A full-duplex wireless power supply backscattering communication system resource allocation method

Abstract

The application belongs to the field of low-power Internet of Things, and particularly relates to a resource allocation method for a full-duplex wireless power supply backscattering communication system; the method comprises the following steps: constructing a full-duplex wireless power supply backscattering communication system model; considering throughput constraints, transmission power constraints, energy collection constraints, transmission energy consumption constraints, reflection coefficient constraints and time allocation factor constraints, constructing a total throughput maximization resource allocation model with the optimization target of maximizing the total throughput; converting the throughput maximization resource allocation model into four optimization sub-problems; solving the four optimization sub-problems to obtain a resource allocation scheme; and allocating resources according to the resource allocation scheme; the application considers the minimum throughput requirement of each reflection node, meets the quality of service requirement of the reflection node, and improves the system throughput.

Description

Technical Field

[0001] This invention belongs to the field of low-power Internet of Things (IoT), specifically relating to a resource allocation method for a full-duplex wireless power-powered backscatter communication system. Background Technology

[0002] With the development of IoT technology, the massive number of device nodes connected to the IoT has led to increased energy consumption in the entire communication system. At the same time, the contradiction between limited device battery capacity or inconvenient battery replacement, small network capacity and low transmission efficiency, and people's ever-growing needs is becoming increasingly prominent. Therefore, how to improve the capacity and transmission efficiency of IoT systems through wireless power supply is an urgent problem to be solved (i.e., battery-free operation). To solve this problem, wireless power backscatter communication technology has emerged.

[0003] Powered-by-wire backscatter communication (PoBW) technology combines the advantages of both backscatter and PoBW communication. Device nodes can operate in energy harvesting, backscatter, or active transmission modes. For information transmission, the circuit power consumption of backscatter communication is significantly lower than that of active transmission. Therefore, when the energy harvested by the node is insufficient for active transmission, backscatter can be used to increase network capacity. On the other hand, the backscatter transmission power is limited by the received radio frequency signal, while active transmission can adaptively adjust its transmission power according to channel conditions and offers higher transmission performance. Therefore, when there is sufficient radio frequency energy around the node, active transmission can improve system transmission efficiency. Thus, appropriate use of PoBW backscatter communication technology can effectively enhance system performance, making it a current research hotspot in academia and industry.

[0004] Due to the aforementioned advantages of this technology, many scholars have conducted research on wirelessly powered backscatter communication networks in recent years, achieving some valuable results. For general wirelessly powered backscatter communication networks, some research focuses on reducing system energy consumption from three aspects: transceiver design, protocol design, and coding design. However, theoretical research on resource allocation issues such as beamforming, throughput, and energy efficiency is lacking. This is extremely critical for an energy-constrained wirelessly powered backscatter communication network. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a resource allocation method for a full-duplex wireless power-powered backscatter communication system, the method comprising:

[0006] S1: Construct a model of a full-duplex wireless power-powered backscatter communication system;

[0007] S2: Considering throughput constraints, transmit power constraints, energy harvesting constraints, transmission energy consumption constraints, reflection coefficient constraints, and time allocation factor constraints, a total throughput maximization resource allocation model is constructed with the goal of maximizing total throughput.

[0008] S3: Transform the throughput maximization resource allocation model into 4 optimization sub-problems;

[0009] S4: Solve the four optimization subproblems to obtain the resource allocation scheme; the system allocates resources according to the resource allocation scheme.

[0010] Preferably, the full-duplex wireless power-powered backscatter communication system model specifically includes: a full-duplex hybrid access point equipped with (M+U) antennas and K reflecting nodes configured with single antennas. The M antennas of the hybrid access point are used for downlink transmission, and the U antennas are used for uplink reception. The transmission frame length of the system is defined as T. According to the time allocation factor, the system transmission process is divided into a backscatter phase and an active transmission phase. During the backscatter phase, the hybrid access point broadcasts an energy signal to each reflecting node and simultaneously receives the backscattered signal from the reflecting node. During the active transmission phase, it receives the information actively transmitted by the reflecting node.

[0011] The preferred resource allocation model for maximizing total throughput is expressed as follows:

[0012]

[0013] Where K represents the number of reflection nodes, R A,k R represents the throughput of the reflecting node k during the active transmission phase. B,k This represents the throughput of the reflecting node k during the backscattering phase. w represents the minimum throughput threshold for reflection node k. k P represents the beamforming vector sent from the hybrid access point to the reflecting node k. max This indicates the maximum transmit power threshold for hybrid access points. This represents the minimum collection threshold of the reflecting node k. This represents the energy collected by the reflecting node k during the backscattering phase, α represents the time allocation factor, and T represents the system transmission frame length. p represents the circuit power consumption of the reflecting node k during information transmission. k μ represents the active transmission power of the reflecting node k during the active transmission phase. k β represents the circuit energy consumption factor of the reflecting node k during the active transmission phase. k This represents the reflection coefficient of the reflecting node k.

[0014] Furthermore, the throughput of the reflection node during the active transmission phase is expressed as:

[0015]

[0016] in, This represents the signal-to-interference-plus-noise ratio (SIR) of the signal received by the hybrid access point from the reflecting node k during the active transmission phase.

[0017] Furthermore, the throughput of the reflecting node during the backscattering phase is expressed as:

[0018]

[0019] in, The signal-to-interference-plus-noise ratio (SIR) of the signal received by the hybrid access point from the reflecting node k during the backscattering phase.

[0020] Furthermore, the energy collected by the reflecting node during the backscattering phase is expressed as:

[0021]

[0022] Where, p B,k This represents the power of energy collected by the reflecting node k during the backscattering phase. This represents the noise power at the reflector node k antenna.

[0023] Preferably, the process of transforming the total throughput maximization resource allocation model into four optimization sub-problems includes:

[0024] A resource allocation sub-model with active transmission power as the optimization variable is established by fixing the time allocation factor, reflection coefficient, and beamforming vector.

[0025] A resource allocation sub-model with time allocation factor as the optimization variable is established by fixing the active transmission power, reflection coefficient and beamforming vector;

[0026] A resource allocation sub-model with fixed active transmission power, time allocation factor, and beamforming vector is established, with reflection coefficient as the optimization variable.

[0027] By fixing the active transmission power, time allocation factor, and reflection coefficient, a resource allocation sub-model is established with beamforming vector as the optimization variable.

[0028] Furthermore, the process of solving the four optimization subproblems includes:

[0029] The resource allocation sub-model with active transmission power as the optimization variable is transformed into a first convex optimization problem using the quadratic transformation method.

[0030] The resource allocation sub-model with time allocation factor as the optimization variable is treated as the second convex optimization problem;

[0031] The resource allocation sub-model with reflection coefficient as the optimization variable is transformed into a third convex optimization problem using the quadratic transformation method.

[0032] The resource allocation sub-model with beamforming vector as the optimization variable is transformed into a fourth convex optimization problem by using the positive semidefinite relaxation method, continuous convex optimization and Taylor approximation method.

[0033] The first, second, third, and fourth convex optimization problems are solved using convex optimization theory to obtain the active transmission power, time allocation factor, reflection coefficient, and beamforming vector, i.e., the resource allocation scheme.

[0034] The beneficial effects of this invention are as follows: Addressing the issues of limited capacity and low transmission efficiency in conventional wireless power-powered backscatter communication systems, this invention introduces full-duplex communication technology into wireless power-powered backscatter communication systems. Considering constraints such as the minimum throughput of each reflecting node, the maximum transmit power of the hybrid access point, and the minimum energy harvesting of each reflecting node, a resource allocation model maximizing total throughput is established. The original problem is transformed into an equivalent convex optimization form using the block coordinate descent method and convex optimization theory, and the convex optimization problem is solved to obtain a resource allocation scheme. Unlike traditional methods that only guarantee transmit power or receive signal-to-noise ratio, this invention considers the minimum throughput requirement of each reflecting node, satisfying the quality of service requirements of the reflecting nodes. Furthermore, compared with traditional backscatter allocation methods and traditional wireless power allocation methods, the method of this invention significantly improves the total system throughput. Attached Figure Description

[0035] Figure 1 This is a flowchart of the resource allocation method for the full-duplex wireless power supply backscatter communication system in this invention;

[0036] Figure 2 This is a schematic diagram of the full-duplex wireless power supply backscatter communication system model in this invention;

[0037] Figure 3 This is a graph showing the relationship between the total throughput of the method of the present invention and the comparative method and the distance from the reflection node to the hybrid access point. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] This invention proposes a resource allocation method for a full-duplex wireless powered backscatter communication system, such as... Figure 1 As shown, the method includes the following:

[0040] S1: Construct a model of a full-duplex wireless power-powered backscatter communication system.

[0041] like Figure 2 As shown, this invention introduces full-duplex communication technology into a wireless power-powered backscatter communication system, constructing a multi-input single-output full-duplex wireless power-powered backscatter communication system model. The model specifically includes: a full-duplex hybrid access point equipped with (M+U) antennas and K reflection nodes equipped with single antennas. The M antennas of the hybrid access point are used for downlink transmission, and the U antennas are used for uplink reception.

[0042] Each reflector node can load its own information using the incident signal and transmit its data to the hybrid access point via backscattering by adjusting the reflection coefficient. Each reflector node is equipped with an energy harvesting module, a backscattering module, and an active transmission module, enabling it to collect ambient radio frequency energy and use it for data transmission. Therefore, each single-antenna reflector node simultaneously possesses both energy harvesting and information transmission circuitry, utilizing the reflection coefficient β... k To adjust the working mode and satisfy β k ∈[0,1].

[0043] Define the system's transmission frame length as T, and divide T into a backscattering phase and an active transmission phase according to the time allocation factor α (0≤α≤1); the information transmission process within T is as follows:

[0044] The backscattering phase, also known as the first phase αT, involves the hybrid access point broadcasting an energy signal to each reflecting node. The reflecting nodes collect the energy and upload their own data to the hybrid access point in backscattering mode, providing uninterrupted service.

[0045] The active transmission phase, also known as the second phase (1-α)T, involves the reflecting node using the energy collected in the first phase to send information to the hybrid access point in an active transmission mode, providing a reliable high-speed information transmission service. The hybrid access point receives the information actively transmitted by the reflecting node.

[0046] S2: Considering throughput constraints, transmit power constraints, energy harvesting constraints, transmission energy consumption constraints, reflection coefficient constraints, and time allocation factor constraints, a total throughput maximization resource allocation model is constructed with the goal of maximizing total throughput.

[0047] The throughput of the reflecting node k during the active transmission phase is represented by R. A,k for:

[0048]

[0049]

[0050] in, p represents the signal-to-interference-plus-noise ratio (SIR) of the signal received by the hybrid access point from the reflecting node k during the active transmission phase. k h represents the active transmission power of the reflecting node k during the active transmission phase. k σ represents the channel vector from the reflecting node k to the hybrid access point in the uplink during the active transmission phase. 2 This indicates the noise power at the receiving antenna of the hybrid access point.

[0051] The throughput of the reflecting node k during the backscattering phase is represented by R. B,k for:

[0052]

[0053]

[0054]

[0055] in, g represents the signal-to-interference-plus-noise ratio (SIR) of the signal received by the hybrid access point from the reflecting node k during the backscattering phase. k β represents the uplink transmission channel vector from the reflecting node k to the hybrid access point during the backscattering phase. k p represents the reflection coefficient of the reflecting node k. B,k G represents the power of energy collected by the reflecting node k during the backscattering phase. d,k w represents the channel vector from the hybrid access point to the reflecting node k in the downlink during the backscattering phase. m This represents the beamforming vector sent from the hybrid access point to the reflecting node m.

[0056] Energy collected by reflection node k during the backscattering phase Represented as:

[0057]

[0058] Where, η k This represents the energy harvesting efficiency of the reflecting node k. This represents the noise power of the reflecting node k.

[0059] By jointly optimizing the time allocation factor, the reflection coefficient of the reflecting node, the beamforming vector sent from the hybrid access point to the reflecting node, and the active transmission power of the reflecting node, a resource allocation model for maximizing total throughput is constructed with the objective of maximizing total throughput. This model is expressed as:

[0060]

[0061] The constraints in the above formula include: C1 represents the throughput constraint of the reflecting node k, C2 represents the maximum transmit power constraint of the hybrid access point, C3 represents the minimum energy harvesting constraint for the reflecting node to satisfy the active transmission phase, C4 represents the total transmission energy consumption constraint of the reflecting node k in both phases, C5 represents the reflection coefficient constraint, and C6 represents the time allocation factor constraint. In the above formula, For the set of reflection nodes, w represents the minimum throughput threshold for reflection node k. k P represents the beamforming vector sent from the hybrid access point to the reflecting node k. max This indicates the maximum transmit power threshold for hybrid access points. This represents the minimum collection threshold of the reflecting node k. μ represents the circuit power consumption for information transmission at the reflecting node k. k This represents the circuit energy consumption factor of the reflecting node k during the active transmission phase.

[0062] S3: The resource allocation model for maximizing total throughput is transformed into four optimization sub-problems.

[0063] Using the block coordinate descent algorithm, P1 can be decomposed into four sub-problems: optimizing active transmission power, time allocation factor, reflection coefficient, and beamforming vector; including the following:

[0064] By fixing the time allocation factor, reflection coefficient, and beamforming vector, a resource allocation sub-model is established with active transmission power as the optimization variable. Specifically, (α, w) in P1 is fixed. k ,β k P1 can be transformed into an optimization variable as the active transmission power p. k The optimization subproblem is represented as:

[0065]

[0066] By fixing the active transmission power, reflection coefficient, and beamforming vector, a resource allocation sub-model is established with time allocation factors as the optimization variables; specifically, (p in P1) is fixed. k ,w k ,β k P1 can be transformed into an optimization subproblem of optimizing α, expressed as:

[0067]

[0068] By fixing the active transmission power, time allocation factor, and beamforming vector, a resource allocation sub-model is established with the reflection coefficient as the optimization variable; specifically, the (p) in P1 is fixed. k ,α,w k P1 can be transformed into optimizing β.k The optimization subproblem is represented as:

[0069]

[0070] By fixing the active transmission power, time allocation factor, and reflection coefficient, a resource allocation sub-model is established with beamforming vector as the optimization variable; specifically, (p in P1) is fixed. k ,α,β k P1 can be transformed into optimizing w k The optimization subproblem is represented as:

[0071]

[0072] S4: Solve the four optimization subproblems to obtain the resource allocation scheme; the system allocates resources according to the resource allocation scheme.

[0073] The process of solving the four optimization subproblems includes:

[0074] To solve the problem involving variable p k The logarithmic objective function and constraint C1 are nonconvex problems. Using the quadratic transformation method, the resource allocation sub-model with active transmission power as the optimization variable, i.e., P2, is transformed into a first convex problem, expressed as:

[0075]

[0076] in, ψ k =p k ||h k || 2 v k This is an auxiliary variable introduced by the quadratic transformation of the k-th downlink. Alternating optimization p k and v k To obtain the optimal solution That is, the optimal active transmission power of the reflecting node k.

[0077] Since the resource allocation sub-model with time allocation as the optimization variable, i.e., P3, is already a convex optimization problem, treating it as a second convex optimization problem allows us to directly obtain its optimal solution α using convex optimization theory. * That is, the optimal time allocation factor.

[0078] To solve the problem involving variable β k The logarithmic objective function and constraint C1 are nonconvex problems. Using a quadratic transformation method, the resource allocation sub-model P4, where the optimization variable is the time allocation factor, is transformed into a third convex problem, expressed as:

[0079]

[0080] in, Λ k =β k ||g k || 2 p B,k , z k This is an auxiliary variable introduced by the quadratic transformation of the k-th downlink. Alternating optimization of β k and z k To obtain the optimal solution That is, the optimal emission coefficient of the reflecting node k.

[0081] To solve problems involving variables The logarithmic objective function and constraint C1 are non-convex problems. Using a positive semidefinite relaxation method, continuous convex optimization, and Taylor approximation, the resource allocation sub-model (P5) with beamforming vectors as optimization variables is transformed into a fourth convex optimization problem. Specifically, new auxiliary variables are introduced. And W k ≥0, r B,k ε B,k and τ B,k It is an auxiliary variable, a B,k b B,k and c B,k Using slack variables, we obtain the fourth convex optimization problem, expressed as:

[0082]

[0083] in, and These are the optimal values ​​obtained in the [l]th iteration (i.e., the optimal values ​​from the previous iteration). Therefore, the optimal solution w of P8 can be obtained through convex optimization theory. k * That is, the optimal beamforming vector.

[0084] After the above solution process, the time allocation factor, the reflection coefficient and active transmission power of each reflecting node, and the beamforming vector sent from the hybrid access point to the reflecting node are obtained, which is the resource allocation scheme. The system allocates resources according to the resource allocation scheme, and can obtain the maximum total throughput of the system while ensuring performance.

[0085] Evaluation of the present invention:

[0086] 1) Simulation conditions

[0087] Assuming this network has one full-duplex hybrid access point and two reflection nodes, and considering a Rayleigh fading channel following a negative exponential distribution, the channel model is expressed as g. d,k =g k =hk =ρd k -χ , where d k ρ is the distance between the hybrid access point and the reflecting node device, χ is the path loss factor, and ρ is the path loss exponent. The parameters in the simulation are set as follows: T = 1s, d k =2m, ρ=10 -3 χ = 3. The hybrid access point is configured with 4 antennas (M = U = 2). The power threshold P for the RF energy signal transmitted by the hybrid access point is... max =3W, noise power The number of reflecting nodes K = 2, and the energy harvesting efficiency, minimum throughput threshold, power consumption of the circuit during the reflection phase, minimum energy harvesting threshold, and energy efficiency factor of reflecting node k are respectively η. k =0.8 and μ k =1.0.

[0088] 2) Simulation Results

[0089] Figure 3 The relationship between total throughput and distance from the reflecting node to the hybrid access point is presented for different algorithms. As can be seen from the figure, the total throughput of different algorithms decreases as the distance from the reflecting node to the hybrid access point increases. Figure 3 This indicates that the total throughput of the method proposed in this invention is significantly improved compared to the traditional backscatter distribution method and the traditional wireless power distribution method. In summary, the present invention performs better than the traditional methods.

[0090] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A resource allocation method for a full-duplex wireless powered backscatter communication system, characterized in that, include: S1: Construct a model of a full-duplex wireless power-powered backscatter communication system; S2: Considering throughput constraints, transmit power constraints, energy harvesting constraints, transmission energy consumption constraints, reflection coefficient constraints, and time allocation factor constraints, a resource allocation model for maximizing total throughput is constructed with the optimization objective of maximizing total throughput. The resource allocation model for maximizing total throughput is expressed as: ； in, Indicates the number of reflection nodes. Represents the reflection node Throughput during the active transmission phase, Represents the reflection node Throughput during the backscattering phase, Represents the reflection node Minimum throughput threshold, Indicates the hybrid access point to the reflection node The transmitted beamforming vector, This indicates the maximum transmit power threshold for hybrid access points. Represents a reflection node The minimum capability collection threshold, Represents a reflection node The energy collected during the backscattering phase, Indicates the time allocation factor. Indicates the system transmission frame length. Represents a reflection node The power consumption of circuits used for information transmission. Represents the reflection node Active transmission power during the active transmission phase, Represents the reflection node The circuit energy consumption factor during the active transmission phase, Represents the reflection node The reflection coefficient; S3: Transform the total throughput maximization resource allocation model into 4 optimization sub-problems; the process of transforming the total throughput maximization resource allocation model into 4 optimization sub-problems includes: A resource allocation sub-model with active transmission power as the optimization variable is established by fixing the time allocation factor, reflection coefficient, and beamforming vector. A resource allocation sub-model with time allocation factor as the optimization variable is established by fixing the active transmission power, reflection coefficient and beamforming vector; A resource allocation sub-model with fixed active transmission power, time allocation factor, and beamforming vector is established, with reflection coefficient as the optimization variable. A resource allocation sub-model with fixed active transmission power, time allocation factor, and reflection coefficient is established, with beamforming vector as the optimization variable. S4: Solve the four optimization subproblems to obtain a resource allocation scheme; the system allocates resources according to the resource allocation scheme; the process of solving the four optimization subproblems includes: The resource allocation sub-model with active transmission power as the optimization variable is transformed into a first convex optimization problem using the quadratic transformation method. The resource allocation sub-model with time allocation factor as the optimization variable is treated as the second convex optimization problem; The resource allocation sub-model with reflection coefficient as the optimization variable is transformed into a third convex optimization problem using the quadratic transformation method. The resource allocation sub-model with beamforming vector as the optimization variable is transformed into a fourth convex optimization problem by using the positive semidefinite relaxation method, continuous convex optimization and Taylor approximation method. The first, second, third, and fourth convex optimization problems are solved using convex optimization theory to obtain the active transmission power, time allocation factor, reflection coefficient, and beamforming vector, i.e., the resource allocation scheme.

2. The resource allocation method for a full-duplex wireless power-powered backscatter communication system according to claim 1, characterized in that, The full-duplex wireless-powered backscatter communication system model specifically includes: a full-duplex hybrid access point equipped with M+U antennas and There are three reflective nodes, each equipped with a single antenna. The hybrid access point has M antennas for downlink transmission and U antennas for uplink reception. The system's transmission frame length is defined as T. Based on the time allocation factor, the system transmission process is divided into a backscattering phase and an active transmission phase. During the backscattering phase, the hybrid access point broadcasts energy signals to each reflective node and simultaneously receives the backscattered signals from the reflective nodes. During the active transmission phase, it receives information actively transmitted by the reflective nodes.

3. The resource allocation method for a full-duplex wireless power-powered backscatter communication system according to claim 1, characterized in that, The throughput of the reflection node during the active transmission phase is expressed as: ； in, This indicates that during the active transmission phase, the hybrid access point receives data from the reflection node. The signal-to-interference-plus-noise ratio of the signal.

4. The resource allocation method for a full-duplex wireless power-powered backscatter communication system according to claim 1, characterized in that, The throughput of the reflecting node during the backscattering phase is expressed as: ； in, For the backscattering phase, the hybrid access point receives data from the reflecting node. The signal-to-interference-plus-noise ratio of the signal.

5. The resource allocation method for a full-duplex wireless power-powered backscatter communication system according to claim 1, characterized in that, The energy collected by the reflecting node during the backscattering phase is expressed as: ； in, Represents the reflection node The power that collects energy during the backscattering phase. Represents the reflection node Noise power at the antenna.

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