Passive Internet of Things performance enhancement method for STAR-RIS auxiliary transmission

Abstract

The invention provides a STAR-RIS auxiliary transmission passive Internet of Things performance enhancement method, and aims to solve the problems of half-space limitation and low energy and information transmission efficiency of an RIS auxiliary passive Internet of Things. According to the invention, the STAR-RIS is deployed between the passive BDs and the access point to assist the BDs to transmit information to the access point, so that the total space distribution of the BDs is realized. In addition, beamforming is constructed at an energy station configured with multiple antennas to improve energy transmission efficiency. The invention provides an iterative algorithm based on a double-layer frame, and aims to solve the problem of joint optimization of a beam forming vector based on NOMA and an STAR-RIS element coefficient, and maximize the system sum rate, thereby remarkably improving the performance of a passive Internet of Things system.

Description

Technical Field

[0001] The invention relates to the field of Internet of Things, and in particular to a method for enhancing the performance of a passive Internet of Things with STAR-RIS assisted transmission. Background Art

[0002] With the development of the Internet of Things (IoT), wireless devices are widely deployed in various fields of production and life. However, the limited battery capacity of wireless devices limits their service life. Although the service life of wireless devices can be extended by replacing or recharging the batteries, the networking cost increases. Especially in the future sixth generation (6G, 6th Generation) mobile communications, a large number of wireless devices will be connected to the network, and the networking cost will increase sharply, which has become a major bottleneck in the development of IoT. In recent years, passive IoT based on backscatter communication (BackCom, BackscatterCommunication) technology is gradually emerging. In the passive IoT, the passive device (BD, Batteryless Device) based on BackCom technology does not need to be equipped with batteries, but uses the radio frequency (RF, Radio Frequency) signal energy in the environment to meet its required energy consumption, and reflects the instantaneous incident signal by regularly adjusting the load impedance to achieve information transmission. This feature makes the passive IoT expected to break through the bottlenecks faced by the development of the above-mentioned IoT. However, BD has the defects of small transmission range and high path loss, which limits the communication range and transmission efficiency of the passive IoT.

[0003] As a key technology for future 6G communications, Reconfigurable Intelligent Surface (RIS) can intelligently control the phase and amplitude of signals to achieve channel control. It can be used in passive IoT and assist in inter-device communication, thereby increasing network communication range and information transmission efficiency. Traditional RIS has a half-space limitation, that is, BD can only be deployed in the reflection area or transmission area of ​​RIS, which is difficult to meet the needs of BD that may be distributed in the entire space in practical applications.

[0004] Simultaneously Transmitting and Reflecting RIS (STAR-RIS) is an extension technology of RIS, which can overcome the half-space limitation of RIS. STAR-RIS divides the incident signal into two parts: the reflected signal and the transmitted signal. The reflected signal is reflected to the same space as the incident signal, namely the reflection space. The transmitted signal is transmitted to the opposite space, namely the transmission space. Based on the excellent performance of STAR-RIS, the present invention applies STAR-RIS to a passive Internet of Things based on non-orthogonal multiple access (NOMA) to improve system performance.

[0005] The technical comparison and differences with patent CN 118523799A_A RIS enhanced full-duplex backscatter communication network resource allocation method are as follows;

[0006] 1. The method adopted in CN 118523799A;

[0007] Patent CN 118523799 A proposes a RIS-enhanced full-duplex backscatter communication network resource allocation method, which uses RIS to implement auxiliary communication in a full-duplex backscatter communication network.

[0008] 1. Construct a RIS-enhanced full-duplex backscatter communication system model, and establish a resource allocation optimization problem to minimize the full-duplex base station transmit power in this communication system model;

[0009] 2. The resource allocation optimization problem of minimizing the full-duplex base station transmit power is split into the full-duplex base station beamforming vector subproblem, the RIS uplink transmission beamforming matrix subproblem and the RIS downlink transmission beamforming matrix subproblem;

[0010] 3. Use variable substitution and semi-positive relaxation methods to solve the full-duplex base station beamforming vector quantum problem and solve the optimal full-duplex base station beamforming vector transmitted to the reflection node;

[0011] 4. The semi-definite relaxation and semi-definite programming methods are used to deal with the RIS uplink transmission beamforming matrix subproblem and the RIS downlink transmission beamforming matrix subproblem respectively, and the optimal phase shift parameters of the RIS uplink transmission and RIS downlink transmission processes are obtained respectively.

[0012] The RIS technology used in this method can significantly improve the spectrum efficiency and energy efficiency of wireless communications by controlling the reflection phase of wireless signals, and can assist communication between devices, expand network coverage, and improve information transmission efficiency. However, the RIS used in this method is a traditional RIS, which is limited to half space because it can only reflect or transmit. That is, the reflection node can only be deployed in the reflection or transmission area of ​​RIS, which is difficult to meet the actual needs of the full space distribution of the reflection node.

[0013] 2. Method of the present invention

[0014] The present invention proposes a passive IoT performance enhancement method with STAR-RIS assisted transmission. STAR-RIS is deployed between the reflection node and the access point to assist the reflection node in transmitting information to the access point, thereby achieving full spatial distribution of the reflection node. The core innovations of the method are:

[0015] Achieve full-space distribution: In order to improve the flexibility of network deployment and the communication efficiency from reflection nodes to access points, a STAR-RIS-assisted passive IoT system model was constructed; in order to improve the energy transmission efficiency of energy stations, energy stations equipped with multiple antennas use beamforming technology to send radio frequency signals.

[0016] 3. Comparison of technical advantages

[0017] Flexibility of spatial distribution: The present invention adopts STAR-RIS assisted communication, which can overcome the half-space limitation of traditional RIS and has the ability of full-space coverage. In contrast, the method in CN 118523799 A adopts traditional RIS assisted communication, and the reflection node can only be located in the reflection or transmission area of ​​RIS, which has a large distribution limitation.

[0018] In summary, the STAR-RIS assisted transmission passive Internet of Things performance enhancement method proposed in the present invention is superior to CN 118523799 A in terms of the flexibility of spatial distribution of reflection nodes, and can more effectively improve network performance. Summary of the invention

[0019] In order to solve the above technical problems, the present invention proposes a passive Internet of Things performance enhancement method for STAR-RIS assisted transmission, which aims to solve the problems of half-space limitation and low energy and information transmission efficiency of RIS assisted passive Internet of Things.

[0020] To achieve the above object, the technical solution adopted by the present invention is:

[0021] A method for enhancing the performance of a passive Internet of Things with STAR-RIS assisted transmission, the specific steps are as follows;

[0022] 1) Obtain uplink channel data, beamforming vector at the energy station, reflection coefficient of passive equipment based on backscatter communication technology, and coefficient matrix of simultaneous transmission and reflection RIS in the passive Internet of Things network; BDs in the coefficient matrix is ​​U i , i=1,…,N is a set of N BDs, where i and N are pre-set values ​​and i≤N;

[0023] 2) Calculate the system and rate based on the data obtained above;

[0024] 3) By calculating the optimal solution of the beamforming vector and the coefficient matrix of STAR-RIS at the energy station, the performance enhancement system and rate of the passive Internet of Things assisted by STAR-RIS transmission are maximized.

[0025] Furthermore, the uplink channel data in the step 1) includes channel data from the energy station to BDs, channel data from BDs to STAR-RIS, and channel data from STAR-RIS to the access point; in the passive Internet of Things performance enhancement system with STAR-RIS assisted transmission, BDs and access points cannot communicate directly due to obstacles, and must communicate through STAR-RIS assistance; the energy station is equipped with M antennas, the BDs and access points are both equipped with a single antenna, the STAR-RIS includes L elements, and N, M and L are all preset values.

[0026] Furthermore, the BDs in the coefficient matrix of step 1) are all equipped with BackCom circuits to backscatter the radio frequency signals from the energy station and thereby transmit the information to be sent; the BDs are U i , i = 1, ..., N are distributed in the reflection area of ​​STAR-RIS, and the backscattered signal is reflected by STAR-RIS to the access point; the BDs is U i ,i=N1+1,…,N are distributed in the transmission area of ​​STAR-RIS, and the backscattered signals are transmitted to the access point through STAR-RIS.

[0027] Furthermore, the STAR-RIS operates in an energy splitting protocol, and the signal incident on the STAR-RIS is split into a transmission signal and a reflection signal. The transmission coefficient and reflection coefficient corresponding to the lth element of the STAR-RIS are respectively expressed as and in, and is the magnitude of the corresponding coefficient, and is the phase shift of the corresponding coefficient. According to the law of conservation of energy, the amplitude parameter should satisfy the equality constraint: in

[0028] Furthermore, in the step 3), when the passive IoT performance enhancement system adopts a non-orthogonal multiple access method, the access point first decodes the signal from the BD with a higher channel gain, that is, U j with U i The corresponding cascade channel gain satisfies: |g s,i |>|g s,j |, then U j The signal will be in U i After decoding the signal, the passive IoT performance enhancement system has the following inequality relationship established:

[0029] |g s,1 |>|g s,2 |>,…,>|g s,N-1 |>|g s,N |,

[0030] That is, the decoding order of the signal of the i-th BD at the access point is i;

[0031] According to the NOMA protocol, the passive IoT performance enhancement system i The SINR is:

[0032]

[0033] Among them, ρ i For U i The reflection coefficient, g s,i For U i Cascade channel with access point, h p,u For energy stations and U i The channel between them, w is the energy beamforming vector, is the power of the additive white Gaussian noise at the access point, the superscript H represents the Hermitian conjugate, and |·| represents the modulo operation of the complex number;

[0034] The energy beamforming vector in the passive IoT performance enhancement system is limited to: ||w|| 2 ≤P H , where P H is the maximum transmission power of the energy station;

[0035] The passive IoT performance enhancement system i The achievable rate to the access point is:

[0036]

[0037] Furthermore, the optimal solution for calculating the beamforming vector at the energy station and the coefficient matrix of STAR-RIS in step 3) specifically includes:

[0038] Based on the channel data from the energy station to the BDs Channel data from BDs to STAR-RIS u,r , channel data from STAR-RIS to access point Beamforming vectors w, U at the energy station i The reflection coefficient ρ i , the coefficient matrix Θ of STAR-RIS s , establish the solution system and rate maximization problem P0, which is expressed as:

[0039]

[0040] st C1:||w|| 2 ≤P H ,

[0041] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0042]

[0043] in, and are the amplitudes of the transmission coefficient and reflection coefficient corresponding to the 1st element of STAR-RIS, and is the phase shift of the corresponding coefficient, U i Cascade channel with access point

[0044] Coefficient matrix of STAR-RIS elements When s=r, it means that BD is located in the R region of STAR-RIS, and when s=t, it means that BD is located in the T region of STAR-RIS; is the complex exponential part;

[0045] Transform the objective function of the optimization problem P0:

[0046]

[0047] The equality at (a) is due to the existence of split products in the expressions of the system and rate. Since the logarithmic function log2(.) is a monotonically increasing function, Maximizing is equivalent to Find the maximum value, so the optimization problem P0 can be equivalently transformed into:

[0048]

[0049] st C1:||w|| 2 ≤P H ,

[0050] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0051]

[0052] In order to facilitate the solution of the optimization problem, the objective function is further transformed:

[0053]

[0054] in Introducing auxiliary variables in Rank(Q s )=1,diag(Q s )=β s , similarly, introduce the auxiliary variable W = ww H , where Rank(W)=1, Tr(W)≤P H ;

[0055] In summary, the optimization problem P1 can be transformed into:

[0056]

[0057] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0058]

[0059] C4:diag(Q s )=β s ,

[0060] C5:Rank(Q s )=1,s∈{t,r},

[0061] C6:Rank(W)=1,

[0062] C7:Q s 0,W 0,

[0063] The Tr(Q s hh H) can be converted into the following form:

[0064]

[0065] At the same time, rewrite constraint C5 into the following equivalent constraint:

[0066]

[0067] The optimization problem P2 is solved by the penalty function method, and the optimization problem P2 is transformed into the following problem:

[0068]

[0069] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0070]

[0071] C4:diag(Q s )=β s ,

[0072] C6:Rank(W)=1,

[0073] C7:Q s 0,W 0,

[0074] Among them, W is the auxiliary variable introduced W = ww H ,||·|| * represents the nuclear norm of the matrix, and ||·||2 represents the spectral norm of the matrix;

[0075] Solve passive IoT performance enhancement systems and rate maximization problems, including:

[0076] Step 1: Use semi-positive definite relaxation and successive convex approximation method to solve the problem P3 at the nth iteration of the penalty function method and obtain the optimal solution at the nth iteration of the penalty function method;

[0077] Step 2: Repeat step 1 until the penalty function term in the objective function is less than the threshold value, and obtain the optimal solution w of the final optimization problem * ,

[0078] Furthermore, the problem P3 of the nth iteration of the penalty function method of the optimization problem P2 is solved by using semi-positive definite relaxation and successive convex approximation method, including:

[0079] For problem P3, the successive convex approximation method is used. In the nth iteration of the SCA method, given point Through the one-stage Taylor expansion, the upper bound of the penalty function term can be obtained. In the nth iteration of the SCA method, given point and W (n) , get Υ i The convex lower bound of :

[0080]

[0081] So, given W (n) , the optimization problem P3 can be further transformed into:

[0082]

[0083] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0084]

[0085] C4:diag(Q s )=β s ,

[0086] C6:Rank(W)=1,

[0087] C7:Q s 0,W 0,

[0088] Since constraint C6 is a rank 1 constraint, P4 is still a non-convex optimization problem. Therefore, SDR technology is used to relax C6. After relaxing constraint C6, the optimization problem P4 is a standard SDP problem, which is solved using the CVX tool.

[0089] Compared with the prior art, the present invention has the following beneficial effects:

[0090] The passive IoT performance optimization method for STAR-RIS assisted transmission enables BDs to be fully distributed in space by deploying STAR-RIS between BDs and access points. In addition, beamforming is designed at energy stations equipped with multiple antennas to improve energy transmission efficiency, and the phase shift and amplitude joint optimization of STAR-RIS elements are considered, effectively improving the information transmission efficiency from BDs to access points. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 is a curve diagram showing the relationship between the system and rate and the change of the energy station transmission power according to the first embodiment of the present invention;

[0092] Figure 2is a curve diagram showing the relationship between the system and rate and the number of energy station antennas provided in the first embodiment of the present invention;

[0093] Figure 3 is a curve diagram showing the relationship between the system and rate and the number of STAR-RIS components provided by the first embodiment of the present invention;

[0094] Figure 4 It is a model diagram of a passive Internet of Things performance enhancement device for STAR-RIS assisted transmission provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0095] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0096] Embodiment 1:

[0097] The present invention provides a STAR-RIS assisted transmission passive Internet of Things performance enhancement method in the field of Internet of Things, comprising the following steps:

[0098] In the passive IoT system, STAR-RIS is deployed between BDs and access points, BDs is a collection of multiple passive devices, and STAR-RIS is a reconfigurable smart reflective surface that transmits and reflects simultaneously;

[0099] The BDs are all equipped with BackCom circuits, which can backscatter RF signals from the energy station and thereby transmit the information they want to send; the BDs are distributed in the reflection area (R area) of STAR-RIS, and the signals backscattered by them are reflected by STAR-RIS to the access point; the BDs are distributed in the transmission area (T area) of STAR-RIS, and the signals backscattered by them are transmitted by STAR-RIS to the access point;

[0100] Acquire uplink channel data, beamforming vectors at energy stations, reflection coefficients of BDs based on BackCom technology, and coefficient matrices of STAR-RIS in a passive IoT network; the BDs is a set of N BDs, where N is a preset value;

[0101] Based on the data obtained above, calculate the system and rate;

[0102] The optimal solution of the beamforming vector and the coefficient matrix of STAR-RIS at the energy station is calculated to maximize the system sum rate.

[0103] Specifically, the uplink channel data includes channel data from the energy station to BDs, channel data from BDs to STAR-RIS, and channel data from STAR-RIS to the access point; in the system, BDs and access points cannot communicate directly due to obstacles, and must communicate through STAR-RIS.

[0104] Specifically, Figure 4 As shown in the figure, a system model is established. The network consists of an energy station with sufficient energy supply, STAR-RIS, an access point and N BDs (U i , i=1,…,N). The energy station is equipped with M antennas, the access point and BD are equipped with a single antenna, and STAR-RIS contains L elements, where N, M and L are all pre-set values. The channel data from the energy station to BDs is The channel data from BDs to STAR-RIS is g u,r , the channel data from STAR-RIS to the access point is

[0105] Specifically, the STAR-RIS works in the ES protocol, and the signal incident to the STAR-RIS is divided into two parts: a transmission signal and a reflection signal. The transmission coefficient and reflection coefficient corresponding to the lth element of the STAR-RIS are respectively expressed as and in, and is the magnitude of the corresponding coefficient, and is the phase shift of the corresponding coefficient. According to the law of conservation of energy, the amplitude parameter should satisfy the equality constraint: in

[0106] The signal sent by the energy station to the BDs is:

[0107] x(t)=ws(t),

[0108] Where w is the energy beamforming vector and satisfies ||w|| 2 ≤P H , P H is the maximum transmission power of the energy station, and s(t) is the pre-generated unit power sequence. i The signal received from the energy station can be expressed as:

[0109]

[0110] Among them, n i,u Indicates U i Additive white Gaussian noise at ;

[0111] U i The backscattered signal can be expressed as:

[0112]

[0113] Among them, ρ i For U i The reflection coefficient of i ≤1, c i (t) is U i The signal to be sent and meets denotes statistical expectation; the superscript H denotes Hermitian conjugation;

[0114] For convenience, let U i The cascade channel between the access point and the access point is g s,i , which can be expressed as:

[0115]

[0116] in, The coefficient matrix represents the STAR-RIS element. When s=r, it means that BD is located in the R region of STAR-RIS. When s=t, it means that BD is located in the T region of STAR-RIS. is the complex exponential part;

[0117] When the system adopts NOMA mode, all BDs transmit information to the access point at the same time with the assistance of STAR-RIS. The signal received by the access point can be expressed as:

[0118]

[0119] Among them, n r,a is the additive white Gaussian noise at the access point. In general, The power ratio noise n r,a The power of is much smaller, so it is ignored for the convenience of study;

[0120] For the uplink NOMA system, the access point often decodes the signal from the BD with higher channel gain first. j with U i The corresponding cascade channel gain satisfies: |g s,i |>|g s,j |, then U j The signal will be in U i The signal is decoded after that. In the system, the following inequality relationship holds true:

[0121] |g s,1 |>|gs,2 |>,…,>|g s,N-1 |>|g s,N |,

[0122] That is, the decoding order of the signal of the i-th BD at the access point is i;

[0123] According to the NOMA protocol, U i The SINR is:

[0124]

[0125] Among them, |·| represents the modulus operation of complex numbers;

[0126] In the system, U i The achievable rate to the access point is:

[0127]

[0128] In one embodiment, based on the channel data from the energy station to the BDs Channel data from BDs to STAR-RIS u,r , channel data from STAR-RIS to access point Beamforming vectors w, U at the energy station i The reflection coefficient ρ i , the coefficient matrix Θ of STAR-RIS s , establish the solution system and rate maximization problem P0, which is expressed as:

[0129]

[0130] st C1:||w|| 2 ≤P H ,

[0131] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0132]

[0133] in, and are the amplitudes of the transmission coefficient and reflection coefficient corresponding to the 1st element of STAR-RIS, and is the phase shift of the corresponding coefficient;

[0134] Specifically, the objective function of the optimization problem P0 is transformed:

[0135]

[0136] The equality at (a) is due to the existence of a split product in the expression of the system and rate. Since the logarithmic function log2(.) is a monotonically increasing function, Maximizing is equivalent to Find the maximum value. Therefore, the optimization problem P0 can be equivalently transformed into:

[0137]

[0138] st C1:||w|| 2 ≤P H ,

[0139] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0140]

[0141] In order to facilitate the solution of the optimization problem, the objective function is further transformed:

[0142]

[0143] in Introducing auxiliary variables in Rank(Q s )=1,diag(Q s )=β s Similarly, we introduce the auxiliary variable W = ww H , where Rank(W)=1, Tr(W)≤P H .

[0144] In summary, the optimization problem P1 can be transformed into:

[0145]

[0146] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0147]

[0148] C4:diag(Q s )=β s ,

[0149] C5:Rank(Qs )=1,s∈{t,r},

[0150] C6:Rank(W)=1,

[0151] C7:Q s 0,W 0, optimize the Tr(Q s hh H ) can be converted into the following form:

[0152]

[0153] At the same time, rewrite constraint C5 into the following equivalent constraint:

[0154]

[0155] The optimization problem P2 is solved by the penalty function method, and the optimization problem P2 is transformed into the following problem:

[0156]

[0157] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0158]

[0159] C4:diag(Q s )=β s ,

[0160] C6:Rank(W)=1,

[0161] C7:Q s 0,W 0,

[0162] Among them, W is the auxiliary variable introduced W = ww H ,||·|| * represents the nuclear norm of the matrix, and ||·||2 represents the spectral norm of the matrix.

[0163] Solving the system and rate maximization problem includes:

[0164] Step 1: Use semi-positive definite relaxation and successive convex approximation method to solve the problem P3 at the nth iteration of the penalty function method and obtain the optimal solution at the nth iteration of the penalty function method;

[0165] Step 2: Repeat step 1 until the penalty function term in the objective function is less than the threshold value, and obtain the optimal solution w of the final optimization problem * ,

[0166] Specifically, the successive convex approximation method is used for problem P3. In the nth iteration of the SCA method, given point Through a one-stage Taylor expansion, the upper bound of the penalty function term can be obtained. In the nth iteration of the SCA method, given point and W (n) , you can get Υ i The convex lower bound of :

[0167]

[0168] So, given W (n) , the optimization problem P3 can be further transformed into:

[0169]

[0170] C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1,

[0171]

[0172] C4:diag(Q s )=β s ,

[0173] C6:Rank(W)=1,

[0174] C7:Q s 0,W 0,

[0175] Since constraint C6 is a rank 1 constraint, P4 is still a non-convex optimization problem. Therefore, SDR technology is used to relax C6. After relaxing constraint C6, the optimization problem P4 is a standard SDP problem, which is solved using the CVX tool.

[0176] In one embodiment, three schemes are used for performance comparison: 1. Traditional RIS scheme: In this scheme, a reflection-only RIS and a transmission-only RIS together form a hybrid RIS. In order to ensure the fairness of the comparative analysis, the number of elements of the reflection-only RIS and the transmission-only RIS is set to L / 2 (assuming L is an even number); 2. Random amplitude scheme: In this scheme, the amplitude of the STAR-RIS element is randomly generated and satisfies 3. Fixed amplitude scheme: In this scheme, the amplitude of the STAR-RIS element is fixed and meets β t,l =0.6,β r,l =0.4.

[0177] The simulation scenario is established in a two-dimensional rectangular coordinate system. The coordinates of the energy station, STAR-RIS and access point are: (0,0) m, (4.5,2) m and (7.5,-0.5) m respectively. There are 4 BDs, located in the reflection area and the transmission area respectively. Among them, the coordinates of the two BDs located in the reflection area are: (3.2,0.5) m and (5.7,0.5) m respectively, and the coordinates of the two BDs located in the transmission area are: (3.1,3.5) m and (5.8,3.5) m respectively. The large-scale fading model is: C0(d m,n / d0) -α , where C0 = (ζ / (4π)) 2 is the path loss at the reference distance d0 = 1 meter. ζ is the carrier wavelength, which corresponds to a frequency of 75 MHz. m,n is the distance between devices m and n. The small-scale fading of the channel between BDs and STAR-RIS and the channel between STAR-RIS and the access point adopts Rice fading, and the channel can be expressed as follows:

[0178]

[0179] Among them, d u,r and d r,a They represent the distances between BD and STAR-RIS, and between STAR-RIS and the access point, respectively. The path losses corresponding to the two channels are α u,r and α r,a Both are set to 2.2, and the Rice fading coefficients of the two channels are K u,r and K r,a Both are set to 3. and The determined line-of-sight portions of the two channels respectively. and are the random non-line-of-sight parts of the two channels, respectively, which adopt the Rayleigh fading model;

[0180] Unless otherwise specified, other parameters are set as follows: The maximum transmission power P of the energy station H Set to 15W, the reflection coefficient of BD is ρ i Set to 1 to access the noise power at the access point The path loss C0 at the reference distance of 1 meter is set to -10dB, and the initial value η of the penalty function coefficient is set to 10 -4 , the penalty function coefficient growth step ω is set to 10.

[0181] Figure 1The relationship curve between the system sum rate and the energy station transmission power is shown; as shown in the figure, the system sum rate of all schemes will increase with the increase of the energy station transmission power. This is because the greater the transmission power of the energy station, the greater the power of the BDs to backscatter the information it wants to send, thereby improving the communication performance between BDs and STAR-RIS. Figure 1 It can also be seen that the performance of the STAR-RIS scheme is better than that of the amplitude random scheme and the fixed amplitude scheme. This is because the STAR-RIS scheme optimizes the amplitude and phase of the element at the same time, while the amplitude random scheme and the fixed amplitude scheme only optimize the phase of the element; the traditional RIS scheme has the worst performance because the traditional RIS uses a fixed number of transmission and reflection elements, so it has lower degrees of freedom compared with other schemes.

[0182] Figure 2 The relationship curve of system sum rate with the number of energy station antennas is shown; as shown in the figure, as the number of energy station antennas increases, the system sum rate of all schemes increases. This is mainly because the increase in the number of antennas brings more diversity, thereby improving the energy transmission efficiency from the energy station to the BDs. Figure 2 It can also be seen that the performance of the STAR-RIS solution is always better than other reference solutions.

[0183] Figure 3 The relationship between the system sum rate and the number of STAR-RIS elements is shown; as shown in the figure, as the number of STAR-RIS elements increases, the system sum rate of all schemes increases. This is because the increase in the number of STAR-RIS elements will bring greater transmission and reflection beamforming gain, thereby improving the information transmission efficiency between STAR-RIS and BDs.

[0184] The present invention deploys STAR-RIS between BDs and access points, so that BDs can be distributed in the entire space. In addition, by designing beamforming at an energy station equipped with multiple antennas to improve energy transmission efficiency, and considering the phase shift and amplitude joint optimization of STAR-RIS elements, the information transmission efficiency from BDs to access points is effectively improved.

[0185] Embodiment 2:

[0186] The present invention provides a passive Internet of Things performance enhancement device for STAR-RIS assisted transmission, the device comprising:

[0187] Acquisition module: Acquire uplink channel data, beamforming vectors at energy stations, reflection coefficients of BDs based on BackCom technology, and coefficient matrix of STAR-RIS in the passive IoT network; the BDs is a set of N BDs, where N is a preset value;

[0188] Calculation module: calculates the system and rate based on the above acquired data;

[0189] Optimization module: Calculates the optimal solution of the beamforming vector and the coefficient matrix of STAR-RIS at the energy station to maximize the system and rate.

[0190] Embodiment three:

[0191] An embodiment of the present invention provides a terminal, including a processor and a storage medium;

[0192] The storage medium is used to store instructions;

[0193] The processor is used to operate according to the instructions to execute the steps of the method described in embodiment 1.

[0194] The processor may include an entity end and / or a service end.

[0195] like Figure 4 As shown, when executing the steps of the method described in Example 1, by setting STAR-RIS between BDs and the access point, STAR-RIS uses the ES protocol to divide the signal incident to STAR-RIS into two parts: a transmission signal and a reflection signal, so that BDs can achieve full spatial distribution. The simulation results show that the present invention can greatly improve the performance of the passive Internet of Things.

[0196] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0197] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. A method for enhancing the performance of a passive Internet of Things with STAR-RIS assisted transmission, characterized in that: The specific steps are as follows; 1) Obtain uplink channel data, beamforming vector at the energy station, reflection coefficient of passive equipment based on backscatter communication technology, and coefficient matrix of simultaneous transmission and reflection RIS in the passive IoT network; BDs in the coefficient matrix is ​​U i , i=1,…,N is a set of N BDs, where i and N are pre-set values ​​and i≤N; 2) Calculate the system and rate based on the data obtained above; 3) By calculating the optimal solution of the beamforming vector and the coefficient matrix of STAR-RIS at the energy station, the performance enhancement system and rate of the passive Internet of Things assisted by STAR-RIS transmission are maximized.

2. The method for enhancing the performance of a passive Internet of Things with STAR-RIS assisted transmission according to claim 1, characterized in that: The uplink channel data in step 1) includes channel data from the energy station to BDs, channel data from BDs to STAR-RIS, and channel data from STAR-RIS to access points; in the passive IoT performance enhancement system with STAR-RIS assisted transmission, BDs and access points cannot communicate directly due to obstacles, and must communicate through STAR-RIS; the energy station is equipped with M antennas, the BDs and access points are both equipped with a single antenna, the STAR-RIS includes L elements, and N, M and L are all preset values.

3. The method for enhancing the performance of a passive Internet of Things with STAR-RIS assisted transmission according to claim 1, characterized in that: The BDs in the coefficient matrix of step 1) are all equipped with BackCom circuits, which backscatter the radio frequency signals from the energy station and transmit the information they want to send; the BDs are U i , i = 1, ..., N are distributed in the reflection area of ​​STAR-RIS, and the backscattered signal is reflected by STAR-RIS to the access point; the BDs is U i ,i=N1+1,…,N are distributed in the transmission area of ​​STAR-RIS, and the backscattered signals are transmitted to the access point through STAR-RIS.

4. The method for enhancing the performance of a passive Internet of Things with STAR-RIS assisted transmission according to claim 3, characterized in that: The STAR-RIS works in an energy splitting protocol. The signal incident on the STAR-RIS is split into a transmission signal and a reflection signal. The transmission coefficient and reflection coefficient corresponding to the lth element of the STAR-RIS are expressed as and in, and is the magnitude of the corresponding coefficient, and is the phase shift of the corresponding coefficient. According to the law of conservation of energy, the amplitude parameter should satisfy the equality constraint: in 5. The method for enhancing the performance of a passive Internet of Things with STAR-RIS assisted transmission according to claim 1, characterized in that: When the passive IoT performance enhancement system adopts a non-orthogonal multiple access method in step 3), the access point first decodes the signal from the BD with a higher channel gain, that is, U j with U i The corresponding cascade channel gain satisfies: |g s,i |>|g s,j |, then U j The signal will be in U i After decoding the signal, the passive IoT performance enhancement system has the following inequality relationship established: g s,1 |>|g s,2 |>,…,>|g s,N-1 |>|g s,N |, That is, the decoding order of the signal of the i-th BD at the access point is i; According to the NOMA protocol, the passive IoT performance enhancement system i The SINR is: Among them, ρ i For U i The reflection coefficient, g s,i For U i Cascade channel with access point, h p,u For energy stations and U i The channel between them, w is the energy beamforming vector, is the power of the additive white Gaussian noise at the access point, the superscript H represents the Hermitian conjugate, and |·| represents the modulo operation of the complex number; The energy beamforming vector in the passive IoT performance enhancement system is limited to: ||w|| 2 ≤P H , where P H is the maximum transmission power of the energy station; The passive IoT performance enhancement system i The achievable rate to the access point is:

6. The method for enhancing the performance of a passive Internet of Things with STAR-RIS assisted transmission according to claim 1, characterized in that: The optimal solution for calculating the beamforming vector at the energy station and the coefficient matrix of STAR-RIS in step 3) specifically includes: Based on the channel data from the energy station to the BDs Channel data from BDs to STAR-RIS u,r , channel data from STAR-RIS to access point Beamforming vectors w, U at the energy station i The reflection coefficient ρ i , the coefficient matrix Θ of STAR-RIS s , establish the solution system and rate maximization problem P0, which is expressed as: s.t.C1:||w|| 2 ≤P H , C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1, in, and are the amplitudes of the transmission coefficient and reflection coefficient corresponding to the 1st element of STAR-RIS, and is the phase shift of the corresponding coefficient, U i Cascade channel with access point Coefficient matrix of STAR-RIS elements When s=r, it means that BD is located in the R region of STAR-RIS, and when s=t, it means that BD is located in the T region of STAR-RIS; is the complex exponential part; transform the objective function of the optimization problem P0: The equality at (a) is due to the existence of split products in the expressions of the system and rate. Since the logarithmic function log2(.) is a monotonically increasing function, Maximizing is equivalent to Find the maximum value, so the optimization problem P0 can be equivalently transformed into: s.t.C1:||w|| 2 ≤P H , C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1, In order to facilitate the solution of the optimization problem, the objective function is further transformed: in Introducing auxiliary variables Where Q s ±0,Rank(Q s )=1,diag(Q s )=β s , similarly, introduce auxiliary variable W = ww H , where Rank(W)=1, W±0, Tr(W)≤P H ; In summary, the optimization problem P1 can be transformed into: C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1, C4:diag(Q s )=β s , C5:Rank(Q s )=1,s∈{t,r}, C6:Rank(W)=1, C7:Q s 0,W 0, The Tr(Q s hh H ) can be converted into the following form: At the same time, rewrite constraint C5 into the following equivalent constraint: The optimization problem P2 is solved by the penalty function method, and the optimization problem P2 is transformed into the following problem: C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1, C4:diag(Q s )=β s , C6:Rank(W)=1, C7:Q s 0,W 0, Among them, W is the auxiliary variable introduced W = ww H ,||·|| * represents the nuclear norm of the matrix, and ||·||2 represents the spectral norm of the matrix; the passive Internet of Things performance enhancement system and rate maximization problem are solved, including: Step 1: Use semi-positive definite relaxation and successive convex approximation method to solve the problem P3 at the nth iteration of the penalty function method and obtain the optimal solution at the nth iteration of the penalty function method; Step 2: Repeat step 1 until the penalty function term in the objective function is less than the threshold value, and obtain the optimal solution w of the final optimization problem * , 7. The method for enhancing the performance of a passive Internet of Things with STAR-RIS assisted transmission according to claim 1, characterized in that: The problem P3 of the nth iteration of the penalty function method of the optimization problem P2 is solved by using semi-positive relaxation and successive convex approximation method. The solution includes: using successive convex approximation method for problem P3, in the nth iteration of the SCA method, given point Through the one-stage Taylor expansion, the upper bound of the penalty function term can be obtained. In the nth iteration of the SCA method, given point and W (n) , get Υ i The convex lower bound of : So, given W (n) , the optimization problem P3 can be further transformed into: C2:β r,l +β t,l ≤1,0≤β r,l ,β t,l ≤1, C4:diag(Q s )=β s , C6:Rank(W)=1, C7:Q s 0,W 0, Since constraint C6 is a rank 1 constraint, P4 is still a non-convex optimization problem. Therefore, SDR technology is used to relax C6. After relaxing constraint C6, the optimization problem P4 is a standard SDP problem, which is solved using the CVX tool.

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