Resource optimization system and method for RIS-assisted millimeter wave wireless energy transfer network

Abstract

The invention discloses a resource optimization system and method for an RIS-assisted millimeter wave wireless energy transfer network, and belongs to the technical field of wireless communication. The system comprises a base station used for transmitting a radio frequency energy signal in a downlink transmission period; the intelligent metasurface RIS is used for reflecting the radio frequency energy signal, so that the terminal equipment obtains energy after receiving the radio frequency energy signal; the plurality of terminal devices are divided into a plurality of groups, and the terminal devices in each group are used for sending the data to the data receiver in the uplink transmission period by using the received energy and adopting a non-orthogonal multiple access strategy; the data receiver is used for receiving data uploaded by each terminal device; wherein the equipment grouping, the transmitting power distribution of the base station, the energy beam forming and the RIS phase shift matrix in the downlink transmission, and the transmitting power distribution of the equipment, the RIS phase shift matrix and the transmission time slot distribution in the uplink transmission are jointly optimized, so that the total achievable throughput of the terminal equipment in the uplink transmission is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, 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 network (WPCN) includes two links: downlink energy transmission and uplink information transmission. The device uses the energy collected in the downlink transmission to transmit uplink information. However, with the rapid increase in the number of devices, the shortage of spectrum resources has become a major problem faced by WPCN. mmWave, due to its rich spectrum resources, has been proposed as one of the key candidate technologies to solve this problem.

[0003] In order to overcome the problem of spectrum resource shortage and building shading in WPCN, the academic community has conducted extensive research on WPCN systems based on mmWave frequency bands and WPCN systems assisted by intelligent metasurfaces (RIS). However, most of the current research on WPCN systems focuses on applying mmWave to WPCN systems, or using RIS to improve the transmission performance of WPCN systems. However, the combination of mmWave and WPCN faces the challenges of low energy transmission efficiency and small coverage, while RIS-assisted WPCN systems are constrained by limited spectrum resources. Therefore, it is necessary to consider combining WPCN, mmWave and RIS for research. In addition, the transmission structure considered by the existing WPCN system is mostly that the hybrid access point sends RF energy signals and receives information signals in downlink and uplink transmission respectively. In this structure, signal interference and network congestion will occur when the hybrid access point receives and sends signals. In addition, the current improvement of the throughput of the RIS-assisted WPCN system is mainly achieved by optimizing RIS phase shift, transmission time slot allocation and active beamforming, and rarely optimizing the grouping of devices. 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 object of the present invention is to provide a resource optimization system and method for a RIS-assisted millimeter wave wireless power 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 a radio frequency energy signal during downlink transmission;

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

[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 used 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 the second stage, based on the device grouping information obtained in the first stage, the optimal value of the base station's transmit 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 all devices; then the total achievable throughput of the device is maximized by iteratively optimizing the device's transmit power allocation, transmission time slot allocation and 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 comprises 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] According to 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(), a′() represent array steering vectors; θ m,k ,φ m,k (β m,k )and denote the departure angle and azimuth (elevation angle) 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 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.

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

[0029] The base station transmits in downlink time slot α 0 The radio frequency energy signal is sent to K devices within T; 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, Φ 0 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; um′,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;

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

[0033]

[0034] Among them, η 0 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] Among them, α 1 T 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, Φ 1 represents the RIS phase shift matrix in the uplink, H represents the channel matrix between the RIS and the data receiver; q k Indicates the transmit power of the kth device in the uplink.

[0039] Further, determining the energy harvesting optimization problem according to the constructed model includes:

[0040] By jointly optimizing the device grouping u, the base station's transmission power allocation P, the transmission power allocation q of each device in wireless information transmission, and the RIS phase shift matrix Φ in the downlink and uplink 0 and Φ 1 , 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:|(Φ 0 ) bb |=1,b=1,…,M R

[0043] C7:|(Φ 1 ) bb |=1,b=1,…,M R ,

[0044] C8:α 0 +α 1 ≤1,α 0 ≥0,α 1 ≥0.

[0045] Among them, the constraints in the optimization problem (P1) are divided into two categories: in the downlink transmission stage, 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 of each group, C4 is the total transmission 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 stage, C5 is the transmission 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 transmit power allocation, RIS phase shift matrix and transmission time slot allocation of devices 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 the second stage, based on the device grouping information obtained in the first stage, the optimal value of the base station's transmit 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 all devices; then the total achievable throughput of the device is maximized by iteratively optimizing the device's transmit power allocation, transmission time slot allocation and RIS phase shift matrix in uplink transmission.

[0049] Furthermore, the first stage specifically includes:

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

[0051]

[0052] Among them, D i , D j Indicates terminal equipment, g i , g j denote 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: Select the two terminal devices with the lowest channel correlation value as the center devices of the first group and the second group, denoted by Ω 1 and Ω 2 , 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 total average normalized channel correlation (SANCC), the two devices are defined as an exclusion relationship, and the preliminary grouping results are fine-tuned based on the exclusion relationship.

[0055] Furthermore, the second stage specifically includes:

[0056] According to 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, the optimal value of the base station's transmit power allocation, energy beamforming, and RIS phase shift matrix in downlink transmission is obtained through an iterative optimization algorithm, 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 beneficial effects of the present invention are as follows: the present invention proposes a RIS-assisted mmWave band WPCN system, in which the device first receives the radio frequency energy signal sent by the base station and reflected by the RIS in the downlink transmission, and then, with the assistance of the RIS, the device uses the collected energy to upload its own data in the uplink using a non-orthogonal multiple access strategy. In order to improve the total achievable throughput of the device in the uplink transmission, the present invention proposes a method for jointly optimizing the device grouping, the base station's transmit power allocation, energy beamforming and RIS phase shift matrix in the downlink transmission, and the device's transmit power allocation, RIS phase shift matrix and transmission time slot allocation in the 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 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.

[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 It is a 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 and the grouping algorithm solution based on channel gain correlation and the smart metasurface solution are respectively adopted;

[0063] Figure 3 This is a schematic diagram of the total rate of information transmission 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 and the smart metasurface scheme are respectively adopted and not deployed. DETAILED DESCRIPTION

[0064] 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.

[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., 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.

[0066] 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.

[0067] 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.

[0068] In order to overcome the problems of limited propagation range and shortage of spectrum resources faced by WPCN system based on mmWave frequency band and WPCN system assisted by RIS, a WPCN system assisted by RIS in mmWave frequency band is designed by combining the characteristics of each technology. In order to solve the non-convex optimization problem of maximizing the achievable throughput of devices in downlink transmission, a joint optimization algorithm for device grouping, base station transmit power allocation, energy beamforming, device transmit power allocation, transmission time slot allocation and RIS phase shift matrix is ​​designed.

[0069] The technical solution of the present invention is explained in detail below in conjunction with 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 in the center between the base station and the data receiver, with a height of 2 meters. R The RIS with M reflective elements can be modeled as a uniform planar array, and the base station is P The data receiver is a uniform linear array that can form N RF links. D Uniform linear array of elements.

[0072] The base station has a continuous energy supply, while the energy-constrained device does not have a continuous energy supply. Therefore, the wireless power communication network adopts the "collect energy first and then transmit information" transmission protocol, that is, in the downlink transmission time slot α 0 Within T, the base station generates multiple energy beams to provide energy to the device, and then the device uses the collected energy to transmit in the uplink time slot α 1 T within which information is transmitted back to the data receiver. Where T represents the total transmission time slot, α 0 and α 1 Satisfy α 0 +α 1 ≤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 They represent 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 of the mth group of corresponding RF links and the lth non-line-of-sight complex path gain 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 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. 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 the RIS and the 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 α 0 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, Φ 0 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 reflection element, respectively. This paper focuses on the effect of RIS phase shift on system performance, so is 1. 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, η 0 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 respective 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 the device whose channel gain is lower than its own. At this time, the total achievable throughput of K devices can be expressed as:

[0091]

[0092] in, represents the RIS phase shift matrix in the uplink, and Respectively 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 grouping u, the base station transmission power allocation p, the transmission power allocation q of each device in wireless information transmission, the RIS phase shift matrix Φ in the downlink and uplink 0 and Φ 1 , 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):

[0095]

[0096] C6:|(Φ 0 ) bb |=1,b=1,…,M R

[0097] C7:|(Φ 1 ) bb |=1,b=1,…,M R ,

[0098] C8: α 0 +α 1 ≤1, α 0 ≥0,α 1 ≥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 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, the optimization parameter w m ,p,Φ 0 ,q,α,andΦ 1 They are highly coupled to each other. In order to effectively obtain these parameters, the present invention proposes a two-stage optimization design method. Specifically, in stage one, 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 stage two, according to the device grouping information obtained in stage one, 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 less than the number of devices, so K devices need to be grouped to improve the efficiency of energy harvesting. In order to ensure the energy harvesting efficiency of the devices in the system, the total number of groups is the same as the number of RF links N. Since the mmWave beam has spatial directionality, 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 combining the K-means++ clustering algorithm and the exclusion theory proposed in the embodiment of the present invention can be divided into two sub-stages. In the first sub-stage, the K-means++ clustering algorithm is used to obtain a preliminary device grouping result with the normalized NCC as an indicator. In the second sub-stage, the grouping result obtained in the first sub-stage is fine-tuned according to the exclusion theory to obtain the final grouping result.

[0106] The first sub-stage: preliminary grouping. This stage can be divided into two steps, namely, selecting the 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 group and the second group, denoted by Ω 1 and Ω 2 , and recorded in the central device set Ω. Then, among the remaining devices, the device with the smallest NCC sum with the determined central device is selected as the central device Ω of the mth group 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 selection is completed, and Ω={Ω 1 ,Ω 2 ,…,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 the central device channel has the highest correlation), for example, the kth device D k According to the following expression, they are divided into the mth group 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, the present invention 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 total 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, i.e., 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), Where Sm 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 ith, jth, and lth devices, then A m =[a i ; a j ; a l ] Matrix A m The sum of the columns is defined as the vector Q m ∈C 1 ×K ,m∈{1,…,N}, as an indicator to determine whether exclusion occurs within a group. 1≤i≤K Q m [:,i]≥2, it indicates that exclusion has occurred in the mth group 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 cause 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, The kth device With D l′ , If 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 Q m 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 the mth group 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 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, namely:

[0114]

[0115] If the NCC is adjusted after the equipment grouping 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 that are mutually exclusive with the center device Ω in the mth group are 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 updates of both devices are stopped, it means that the grouping result for the two devices is already optimal and no further adjustment is required. Then set Q m The corresponding values ​​of the two columns are 0.

[0117] Case 3: When max 1≤i≤K Q m When [:,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 stage, so there is no need to further fine-tune the device grouping.

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

[0119] According to the device grouping results obtained in phase 1, the optimization process of other parameters can be divided into two sub-phases. In the first sub-phase, the optimal value of the base station's transmit 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. In the second sub-phase, the total achievable throughput of the device is maximized by iteratively optimizing the device's transmit power allocation, transmission time slot allocation, and RIS phase shift matrix in 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:|(Φ 0 ) bb |=1,b=1,…,M R ,

[0123]

[0124] Among them, C9 is the minimum harvested power constraint of 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 Φ 0 In the case of , the channel response vector of the device with the highest channel gain is selected as the equivalent channel vector of the group, which is 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:|(Φ 0 ) bb |=1,b=1,…,M R . (19)

[0132] definition

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

[0134]

[0135] stC10:|(V 0 ) nn|=1,n=1,…,M R +1,

[0136]

[0137] in, After the transformation, 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 satisfy the constraint condition that rank(V0) = 1, the optimal result can be converted to Rewrite it as V 0 * =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, an adjusted 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 gain above the threshold to improve the total achievable throughput. However, for devices with lower channel gain, only the minimum harvested power requirement needs to be met. Assume that the device channel gain in each group satisfies If the S m If the harvested power requirement of the device is , then the harvested power requirements of other devices with higher channel gain in the mth group can also be met, 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 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:|(Φ 1 ) bb |=1,b=1,…,M R ,

[0149] C8: α 0 +α 1 ≤1, α 0 ≥0,α 1 ≥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 transmission power allocation of the device 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 α 0 ∈[0,1), expression (26) is continuously differentiable, so it 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 of .

[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:|(Φ 1 ) bb |=1,b=1,…,M R . (28)

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

[0162]

[0163] stC14:|(V 1 ) 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. 1 * Does not satisfy rank(V 1 * )=1, can be obtained by , deriving 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 the 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 achievable throughput of the system, and compares it with the grouping algorithm based on channel gain correlation and the solution without deploying 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 1, and the method proposed in the present invention is used to optimize the remaining parameters in stage 2. In the solution without deploying intelligent metasurface, the system does not deploy 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 relevant parameters of the simulation experiment are set as follows: the base station coverage radius is 20 meters, the total number of devices is 24, the number of RF chains is 4, the base station is equipped with 64 antenna arrays, and the data receiver is equipped with 4 antenna arrays. RIS is a uniform planar array composed of 32 elements. The energy conversion efficiency is 0.7, and the device battery saturation power is 0.1W.

[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 metasurface. It can be seen from the simulation diagram that the total energy harvested by all devices under the above three schemes increases with the increase of the transmission power of the base station. The energy harvested by the device under the proposed two-stage resource optimization configuration scheme is the largest, and the energy harvested by the device under the scheme without deploying intelligent metasurface is the smallest. It can be seen that the proposed two-stage resource optimization configuration scheme is effective in improving the energy efficiency of the system, and the intelligent metasurface plays an important role in improving the transmission performance of the system.

[0171] Figure 3 The sum of the information transmission rates of all devices when the resource optimization allocation scheme proposed by the present invention is adopted in the RIS-assisted mmWave band WPCN system under consideration is shown, and compared with the grouping algorithm scheme based on channel gain correlation and the scheme without deploying intelligent metasurface. It can be seen from the simulation diagram that the total rate of information transmission by the base station receiving 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 transmission by the device.

[0172] (5) Advantages and beneficial effects

[0173] Most of the existing research on WPCN systems focuses on applying mmWave to WPCN systems, or using RIS to improve the transmission performance of WPCN systems, and 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 research in the prior art, 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) The present invention considers a system framework that combines WPCN, mmWave and RIS, uses the mmWave frequency band to solve the problem of spectrum resource shortage, and combines RIS to improve the coverage and transmission efficiency of the system.

[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 a radio frequency energy signal, and the data receiver is responsible for receiving the information sent by the device.

[0176] 3) On the basis of 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 transmission power allocation, energy beamforming, downlink and uplink RIS phase shift matrix, device transmission power allocation and transmission time slot allocation, which can effectively improve the total achievable throughput of the device in uplink transmission.

[0177] 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.

[0178] 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.

[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 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 RIS-assisted millimeter wave wireless energy transmission network resource optimization system, characterized in that: include: A base station for transmitting a radio frequency energy signal during downlink transmission; Intelligent metasurface RIS, used to reflect radio frequency energy signals so that the terminal device can obtain energy after receiving the radio frequency energy signal; 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 used 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; 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.

2. According to claim 1, a RIS-assisted millimeter wave wireless energy transmission network resource optimization system is characterized in that: 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 the second stage, based on the device grouping information obtained in the first stage, the optimal value of the base station's transmit 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 all devices; then the total achievable throughput of the device is maximized by iteratively optimizing the device's transmit power allocation, transmission time slot allocation and RIS phase shift matrix in uplink transmission.

3. A resource optimization method for a RIS-assisted millimeter wave wireless energy transmission network, 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; According to 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.

4. The method for optimizing resources of a RIS-assisted millimeter wave wireless energy transmission network according to claim 3, 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: Where a( ) and a′( ) represent array steering vectors; 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 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; 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.

5. The method for optimizing resources of a RIS-assisted millimeter wave wireless energy transmission network according to claim 3, characterized in that: The energy harvesting model in the downlink transmission is constructed as follows: The base station sends a radio frequency energy signal 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 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: Where 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.

6. The method for optimizing resources of a RIS-assisted millimeter wave wireless energy transmission network according to claim 3, 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: Among them, 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.

7. The method for optimizing resources of a RIS-assisted millimeter wave wireless energy transmission network according to claim 3, characterized in that: The energy harvesting optimization problem is determined according to the constructed model, including: By jointly optimizing the device grouping 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 stage, C1 is the device scheduling variable restriction, C2 indicates that a device can be associated with at most one RF link, C3 indicates the energy beamforming vector constraint for each group, C4 is the total transmit power constraint of the base station, and C6 indicates the unit modulus constraint of the RIS phase shift in wireless power transmission; for the uplink transmission stage, C5 is the transmit power constraint of each device, C7 indicates the unit modulus constraint of the RIS phase shift used for wireless information transmission, and C8 indicates the constraint of the total transmission time slot.

8. The method for optimizing resources of a RIS-assisted millimeter wave wireless energy transmission network according to claim 7, characterized in that: The joint optimization of device grouping, base station transmit power allocation, energy beamforming and RIS phase shift matrix in downlink transmission, and transmit power allocation, RIS phase shift matrix and transmission time slot allocation of devices in uplink transmission includes: 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 the second stage, based on the device grouping information obtained in the first stage, the optimal value of the base station's transmit 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 all devices; then the total achievable throughput of the device is maximized by iteratively optimizing the device's transmit power allocation, transmission time slot allocation and RIS phase shift matrix in uplink transmission.

9. The method for optimizing resources of a RIS-assisted millimeter wave wireless energy transmission network according to claim 8, characterized in that: The first stage specifically includes: The total number of terminal devices in groups is set to be the same as the number of RF links N. Since the mmWave beam has spatial directionality, the grouping principle aims to maximize the correlation between devices in the same group while minimizing the correlation between terminal devices in different groups. The expression of the channel correlation between two terminal devices is: Among them, D i , D j Indicates terminal equipment, g i , g j denote 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 total average normalized channel correlation, the two devices are defined as an exclusion relationship, and the preliminary grouping results are fine-tuned based on the exclusion relationship.

10. The method for optimizing resources of a RIS-assisted millimeter wave wireless energy transmission network according to claim 8, characterized in that: The second stage specifically includes: According to 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, the optimal value of the base station's transmit power allocation, energy beamforming, and RIS phase shift matrix in downlink transmission is obtained through an iterative optimization algorithm, thereby maximizing the total collected energy of the terminal device; 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.

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