reflection coefficient of the bth reflection element during the duration of wireless information transmission. set up is 1. k Indicates the transmit power of the kth device in the uplink.

[0093] (2) Energy harvesting optimization problem

[0094] To complete the above definition, the present invention aims to optimize the device group u, the base station's transmit power allocation p, the transmit power allocation q of each device in wireless information transmission, the RIS phase shift matrices Φ0 and Φ1 in the downlink and uplink, and the energy beamforming vector w of each group by jointly optimizing m And the transmission time slot allocation α to maximize the total achievable throughput. The mathematical expression of the optimization problem is (P1):

[0095]

[0096] C6:|(Φ0) bb |=1,b=1,…,M R

[0097] C7:|(Φ1) bb |=1,b=1,…,M R ,

[0098] C8: α0+α1≤1, α0≥0,α1≥0. (10)

[0099] The constraints in the optimization problem (P1) can be divided into two categories. In the downlink transmission phase, C1 is the device scheduling variable restriction. C2 indicates that a device can only be associated with at most one RF link (can only be divided into one group). C3 represents the energy beamforming vector constraint for each group. C4 is the total transmit power constraint of the base station. C6 represents the unit modulus constraint of the RIS phase shift in wireless power transmission. For the uplink transmission phase, C5 is the transmit power constraint of each device. C7 represents the unit modulus constraint of the RIS phase shift used for wireless information transmission. C8 represents the constraint of the total transmission time slot.

[0100] (3) Two-stage optimization design method

[0101] Since the constraints C3, C6 and C7 are all non-convex functions, and the optimization parameter w m ,p,Φ0,q,α, and Φ1 are highly coupled with each other. In order to effectively obtain these parameters, the present invention proposes a two-stage optimization design method. Specifically, in the first stage, based on the device channel correlation, an algorithm combining the K-means++ clustering algorithm and the exclusion theory is used to obtain the optimized device grouping result; in the second stage, according to the device grouping information obtained in the first stage, the optimal value of the base station's transmission power allocation, energy beamforming and RIS phase shift matrix in downlink transmission is obtained through an iterative optimization algorithm, so as to maximize the total collection energy of the device. Then, the total achievable throughput of the device is maximized by iteratively optimizing the transmission time slot allocation and the RIS phase shift matrix in uplink transmission. Specifically:

[0102] a) Phase 1: Optimizing device grouping

[0103] In practical communication networks, the number of RF links is often smaller than the number of devices. Therefore, K devices need to be grouped to improve energy harvesting efficiency. To ensure energy harvesting efficiency for devices in the system, the total number of groups is the same as the number of RF links, N. Due to the spatial directionality of mmWave beams, the grouping principle aims to maximize the correlation between devices in the same group while minimizing the correlation between devices in different groups. The channel correlation (NCC) between two devices is expressed as:

[0104]

[0105] The grouping algorithm proposed in this embodiment, which combines the K-means++ clustering algorithm and the exclusion theory, can be divided into two sub-stages. In the first sub-stage, the K-means++ clustering algorithm uses the normalized NCC as a metric to obtain preliminary device grouping results. In the second sub-stage, the grouping results obtained in the first sub-stage are fine-tuned using the exclusion theory to obtain the final grouping results.

[0106] The first sub-stage: preliminary grouping. This stage can be divided into two steps: selecting a central device and grouping the remaining devices. Specifically, the two devices with the lowest NCC values ​​are selected as the central devices of the first and second groups, denoted as Ω1 and Ω2, respectively, and recorded in the central device set Ω. Then, among the remaining devices, the device with the smallest sum of NCCs with the identified central device is selected as the central device of group m, Ω. m , such as the expression:

[0107]

[0108] Where D represents the set of all devices, D l ∈Ω\D represents the remaining equipment, D m′ ∈Ω represents the determined central device. After obtaining the required number of central devices, the central device is selected and defined as Ω={Ω1,Ω2,…,Ω N The remaining devices are divided into corresponding groups according to the principle of the highest correlation between devices in the same group (with which group's central device channel has the highest correlation), for example, the kth device D k According to the following expression, they are divided into group m middle,

[0109]

[0110] The second sub-stage: fine-tuning the grouping. In order to further enhance the correlation of device channels within the same group and weaken the correlation between different groups, this paper introduces a new concept, namely the repulsion theory, to fine-tune the device grouping obtained in the first sub-stage. When the NCC between two devices is lower than the overall average normalized channel correlation (SANCC), the two devices are defined as a repulsive relationship. Specifically, the repulsion vector (EV) of the kth device is defined as a k ∈C 1×K ,k∈{1,…,K}, which consists of only 0 or 1. 0 means that the NCC between a pair of devices is higher than the total SANCC, while 1 means it is lower than the total SANCC, that is, the two devices are mutually exclusive. For example, if the NCC between the kth device and the jth device is lower than the SANCC, then set a k The j-th column element of is 1, that is, a k [:,j]=1. And a k The value of [:,k] is fixed to 1. After K-1 calculations, a k After the first sub-stage, each group will have an exclusion matrix (EM), Among them S m is the total number of devices in the mth group. EM is composed of the EVs corresponding to the devices in the group, that is, For example, if group m contains the i-th, j-th, and l-th devices, then A m =[a i ;a j ;a l ]. Matrix A m The sum of the columns is defined as vector Q m ∈C 1 ×K ,m∈{1,…,N}, as an indicator to judge whether exclusion occurs in the group. 1≤i≤K Q m [:,i]≥2, it means that exclusion has occurred in group m and the device grouping needs to be adjusted. However, a special case must be considered, that is, multiple devices in a group exclude a device that does not belong to the group at the same time. This situation will also lead to max1 ≤i≤K Q m [:,i]≥2, but no exclusion occurs within the group at this time, which is defined as "pseudo-exclusion" in the present invention. For example, kth device With D l′ , The NCC between them is lower than the total SANCC, then Q m [:,l′] will also be greater than 2, but at this time D l′ is not in the mth group, so the mth group does not exclude. In order to avoid misjudgment, when Qm When [:,l′]≥2, it is necessary to verify whether the mth group contains the l′th device. After excluding the pseudo-exclusion case, all possible max 1≤i≤K Q m The [:,i] value is divided into the following three cases.

[0111] Case 1: When max 1≤i≤K Q m [:,i]>2, indicating that at least one device in group m excludes multiple devices in the group at the same time. In this case, 1≤i≤K Q m [:,i] The corresponding device will be processed first. For example, if l * =max 1≤i≤ K Q m [:,i], then D l* will be removed from the mth group and then added to m according to the following expression * In the group:

[0112]

[0113] Each adjustment of the device grouping is recorded as an update. siga It is defined as the overall inter-group mean NCC, i.e.:

[0114]

[0115] If the NCC is adjusted after the device group siga If it is lower than the value of the previous round, the update of Q is terminated. m [:,l * ], and set Q m [:,l * ]=0. This process is repeated until max 1≤i≤K Q m [:,i]≤2.

[0116] Case 2: When max 1≤i≤K Q m [:,i]= 2 , it is necessary to analyze which device should be regrouped. This is because max1 ≤i≤K Q m [:,i]= 2 In this case, the two devices are mutually exclusive with the central device Ω in group m. m The device with the lower NCC needs to be regrouped according to (14). If the update of the device is terminated, another device is selected for regrouping. When the update of both devices stops, it means that the grouping result for the two devices is already optimal and no further adjustment is required. Then set Qm The corresponding values ​​of the two columns are 0.

[0117] Case 3: When max 1≤i≤K Q m [:,i]=1, the device group update is complete and no further processing is required. 1≤i≤K Q m [:,i]=1 has been achieved in the first phase, so there is no need to further fine-tune the device grouping.

[0118] b) Phase 2: Optimizing base station transmit power allocation, energy beamforming, and the RIS phase shift matrix in downlink transmission

[0119] Based on the device grouping results obtained in Phase 1, the optimization process for other parameters can be divided into two sub-phases. In the first sub-phase, an iterative optimization algorithm is used to obtain the optimal values ​​for the base station's transmit power allocation, energy beamforming, and the RIS phase shift matrix for downlink transmission, thereby maximizing the total energy collected by the devices. In the second sub-phase, the device's total achievable throughput is maximized by iteratively optimizing the device's transmit power allocation, transmission time slot allocation, and the RIS phase shift matrix for uplink transmission.

[0120] The first sub-stage: maximize the total harvested energy. The optimization problem of this stage is expressed as (P2):

[0121]

[0122] C6:|(Φ0) bb |=1,b=1,…,M R ,

[0123]

[0124] Among them, C9 is the minimum harvested power constraint for each device. Due to the coupling between the three optimization parameters, the optimization problem (P2) is further decomposed into three sub-problems, which are then solved using an iterative optimization algorithm. The details are as follows:

[0125] Optimize the energy beamforming vector: In each device group, given Φ0, select the channel response vector of the device with the highest channel gain as the equivalent channel vector of the group, expressed as:

[0126]

[0127] Then, the normalized energy beamforming vector of the mth group is expressed as:

[0128]

[0129] Optimizing the RIS phase shift matrix in downlink transmission: Under fixed energy beamforming vectors and base station transmit power allocation, the optimization problem (P2) can be simplified to (P3):

[0130]

[0131] st C6:|(Φ0) bb |=1,b=1,…,M R . (19)

[0132] definition

[0133] in, and Therefore, the optimization problem (P3) can be reformulated as:

[0134]

[0135] stC10:|(V0) nn |=1,n=1,…,M R +1,

[0136]

[0137] in, After the transformation is completed, the optimization problem (P3) becomes a convex semidefinite programming problem (SDP), which can be solved using convex optimization tools such as CVX. If the result does not meet the constraint condition of rank(V0)=1, the optimal result can be converted to Rewrite to V0 * =PΛP H , where P∈C (N+1)×(N+1) is the identity matrix, Λ∈C (N+1)×(N+1) is a diagonal matrix composed of eigenvalues. Through the above operations, a suboptimal solution with rank one can be obtained. in,

[0138] Optimizing the base station's transmit power allocation: For a given energy beamforming vector and the RIS phase shift matrix in downlink transmission, a modified greedy algorithm can be used to solve the optimization problem of base station transmit power allocation. Based on the total transmit power constraint, the optimization strategy is to allocate as much power as possible to devices with channel gains above the threshold to improve the total achievable throughput. However, for devices with lower channel gains, only the minimum harvested power requirement needs to be met. Assume that the device channel gain in each group satisfies If the S mIf the harvested power of the device is , then the harvested power of other devices with higher channel gain in the mth group can also be satisfied, which can be expressed as: p p≥Z p ,in,

[0139]

[0140] Therefore, the optimization problem of base station transmission power allocation can be expressed as (P4):

[0141]

[0142] stC12:Ξ p p≥Z p ,

[0143]

[0144] in is the number of devices whose channel gain is above the threshold. Since the objective function and the constraints are linear, it can be concluded that the optimization problem (P4) is a linear programming (LP) problem, which can be solved by CVX.

[0145] Iterative optimization: Finally, the energy beamforming vector, the base station's transmit power allocation, and the RIS phase shift matrix in downlink transmission are iteratively optimized until the target value of the optimization problem (P2) converges.

[0146] The second sub-stage: maximize the total achievable throughput. Based on the energy beamforming vector obtained in the first sub-stage, the base station's transmit power allocation, and the RIS phase shift matrix optimization results in downlink transmission, problem (P1) can be rewritten as (P5):

[0147]

[0148] C7:|(Φ1) bb |=1,b=1,…,M R ,

[0149] C8: α0+α1≤1, α0≥0,α1≥0. (24)

[0150] Due to the coupling between the optimization parameters, the optimization problem (P5) is further decomposed into three sub-problems, which are then solved by an iterative optimization algorithm. The details are as follows:

[0151] Optimizing the transmission power allocation of devices: Assume that the processing cost of the device, such as the energy consumption of generating and encoding information, can be ignored. Then when the transmission slot is allocated to timing, the constraint C5 in expression (24) is equivalent to:

[0152]

[0153] Optimizing transmission time slot allocation: Given the device's transmit power allocation and the RIS phase shift matrix in uplink transmission, the optimization problem for transmission time slot allocation can be expressed as:

[0154]

[0155] in, When α0∈[0,1), expression (26) is continuously differentiable and can be obtained by The optimized transmission time slot allocation result is obtained, namely:

[0156]

[0157] in W(·) means that for a complex number x, x=W(x)e W(x) Lambert-W function.

[0158] Optimizing the RIS phase shift matrix in uplink transmission: For a given device transmit power allocation and transmission time slot allocation, the problem of optimizing the RIS phase shift matrix in uplink transmission can be expressed as (P6):

[0159]

[0160] stC7:|(Φ1) bb |=1,b=1,…,M R . (28)

[0161] Define separately in Then the optimization problem (P6) can be rewritten as:

[0162]

[0163] stC14:|(V1) nn |=1,n=1,…,M R +1,

[0164]

[0165] in, Similar to the optimization problem (P3), (P6) is a convex semidefinite programming problem that can be solved using convex optimization tools such as CVX. * Does not meet rank(V1 * )=1, can be obtained by , derive a solution that satisfies Suboptimal solution

[0166] Iterative optimization: Finally, iterative optimization alternately optimizes the transmission power allocation of the device, optimizes the transmission time slot allocation, and the RIS phase shift matrix in uplink transmission until the target value of the optimization problem (P5) converges.

[0167] (4) Simulation experiment results

[0168] In this embodiment, simulation analysis verifies the effectiveness of the proposed two-stage resource optimization configuration method in improving the system's achievable throughput, and compares it with a grouping algorithm based on channel gain correlation and a solution without deploying an intelligent metasurface. In the grouping algorithm based on channel gain correlation, the grouping algorithm proposed in the literature [4] is used to group devices in stage one, and the method proposed in the present invention is used to optimize the remaining parameters in stage two. In the solution without deploying an intelligent metasurface, the system does not deploy an intelligent metasurface, and the base station's transmit power allocation, energy beamforming, device transmit power allocation, and transmission time slot allocation all use the method mentioned in the present invention.

[0169] The simulation experiment parameters were set as follows: the base station coverage radius was 20 meters, the total number of devices was 24, the number of RF chains was 4, the base station was equipped with a 64-antenna array, and the data receiver was equipped with a 4-antenna array. The RIS was a uniform planar array consisting of 32 elements. The energy conversion efficiency was 0.7, and the device battery saturation power was 0.1 W.

[0170] Figure 2 The total energy harvested by all devices when the resource optimization allocation scheme proposed in the present invention is adopted in the RIS-assisted mmWave band WPCN system under consideration is demonstrated, and compared with the grouping algorithm scheme based on channel gain correlation and the scheme without deploying intelligent metasurfaces. As can be seen from the simulation diagram, the total energy harvested by all devices under the above three schemes increases with the increase of the base station's transmission power. The energy harvested by the devices under the proposed two-stage resource optimization configuration scheme is the largest, and the energy harvested by the devices under the scheme without deploying intelligent metasurfaces is the smallest. It can be seen that the proposed two-stage resource optimization configuration scheme is effective in improving the system's energy efficiency, and that the intelligent metasurface plays an important role in improving the system's transmission performance.

[0171] Figure 3The paper demonstrates the aggregate transmission rate of all devices in a RIS-assisted mmWave band WPCN system using the proposed resource optimization allocation scheme. The results are compared with a grouping algorithm based on channel gain correlation and a scheme without intelligent metasurface deployment. The simulation graphs show that the total rate at which base stations receive information transmitted by devices using the proposed two-stage resource optimization scheme significantly outperforms the other two schemes, demonstrating the crucial importance of optimizing resource allocation in improving the aggregate transmission rate of devices.

[0172] (5) Advantages and beneficial effects

[0173] Existing research on WPCN systems mostly focuses on applying mmWave to WPCN systems, or using RIS to improve the transmission performance of WPCN systems. The transmission structures considered are mostly hybrid access points that send RF energy signals and receive information signals in downlink and uplink transmissions, respectively. In terms of system performance optimization, most of them only consider the impact of RIS phase shift, transmission time slot allocation, and active beamforming on system performance. Taking into account the current status of existing technology research, the present invention proposes a RIS-assisted mmWave frequency band WPCN system and a corresponding resource configuration method. In general, the present invention has the following advantages:

[0174] 1) This invention considers a system framework that combines WPCN, mmWave, and RIS. It uses the mmWave frequency band to solve the problem of spectrum resource shortage, while combining it with RIS to improve the system's coverage and transmission efficiency.

[0175] 2) At the same time, in order to avoid signal interference and network congestion when using a hybrid access point to receive and send signals, the present invention adopts a single-transmit single-receive structure, that is, the base station sends the radio frequency energy signal, and the data receiver is responsible for receiving the information sent by the device.

[0176] 3) Based on the proposed system, in order to improve the total achievable throughput of the system, the present invention proposes a joint optimization method for device grouping, base station transmit power allocation, energy beamforming, downlink and uplink RIS phase shift matrices, device transmit power allocation, and transmission time slot allocation, which can effectively improve the total achievable throughput of devices in uplink transmission.

[0177] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" 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 suitable manner in any one or more embodiments or examples.

[0178] While 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 invention, and that the scope of the invention is defined by the claims and their equivalents.

[0179] 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 can 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 RIS-assisted millimeter wave wireless energy transmission network resource optimization system, characterized in that: include: a base station for transmitting radio frequency energy signals during downlink transmission; Intelligent metasurface RIS, used to reflect radio frequency energy signals so that terminal devices can obtain energy after receiving the radio frequency energy signals; a plurality of terminal devices, wherein the plurality of terminal devices are divided into a plurality of groups, and the terminal devices in each group are configured to utilize the received energy to transmit data to a data receiver using a non-orthogonal multiple access strategy during an uplink transmission period; A data receiver, used to receive data uploaded by each terminal device; The system jointly optimizes device grouping, base station transmit power allocation, energy beamforming, and RIS phase shift matrix in downlink transmission, as well as device transmit power allocation, RIS phase shift matrix, and transmission time slot allocation in uplink transmission, to improve the total achievable throughput of terminal devices in uplink transmission. The joint optimization process consists of two stages: In the first stage, based on the channel correlation of terminal devices, an algorithm combining Kmeans++ clustering algorithm and exclusion theory is used to obtain optimized device grouping; In phase two, based on the device grouping information obtained in phase one, an iterative optimization algorithm is used to obtain the optimal values ​​of the base station's transmit power allocation, energy beamforming, and the RIS phase shift matrix in downlink transmission, thereby maximizing the total collected energy of all devices. The total achievable throughput of the devices is then maximized by iteratively optimizing the devices' transmit power allocation, transmission time slot allocation, and the RIS phase shift matrix in uplink transmission.

2. A resource optimization method for a RIS-assisted millimeter wave wireless energy transmission network, applied to the system according to claim 1, characterized in that: The following steps are involved: Design a RIS-assisted mmWave band WPCN system, which includes a base station, RIS, a data receiver, and multiple terminal devices. Based on the mmWave frequency band WPCN system, mmWave channel modeling, energy collection model in downlink transmission, and information transmission model in uplink transmission are constructed; Determine the energy harvesting optimization problem based on the constructed model; Based on the optimization problem, the device grouping, base station transmit power allocation, energy beamforming and RIS phase shift matrix in downlink transmission, as well as the device transmit power allocation, RIS phase shift matrix and transmission time slot allocation in uplink transmission are jointly optimized to improve the total achievable throughput of terminal devices in uplink transmission.

3. The resource optimization method for a RIS-assisted millimeter wave wireless energy transmission network according to claim 2, characterized in that: The mmWave channel modeling is constructed as follows: The mmWave channel is characterized by a line-of-sight link and several non-line-of-sight links. The mmWave channel between the kth device in the mth group and the base station or RIS is modeled as: Among them, a(), a ′ () represents the array steering vector; θ m,k 、φ m,k (β m,k )and denote the departure angle and azimuth of the line-of-sight link and the lth non-line-of-sight link associated with the kth device in the mth group, respectively; γ g,k,m 、 γ r,k,m and They represent the line-of-sight and the lth non-line-of-sight complex path gains of the mth group of corresponding RF links respectively; d g,k,m d r,k,m represents the Euclidean distance from the base station or RIS to the kth device in the mth group, and α is the path loss exponent; The channel matrix between the base station and RIS is modeled as: Among them, φ(β) and φ l (β l ) are the azimuths of the line-of-sight link and the lth non-line-of-sight link associated with the RIS, θ and θ l are the departure angles associated with the base station, and d represents the Euclidean distance from the base station to the RIS.

4. The resource optimization method for a RIS-assisted millimeter wave wireless energy transmission network according to claim 2, characterized in that: The energy harvesting model in the downlink transmission is constructed as follows: The base station sends radio frequency energy signals to K devices in the downlink transmission time slot α0T; therefore, the energy signal received by the kth device in the mth group is written as: Among them, W m , W m′ denote the energy beamforming vectors pointing to the mth group and the m′th group respectively; is the equivalent channel from the base station to the kth device in the mth group, Φ0 represents the RIS phase shift matrix in the downlink, G represents the channel matrix between the base station and the RIS; u m′,k′ is the scheduling variable of the k′th device; n k represents the additive white Gaussian noise received by the kth device; s k 、s k′ are the RF energy signal vectors of the kth and k′th devices respectively; p m 、p m′ are the powers of the mth and m′th RF links respectively; The energy collected by the kth device in the mth group is expressed as: Among them, η0 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.

5. The resource optimization method for a RIS-assisted millimeter wave wireless energy transmission network according to claim 2, characterized in that: The information transmission model in the uplink transmission is constructed as follows: With the assistance of RIS, the terminal devices use the collected energy to upload their own data using a non-orthogonal multiple access strategy. Assuming that the kth terminal device can eliminate the interference of devices with lower channel gain than itself, the total achievable throughput of K terminal devices is expressed as: Wherein, α1T is the duration of the uplink transmission phase; represents the channel vector between k terminal devices and the data receiver, represents the channel vector between k terminal devices and RIS, Φ1 represents the RIS phase shift matrix in the uplink, H represents the channel matrix between RIS and data receiver; q k Indicates the transmit power of the kth device in the uplink.

6. The resource optimization method for a RIS-assisted millimeter wave wireless energy transmission network according to claim 2, characterized in that: The energy harvesting optimization problem is determined based on the constructed model, including: By jointly optimizing the device group u, the base station's transmit power allocation P, the transmit power allocation q of each device in wireless information transmission, the RIS phase shift matrices Φ0 and Φ1 in the downlink and uplink, and the energy beamforming vector W of each group m and the transmission time slot allocation α to maximize the total achievable throughput; the mathematical expression of the optimization problem is (P1): C6:|(Φ0) bb |=1,b=1,…,M R C7:|(Φ1) bb |=1,b=1,…,M R , C8:α0+α1≤1,α0≥0,α1≥0. Among them, the constraints in the optimization problem (P1) are divided into two categories: in the downlink transmission phase, C1 is the device scheduling variable restriction, C2 indicates that a device can be associated with at most one RF link, C3 represents the energy beamforming vector constraint for each group, C4 is the total transmit power constraint of the base station, and C6 represents the unit modulus constraint of the RIS phase shift in wireless power transmission; for the uplink transmission phase, C5 is the transmit power constraint of each device, C7 represents the unit modulus constraint of the RIS phase shift used for wireless information transmission, and C8 represents the constraint of the total transmission time slot.

7. The resource optimization method for a RIS-assisted millimeter wave wireless energy transmission network according to claim 2, characterized in that: The first stage specifically includes: The total number of terminal devices in a group is set to be the same as the number of RF chains N. Due to the spatial directionality of mmWave beams, the grouping principle aims to maximize the correlation between devices in the same group while minimizing the correlation between devices in different groups. The expression for the channel correlation between two terminal devices is: Among them, D i 、D j Indicates terminal equipment, g i 、g j They represent the equivalent millimeter wave channels between the base station and the i-th device and between the base station and the j-th device in downlink transmission respectively; Initial grouping stage: The two terminal devices with the lowest channel correlation values ​​are selected as the central devices of the first and second groups, respectively denoted as Ω1 and Ω2, and recorded in the central device set Ω; then, among the remaining devices, the terminal device with the smallest sum of channel correlations with the determined central device is selected as the central device Ω of the mth group. m ; Fine-tuning grouping stage: When the channel correlation between two devices is lower than the overall average normalized channel correlation, the two devices are defined as having an exclusion relationship, and the preliminary grouping results are fine-tuned based on the exclusion relationship.

8. The resource optimization method for a RIS-assisted millimeter wave wireless energy transmission network according to claim 2, characterized in that: The second stage specifically includes: Based on the terminal device grouping results obtained in phase 1, the optimization process of other parameters is divided into two sub-phases; In the first sub-stage, an iterative optimization algorithm is used to obtain the optimal value of the base station's transmit power allocation, energy beamforming, and the RIS phase shift matrix in downlink transmission, thereby maximizing the total collected energy of the terminal device. In the second sub-phase, the total achievable throughput of the device is maximized by iteratively optimizing the terminal device's transmit power allocation, transmission time slot allocation, and RIS phase shift matrix in uplink transmission.

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