IRS-assisted distributed radio frequency energy transmission system and optimization method for improving energy collection efficiency using it

Abstract

The present invention provides an IRS-assisted distributed radio frequency energy transmission system, including an intelligent reflection surface IRS, a distributed radio frequency signal transmitter / base station BS, and a joint information receiving and energy collecting user EU. By forming a passive reflection beam in the intelligent reflection surface IRS, the radio frequency beams from multiple distributed wireless signal transmitters are calibrated to compensate for signal attenuation. At the same time, a maximization optimization method for the above system to improve the minimum energy value obtained in the energy receiver under the premise of ensuring the quality of information transmission is also provided. The method is to jointly design the passive beamforming vector of the IRS and the active beamforming vector of the multi-node distributed radio frequency signal transmitter, so as to cope with the challenge brought by the sharp attenuation of the signal strength during the propagation of the radio frequency wireless signal, and to use the IRS to assist the multi-node distributed radio frequency transmitter to transmit information and energy signals, thereby enhancing the signal strength of the information and energy receiver.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless charging, and in particular relates to an IRS-assisted distributed radio frequency energy transmission system and an optimization method for improving energy collection efficiency using the system. Background Art

[0002] In the 6G IoT scenario, there will be hundreds of millions of low-power devices (e.g., sensors) to sense environmental data. For these frequently used devices, it is inconvenient to frequently replace batteries and wires. To this end, wireless power transmission (SWIPT) technology, especially SWIPT based on radio frequency (RF) transmission, can effectively solve this problem. Therefore, RF-based SWIPT technology has attracted widespread attention from academia and industry.

[0003] However, the surge in demand for wireless energy transmission SWIPT also faces technical problems such as low transmission efficiency caused by various wireless channel impairments, namely distance-based path loss, shadowing, and fading. In this case, it is particularly important to design a method to reduce signal attenuation. Smart reflecting surface (IRS) is a promising technology that forms a passive reflection beam on the smart reflecting surface IRS to calibrate the energy beam from the signal transmitter to compensate for the high attenuation of the signal. At the same time, multi-node distributed RF transmitters can shorten the physical distance between the energy receiver and reduce the transmission loss of wireless energy signals caused by distance. Therefore, how to improve the efficiency of wireless charging based on IRS-assisted multi-node distributed wireless charging technology has become an important research topic. Summary of the invention

[0004] Technical solution: In order to solve the above-mentioned technical problems, the present invention proposes a method for achieving high energy collection efficiency by maximizing the multi-node distributed wireless energy transmission system assisted by an intelligent reflecting surface IRS. That is, with the help of IRS, a joint passive and active RF transmission shaping beam is designed to compensate for the attenuation of the signal in SWIPT and collect effective energy. Its goal is to maximize the minimum collected energy among energy collection users while ensuring the quality of information transmission.

[0005] In order to solve this problem, the present invention transforms the original problem into two easy-to-handle sub-problems by alternately fixing two variables, and proposes an efficient two-layer iterative algorithm to solve the original problem by alternately optimizing the signal beamforming vector and the reflection beamforming vector.

[0006] The present invention proposes an IRS-assisted distributed radio frequency energy transmission system, including an intelligent reflecting surface IRS, a distributed radio frequency signal transmitter / base station BS, and an energy collection and information receiving user EU. By forming a passive reflection beam on the intelligent reflecting surface IRS, the light beams from multiple distributed wireless signal transmitters are calibrated to compensate for signal attenuation, wherein B base stations BS are set. S smart reflectors, denoted as s∈S={1, 2..., S}, and B single-antenna energy and information joint receiving users, denoted as

[0007] Furthermore, each EU is allocated a dedicated signal beam by a BS, thereby realizing B transmission-reception pairs.

[0008] At the same time, the present invention also provides an IRS-assisted distributed radio frequency energy transmission system and an optimization method for improving energy collection efficiency using the same, specifically an optimization method for improving the energy value obtained in the minimum energy receiver based on the IRS-assisted energy transmission system, comprising the following steps:

[0009] (1) constructing the above-mentioned IRS-assisted information transmission and energy harvesting system, and obtaining the received wireless RF energy and signal gain of the user EU end of the system;

[0010] (2) Establish a multi-node distributed wireless energy transmission system model assisted by an intelligent reflector (IRS) and propose an optimization problem to maximize the minimum energy value obtained by the energy receiver;

[0011] (3) Fix the signal beamforming vector and the reflection beamforming vector respectively, and transform the problem in step (2) into two tractable sub-problems;

[0012] (4) The solution is obtained by using an alternating optimization algorithm that jointly designs active beamforming and reflected beamforming.

[0013] Further, in step (1), the receiving wireless radio frequency energy part E of the joint information receiving and energy collection user EU of the system is obtained. k and the signal-to-interference-plus-noise ratio (SINR)γ of the received information decoding part k They are

[0014]

[0015]

[0016] in is the MIMO channel matrix from base station b to EU k, Represented as from IRSs∈S to EU The MISO channel matrix, It is represented as the MIMO channel matrix from base station b to IRSs∈S, H represents the conjugate transpose, θ s，n represents the phase shift of the nth element in the IRS, is the reflection phase shift matrix function, η k represents the energy conversion efficiency of the kth receiver.

[0017] Furthermore, in step (2), an optimization problem of maximizing the minimum collected energy among energy collection users while ensuring the quality of information transmission is proposed:

[0018]

[0019]

[0020]

[0021]

[0022] Among them, E k In order to obtain the received wireless RF energy at the energy harvesting user EU end of the system, the reflection phase shift matrix is the b energy beamforming vector of the base station BS, P b is the maximum transmit power of base station BS b, is the minimum information receiving rate of EU k.

[0023] Further, step (3) comprises the following steps:

[0024] (3.1) Based on the fixed signal beam, the reflection beam is optimized. After simplifying the problem and solving it using the semidefinite relaxation method, the problem is transformed into a convex optimization problem:

[0025]

[0026]

[0027]

[0028] rank(Ω)=1

[0029] |Ω n，n |=1, n∈{1, 2,..., SN+1}

[0030] Ω≥0, where Tr represents the trace function.

[0031] (3.2) Based on the fixed reflection beamforming, the energy beam is optimized and transformed into a convex optimization problem by using the continuous convex approximation method and applying the first-order Taylor expansion and the second-order cone constraint:

[0032]

[0033]

[0034]

[0035]

[0036] in,

[0037] Further, step (4) comprises the following steps:

[0038] (4.1) Initialization, set the initial value of iteration n = 1, and adjust the feasible signal beam vector

[0039] (4.2) loop iteration n = 1, 2, ..., the following loop is performed:

[0040] Step 1: Fix the signal beam vector Solving problem P2.2 yields the optimal reflection phase shift solution:

[0041] and the optimal power E opt1 .

[0042] Step 2: Set the initial value of the iteration t = 1 and the feasible vector Perform the following loop:

[0043] (4.3) Fixed reflection beam vector Based on the updated feasible domain vector Solve Problem P3.2

[0044] The optimal energy beam solution is and the optimal power E opt2 ;

[0045] (4.4) Update local points

[0046] (4.5)t=t+1;

[0047] (4.6) Until it converges to the required accuracy, the inner loop ends;

[0048] (4.7) If the increased objective function value is less than the given threshold, stop the algorithm;

[0049] (4.8)End the loop.

[0050] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention designs a method for maximizing the minimum collected energy among energy collection users in an IRS-assisted multi-node distributed RF wireless energy transmission system. With the help of IRS and a multi-node distributed system architecture, a joint passive and active forming beam is designed to compensate for signal attenuation during wireless transmission, thereby improving the energy collection efficiency of users with minimum energy acquisition. By alternately optimizing the signal beamforming vector and the reflection beamforming vector, an efficient two-layer iterative algorithm is proposed to solve the original problem.

[0051] The present invention is to cope with the challenge brought by the rapid attenuation of signal strength during the propagation of radio frequency wireless signals, and utilizes IRS to assist the signal transmitted by a multi-node distributed radio frequency transmitter, thereby enhancing the signal strength of the energy and information hybrid receiver. By jointly designing the passive beamforming vector of the IRS and the active beamforming vector of the multi-node distributed radio frequency transmitter, the balance of energy acquisition is achieved. The design of the joint beamforming vector involves the solution of a non-convex optimization problem, and for this purpose, an alternating iterative optimization method is proposed, that is, using a semidefinite relaxation method to optimize the reflection beam of the IRS and a continuous convex approximation method to optimize the signal beam. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a system model diagram of the present invention.

[0053] Figure 2 Deployment locations for all BSs, IRSs and EUs.

[0054] Figure 3 Figure 2. Energy obtainable by two EUs in IRS-assisted SWIPT with different numbers of reflective elements.

[0055] Figure 4 Figure 2 shows the achievable energy harvesting size of two EUs in IRS-assisted SWIPT under different maximum power constraints. DETAILED DESCRIPTION

[0056] The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0057] The present invention designs an IRS-assisted distributed radio frequency energy transmission system, that is, an IRS-assisted multi-node distributed radio frequency wireless energy transmission system, and then uses this system to improve the optimization method of maximizing the minimum energy value obtained in the energy receiver while ensuring the quality of information transmission.

[0058] The optimization method specifically includes the following steps:

[0059] Step 1: Construct an IRS-assisted distributed RF energy transmission system, further a multi-node distributed RF wireless energy transmission system assisted by IRS.

[0060] like Figure 1 As shown in the figure, the system consists of an intelligent reflecting surface (IRS), multiple distributed signal transmitters / base stations (BS), and multiple information receiving and energy collection users (EU). By forming a passive reflection beam on the IRS, the beams from multiple distributed wireless signal transmitters are calibrated to compensate for the signal attenuation. Considering a downlink wireless transmission system with B base stations, There are S smart reflectors, denoted as s∈S={1, 2..., S}, and B single-antenna users, denoted as

[0061] The present invention assumes that each EU is assigned a dedicated beam by a BS, i.e., B transmit-receive pairs. Each base station is equipped with M antennas, and each IRS has N passive reflective units. As the direct MISO channel matrix from base station b to EU k, is represented as the MIMO channel matrix from base station b to IRSs∈S, and Represented as from IRSs∈S to EU MISO channel matrix.

[0062] Let x b Expressed as BS b load signal, satisfying and represents the beam vector at base station b. Therefore, the signal transmitted by base station b can be expressed as v b x b , where |v b | 2 ≤P b , and P b is the maximum transmission power of BS b. In IRS, it is assumed that each reflector can adjust the phase of the incident electromagnetic wave with a fixed reflection amplitude (the amplitude is usually set to 1). Let θ s，n represents the phase shift of the nth element in the IRS. Therefore, the reflection phase shift matrix at IRS s is in So the received signal at EU k is:

[0063]

[0064] in Indicates receiving noise.

[0065] Assume that each EU uses the same received signal to decode information and obtain energy by power division. Specifically, for example, for the kth EU, it divides the received signal power into k (0≤ρ k ≤1) part is decoded, and the remaining 1-ρ k Part of the signal power is used to obtain energy. Therefore, the received wireless RF energy at the EU end is expressed as

[0066]

[0067] Phase shift matrix Θ s is a diagonal matrix whose diagonal elements are vectors u s , by definition The effective channel from BS b to EU k with the assistance of IRS s can be written as:

[0068]

[0069] So the received wireless RF energy at the EU end is expressed as:

[0070]

[0071] Signal-to-interference-plus-noise ratio (SINR) of the decoded part of the information received by the EU endγ k They are:

[0072]

[0073] Step 2: Propose an IRS-assisted multi-node distributed RF wireless energy transmission system model and the optimization problem of maximizing the minimum collected energy among energy collection users while ensuring the quality of information transmission.

[0074] With the goal of designing joint passive and active beamforming to compensate for the signal attenuation in SWIPT and improve the energy collection value of the minimum energy collection user, a mathematical optimization model is designed, namely:

[0075]

[0076]

[0077]

[0078]

[0079] Among them, E k In order to obtain the received wireless RF energy at the energy harvesting user EU end of the system, the reflection phase shift matrix is the b energy beamforming vector of the base station BS, Pb is the maximum transmit power of base station BS b, is the minimum information receiving rate of EU k.

[0080] P can be equivalently transformed into the following problem

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] Step 3: Fix the signal beam vector and the reflection phase shift vector respectively, and transform the problem in step (2) into two easy-to-handle sub-problems.

[0087] Since the above problem is non-convex, consider fixing the signal beam vector and the reflection phase shift vector respectively, and transform the original problem into two easy-to-handle sub-problems. The specific steps are as follows:

[0088] (1) Fix the signal beamforming vector and optimize the reflection beamforming vector.

[0089] First, construct subproblem P2 as follows

[0090]

[0091]

[0092]

[0093]

[0094] make d k，s，b =F k，s，b v b , we define the following two vectors

[0095]

[0096]

[0097] So question 2 can be rewritten as:

[0098]

[0099]

[0100]

[0101] ω1=1

[0102] |ω n |=1,n∈{2,...,SN+1}

[0103] The above problem is still a non-convex problem, so the SDR method is used to solve it. rank(Ω)=1, The problem is rewritten as:

[0104]

[0105]

[0106]

[0107] rank(Ω)=1

[0108] |Ω n，n |=1, n∈{1, 2,..., SN+1}

[0109] Ω≥0

[0110] (2) Similarly, the reflection beamforming vector is fixed and the signal beamforming vector is optimized.

[0111] By fixing the reflection phase shift vector, We consider the following subproblem P3

[0112]

[0113]

[0114]

[0115] ||v b || 2 ≤P b , b∈B

[0116] make So question 3 can be rewritten as

[0117]

[0118]

[0119]

[0120] ||v b || 2 ≤P b , b∈B

[0121] By order In the feasible vector The first-order Taylor expansion at is:

[0122]

[0123] The second constraint is transformed into a second-order cone constraint:

[0124]

[0125] So problem P3.1 is transformed into P3.2:

[0126]

[0127]

[0128]

[0129]

[0130] Problem P3.2 is a convex optimization problem and can be solved using CVX.

[0131] Step 4: Adopt an alternating optimization algorithm for the joint design of active beamforming vectors and reflected beamforming vectors.

[0132] The specific steps are as follows:

[0133] (4.1) Initialization, set the initial value of iteration n = 1, and adjust the feasible signal beam vector

[0134] (4.2) loop iteration n = 1, 2, ..., the following loop is performed:

[0135] Step 1: Fix the signal beam vector Solving problem P2.2 yields the optimal reflection phase shift solution:

[0136] and the optimal power E opt1 .

[0137] Step 2: Set the initial value of the iteration t = 1 and the feasible vector Perform the following loop:

[0138] (4.3) Fixed reflection beam vector Based on the updated feasible domain vector Solve Problem P3.2

[0139] The optimal energy beam solution is and the optimal power E opt2 ;

[0140] (4.4) Update local points

[0141] (4.5)t=t+1;

[0142] (4.6) Until it converges to the required accuracy, the inner loop ends;

[0143] (4.7) If the increased objective function value is less than the given threshold, stop the algorithm;

[0144] (4.8)End the loop.

[0145] In the Matlab software simulation, it is assumed that S = 2 IRSs are deployed between B = 2 BSs and K = 2 EUs. All devices are placed on a two-dimensional coordinate plane. The positions of the two BSs are (10m, 80m) and (110m, 65m), and the two EUs are located at (10m, 0) and (90m, 0). The positions of the two IRSs are located in the middle of the BSs and EUs, which are (40m, 20m) and (125m, 20m). Assuming that the path loss index of the BS-IRS and IRS-EU channels are 2.2 and 2.4, respectively, which are lower than the path loss index of 3.5 for the BS-EU channel, the maximum transmission power P b Set to 30dBm, the number of passive reflection elements of IRS is N=64.

[0146] Figure 2 For the deployment locations of all BSs, IRSs, and EUs, all devices are placed on a two-dimensional coordinate plane.

[0147] Figure 3 The energy that can be obtained by two EUs in IRS-assisted WPT when setting different numbers of reflective elements.

[0148] from Figure 3 It can be seen that compared with optimizing the beam at the base station only, the IRS-assisted WPT using this design can obtain more energy. When the number of passive reflection units increases from 32 to 64, the energy that can be obtained by the auxiliary IRS with 64 reflection units is greater than that of the auxiliary IRS with 32 reflection units. It can be seen that the more reflection units deployed in the IRS, the stronger the electromagnetic waves reflected to the EU.

[0149] Figure 4 To illustrate the achievable energy harvesting of two EUs in IRS-assisted WPT under different maximum power constraints. Figure 4 The energy that can be obtained by two EUs in IRS-assisted WPT is shown. When the maximum power increases, the obtainable energy increases significantly.

[0150] The advantage of the method of the present invention is that it can better solve the problem of joint energy and reflection beam forming, fully enhance the IRS to help improve the efficiency of energy collection.

[0151] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An optimization method for improving energy collection efficiency based on an IRS-assisted distributed radio frequency energy transmission system, characterized in that: The following steps are involved: (1) Construct an IRS-assisted distributed RF energy transmission system and obtain the received wireless RF energy and signal gain of the user EU end of the system; (2) Establish a multi-node distributed wireless energy transmission system model assisted by an intelligent reflector (IRS) and propose an optimization problem to maximize the minimum energy value obtained by the energy receiver; (3) Fix the signal beamforming vector and the reflection beamforming vector respectively, and transform the problem in step (2) into two tractable sub-problems; (4) Solve using an alternating optimization algorithm that combines active beamforming and reflected beamforming; In step (1), the receiving wireless RF energy part E of the joint information receiving and energy collection user EU of the system is obtained. k and the signal-to-interference-to-noise ratio γ of the received information decoding part k They are: in is the MIMO channel matrix from base station b to EU k, Represented as from IRSs∈S to EU The MISO channel matrix, It is represented as the MIMO channel matrix from base station b to IRSs∈S, H represents the conjugate transpose, θ s，n represents the phase shift of the nth element in the IRS, is the reflection phase shift matrix function, η k represents the energy conversion efficiency of the kth receiver, ρ k Indicates the signal power allocation ratio with a value of (0,1), u s is the reflection phase shift vector of IRSs, v b is the b energy beamforming vector of the base station BS, and represents the noise power; In step (2), an optimization problem is proposed to maximize the minimum collection energy among energy collection users while ensuring the quality of information transmission: P： Among them, E k To obtain the received wireless RF energy at the energy harvesting user EU of the system, the reflected phase shift vector is the energy beamforming vector of base station BSb, P b is the maximum transmit power of base station BS b, is the minimum information receiving rate of EUk; Step (3) comprises the following steps: (3.1) Based on the fixed signal beam, the reflection beam is optimized. After simplifying the problem and solving it using the semidefinite relaxation method, the problem is transformed into a convex optimization problem: R%aZ: rank(Ω)=1 |Ω n，n |1,n∈{1,2,...,SN+1} Ω≥0 in, Tr represents the trace function; E represents the lower bound of the collected energy value of all energy collection users, and SN represents the number of elements of all smart emission surfaces; (3.2) Based on the fixed reflection beamforming, the energy beam is optimized and transformed into a convex optimization problem by using the continuous convex approximation method and applying the first-order Taylor expansion and the second-order cone constraint: P3.2: in, t represents the number of iterations of the algorithm, Re means taking the real part of a complex number. represents the energy beamforming vector of base station BSb at the t-1th iteration of the algorithm; Step (4) comprises the following steps: (4.1) Initialization, set the initial value of iteration n = 1, and adjust the feasible signal beam vector (4.2) loop iteration n = 1, 2, ..., the following loop is performed: Step 1: Fix the signal beam vector Solving problem P2.2 yields the optimal reflection phase shift solution: and the optimal power E opt1 ; Step 2: Set the initial value of the iteration t = 1 and the feasible vector Perform the following loop: (4.3) Fixed reflection beam vector Based on the updated feasible domain vector Solving problem P3.2, we get the optimal energy beam solution: and the optimal power E opt2 ; (4.4) Update local points (4.5)t=t+1; (4.6) Until it converges to the required accuracy, the inner loop ends; (4.7) If the increased objective function value is less than the given threshold, stop the algorithm; (4.8)End the loop.

2. The optimization method for improving energy collection efficiency based on the IRS-assisted distributed radio frequency energy transmission system according to claim 1 is characterized in that: In step 1, based on the IRS-assisted distributed RF energy transmission system, including the intelligent reflecting surface IRS, the distributed RF signal transmitter / base station BS, and the joint information receiving and energy collection user EU, the RF beams from multiple distributed wireless signal transmitters are calibrated by forming a passive reflection beam on the intelligent reflecting surface IRS to compensate for the attenuation of the signal, wherein B base stations BS are set. There are S smart reflectors, denoted as s∈S={1, 2, ..., S}, and B single-antenna users, denoted as 3. The optimization method for improving energy collection efficiency based on the IRS-assisted distributed radio frequency energy transmission system according to claim 2 is characterized in that: Each EU is assigned a dedicated signal beam by a BS to achieve B transmission and reception pairs.

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