A resource allocation method for RIS-assisted MISO symbiotic radio system

Abstract

The invention belongs to the field of symbiotic radio network architecture and resource allocation, and specifically relates to a resource allocation method for a RIS-assisted MISO symbiotic radio system; the method comprises: introducing RIS into a MISO symbiotic radio system to construct a RIS-assisted MISO symbiotic radio system; establishing an energy efficiency maximization resource allocation model for the RIS-assisted MISO symbiotic radio system; adopting a Dinkelbach-based block coordinate descent method to transform the energy efficiency maximization resource allocation into three sub-problems; solving the three sub-problems to obtain a resource allocation plan; the system performs resource allocation according to the resource allocation plan; while ensuring the maximization of the total energy efficiency of the system, the invention has significantly improved performance compared with a traditional algorithm.

Description

Technical Field

[0001] The present invention belongs to the field of symbiotic radio network architecture and resource allocation, and in particular relates to a resource allocation method for a RIS-assisted MISO symbiotic radio system. Background Art

[0002] With the rapid growth of the number of IoT devices, the shortage of spectrum resources and the sharp increase in energy consumption have become increasingly prominent. Cognitive radio can solve the problem of spectrum resource shortage through spectrum sharing mechanism, but there will be interference between the primary and secondary systems in cognitive radio. Therefore, in order to make full use of interference signals and realize resource sharing, symbiotic radio was proposed. Through the coordinated operation of primary and secondary systems, spectrum, power and infrastructure can be shared, and the primary user can treat the signal from the secondary transmission system as multipath gain instead of interference, and the system capacity can be significantly improved.

[0003] A smart reflector surface (RIS) is a uniform array plane that integrates a large number of low-power, low-cost, sub-wavelength structures and independently controllable passive electromagnetic reflective elements. Its main function is to adjust the amplitude and phase of the reflected signal through software programming based on the communication link information fed back by signal propagation, so that the reflected signal is constructively added to the signal of other paths, thereby enhancing the desired signal power at the receiving end and improving the communication quality.

[0004] Therefore, symbiotic radio and RIS can be used as two main technologies to improve spectrum efficiency and energy efficiency. Many papers have studied RIS-assisted symbiotic radio networks, such as performance analysis, minimization of transmission power, and system capacity, but energy efficiency optimization and the energy collection capability of secondary reflection nodes have been ignored, which is extremely critical for an energy-constrained symbiotic radio system. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a resource allocation method for a RIS-assisted MISO symbiotic radio system, the method comprising:

[0006] S1: Introduce RIS into the MISO symbiotic radio system and build a RIS-assisted MISO symbiotic radio system;

[0007] S2: Establish an energy efficiency maximization resource allocation model for RIS-assisted MISO symbiotic radio systems;

[0008] S3: Using the Dinkelbach-based block coordinate descent method, the energy efficiency maximization resource allocation is transformed into three sub-problems;

[0009] S4: Solve the three sub-problems to obtain a resource allocation plan; the system allocates resources according to the resource allocation plan.

[0010] Preferably, the RIS-assisted MISO symbiotic radio system comprises: a RIS having N reflection units, a primary transmitter equipped with Q antennas, a single-antenna primary receiver and a plurality of single-antenna secondary reflection nodes;

[0011] The main transmitter sends a signal to the main receiver;

[0012] Each secondary reflection node modulates its own information onto the incident main signal modulated from the main transmitter, and sends the modulated signal to the main receiver by adjusting the reflection coefficient;

[0013] Each secondary reflection node is equipped with energy collection circuit and passive backscatter circuit, and all signals from the main transmitter will be reflected by RIS for signal enhancement.

[0014] Preferably, the energy efficiency maximization resource allocation model is expressed as:

[0015]

[0016]

[0017]

[0018] C3:|φ n | 2 =1,

[0019] C4:0≤α m ≤1,

[0020]

[0021]

[0022] Among them, R sum is the system sum rate, E total is the total power consumption of the system, Φ is the RIS phase shift matrix, R m represents the rate of the mth secondary reflection node, represents the minimum rate required by the mth secondary reflection node; R s Indicates the primary user rate, represents the minimum rate required by the primary user, φ n Represents the nth element of the diagonal vector of the RIS phase shift matrix; α m is the reflection coefficient of the mth secondary reflection node; E m is the energy collected by the mth secondary reflection node; is the circuit power consumption of the mth secondary reflection node; w is the active beamforming vector of the main transmitter; P max The maximum transmit power of the main transmitter.

[0023] Preferably, the process of converting the resource optimization model into three sub-problems includes:

[0024] S31, fixing the RIS phase shift matrix and the secondary reflection node reflection coefficient, and establishing a resource allocation model with the optimization variable being the active beamforming vector;

[0025] S32, fixing the RIS phase shift matrix and active beamforming vector, and establishing a resource allocation model with the optimization variable being the reflection coefficient of the secondary reflection node;

[0026] S33, fixing the active beamforming vector and the secondary reflection node reflection coefficient, and establishing a resource allocation model with the optimization variable being the RIS phase shift matrix.

[0027] Furthermore, the resource allocation model with the optimization variable being the active beamforming vector is expressed as:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] Where W = ww H , w is the active beamforming vector of the main transmitter; η is the energy efficiency auxiliary variable, P cir is the total power consumption of the system circuit, represents the first equivalent rate of the mth secondary reflection node, represents the first equivalent rate of the primary user rate, M is the number of secondary reflection nodes in the system, represents the first energy equivalent value collected by the mth secondary reflection node, Tr() represents the trace of the matrix, represents the minimum rate required by the mth secondary reflection node, Indicates the minimum rate required by the primary user, is the circuit power consumption of the mth secondary reflection node, P max is the maximum transmit power of the main transmitter, and rank() represents the rank of the matrix.

[0036] Furthermore, the resource allocation model with the optimization variable being the reflection coefficient of the secondary reflection node is expressed as:

[0037]

[0038]

[0039]

[0040] C4:0≤α m ≤1,

[0041]

[0042] Among them, η is the energy efficiency auxiliary variable, P cir is the total power consumption of the system circuit, M is the number of secondary reflection nodes in the system; W = ww H , w is the active beamforming vector of the main transmitter, Tr() represents the trace of the matrix; represents the first equivalent rate of the mth secondary reflection node, Indicates the first equivalent rate of the primary user rate, represents the minimum rate required by the mth secondary reflection node, represents the minimum rate required by the primary user, α m is the reflection coefficient of the mth secondary reflection node, represents the first energy equivalent value collected by the mth secondary reflection node, is the circuit power consumption of the mth secondary reflection node.

[0043] Furthermore, the resource allocation model with the optimization variable being the RIS phase shift matrix is ​​expressed as:

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] C10: rank(Ψ)=1.

[0051] Among them, η is the energy efficiency auxiliary variable, P cir is the total power consumption of the system circuit, represents the second equivalent rate of the mth secondary reflection node, represents the second equivalent rate of the primary user rate, M is the number of secondary reflection nodes in the system, represents the second energy equivalent value collected by the mth secondary reflection node; W = ww H , w is the active beamforming vector of the main transmitter, Tr() represents the trace of the matrix; represents the minimum rate required by the mth secondary reflection node, Indicates the minimum rate required by the primary user, is the circuit power consumption of the mth secondary reflection node, N is the total number of RIS reflection units; Ψ = [φ, β] T [φ, β], φ represents the diagonal vector of the RIS phase shift matrix, and β represents the phase shift auxiliary variable.

[0052] Preferably, the process of solving the three sub-problems includes:

[0053] By using semi-positive definite relaxation and continuous convex approximation, the resource allocation model with the optimization variable being the active beamforming vector is transformed into the first convex optimization problem.

[0054] The resource allocation model with the optimization variable as the reflection coefficient of the secondary reflection node is transformed into a second convex optimization problem by using the quadratic transformation method.

[0055] By using semi-positive definite relaxation and continuous convex approximation, the resource allocation model with the optimization variable being the RIS phase shift matrix is ​​transformed into a third convex optimization problem.

[0056] The convex optimization theory is used to solve the first convex optimization problem, the second convex optimization problem and the third convex optimization problem to obtain the active beamforming vector of the main transmitter, the reflection coefficient of the secondary reflection node and the RIS phase shift matrix, that is, the resource allocation scheme.

[0057] The beneficial effects of the present invention are as follows: In view of the problem of poor signal reception caused by double fading channels and obstacles in traditional symbiotic radio systems, the present invention introduces RIS into the symbiotic radio system, considers the minimum rate constraints of primary and secondary users, the maximum transmission power constraints of base stations, and the minimum collection energy constraints of each secondary reflection node, and establishes an energy-efficiency maximization resource allocation model. The original problem is converted into an equivalent convex optimization form by using the Dinkelbach method, continuous convex approximation, semi-positive relaxation and quadratic transformation, and the convex optimization problem is solved to obtain a resource allocation plan; different from the traditional method of only guaranteeing transmission power or rate, the present invention balances rate and power, takes more comprehensive considerations, and fully considers the energy collection capabilities of secondary reflection nodes, which can effectively improve the system energy efficiency. In addition, compared with the existing system without RIS assistance, the system energy efficiency is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the resource allocation method of the RIS-assisted MISO symbiotic radio system in the present invention;

[0059] Figure 2 is a curve diagram of system energy efficiency of the present invention and the comparative method at different primary user rates;

[0060] Figure 3 The graph is a graph of the system energy efficiency of the present invention and the comparative method at different energy conversion efficiencies. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] The present invention proposes a resource allocation method for a RIS-assisted MISO symbiotic radio system, such as Figure 1 As shown, the method includes the following contents:

[0063] S1: Introduce RIS into the MISO symbiotic radio system and build a RIS-assisted MISO symbiotic radio system.

[0064] The present invention considers a RIS (intelligent reflector) assisted MISO (multiple input single output) symbiotic radio network. There is a main transmitter in the network that can serve a single-antenna main receiver, which is equipped with Q antennas, M single-antenna secondary reflection nodes, a RIS with N reflection units and a main receiver. Each secondary reflection node can modulate its own information onto the incident main signal from the main transmitter, and then send the modulated signal to the main receiver by adjusting the reflection coefficient. It is assumed that each secondary reflection node is equipped with an energy collection circuit and a passive backscattering circuit, and all signals from the main transmitter will be reflected by the RIS for signal enhancement. Define T s , T c are the signal periods of the primary signal and the secondary signal respectively, and T c =LT s (L>>1). That is, one secondary signal cycle covers L primary signal cycles. Define the secondary reflection node set RIS phase shift matrix Φ=diag{φ1,φ2,...,φ N}, number of signal cycles

[0065] S2: Establish an energy-efficiency maximizing resource allocation model for RIS-assisted MISO symbiotic radio systems.

[0066] Combining the user quality constraint of each user, the phase shift constraint of RIS, the maximum transmission power constraint at the main transmitter and the minimum collection energy constraint of each secondary reflection node, an energy efficiency maximization resource allocation model is established, which is expressed as:

[0067]

[0068]

[0069]

[0070] C3:|φ n | 2 =1,

[0071] C4:0≤α m ≤1,

[0072]

[0073]

[0074] The constraints in the above formula include: C1 is the minimum secondary signal rate constraint for each secondary reflection node, C2 is the minimum primary signal rate constraint, C3 is the RIS phase shift constraint, C4 is the reflection coefficient constraint for each secondary reflection node, C5 is the energy constraint collected by each secondary reflection node, and C6 is the maximum transmission power constraint for the primary transmitter; where R m represents the rate of the mth secondary reflection node, and are the minimum rates required by the primary and secondary users respectively; represents the power consumption of the secondary reflection node m, R s Indicates the primary user rate, P max is the maximum transmit power of the main transmitter, φ n represents the nth element of the diagonal vector of the RIS phase shift matrix; is the beamforming vector of the main transmitter; α m is the reflection coefficient from the mth secondary reflection node; Φ represents the RIS phase shift matrix; is the system sum rate, M is the number of secondary reflection nodes in the system; is the total system power consumption, They represent the circuit power consumption of the main transmitter, main receiver, RIS and secondary reflection node respectively, is the energy collected by the mth secondary reflection node, ρ is the energy conversion efficiency of the secondary reflection node, is the channel coefficient from the primary receiver to the mth secondary reflection node, is the channel coefficient from RIS to the mth secondary reflection node, is the channel coefficient from the main transmitter to the RIS; is the mth secondary reflection node rate, To decode the secondary signal c m The signal-to-interference-noise ratio, σ 2 is the noise power, is the channel coefficient from the mth secondary reflection node to the primary receiver; represents the main signal rate, c=[c1,c2,...,c M ] T is the sign vector of the secondary reflection node, c M is the set of all possible values ​​of c, is the signal-to-noise ratio of the decoded main signal s(l), where They are the channel coefficients from the main transmitter to the main receiver and from the RIS to the main receiver, respectively.

[0075] S3: Using the Dinkelbach-based block coordinate descent method, the energy efficiency maximization resource allocation is transformed into three sub-problems.

[0076] Using the Dinkelbach method, P1 is written as the following optimization problem:

[0077]

[0078] stC1-C6.

[0079] Among them, η is the energy efficiency auxiliary variable; represents the equivalent rate of the mth secondary reflection node, where U i represents the diagonalized matrix of the channel coefficients from RIS to the i-th secondary reflection node, φ = [φ1, φ2, ... φ N ] T ; is the primary user equivalent rate, V = diag{v H}; is the equivalent value of the energy collected by the mth secondary reflection node, and ρ is the energy conversion efficiency of the secondary reflection node.

[0080] S31, fix the RIS phase shift matrix and the secondary reflection node reflection coefficient, and establish a resource allocation model with the optimization variable being the active beamforming vector.

[0081] Fix φ and α in P2 m ,P2 can be transformed into a resource allocation model with the optimization variable as the active beamforming vector. Specifically, the definition W=ww H , P2 can be transformed into the subproblem of optimizing w:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] in, is the first equivalent rate of the secondary reflection node m, is the first equivalent rate of the primary user, is the first energy equivalent value collected by the mth secondary reflection node, Tr() represents the trace of the matrix, and rank() represents the rank of the matrix.

[0090] S32. Fix the RIS phase shift matrix and active beamforming vector, and establish a resource allocation model with the optimization variable being the reflection coefficient of the secondary reflection node.

[0091] Fixed φ and w in P2, P2 can be transformed into a resource allocation model with the optimization variable as the active beamforming vector, that is, into the optimization α m Sub-questions:

[0092]

[0093]

[0094] S33, fixing the active beamforming vector and the secondary reflection node reflection coefficient, and establishing a resource allocation model with the optimization variable being the RIS phase shift matrix.

[0095] Fix w and α in P2 m , P1 can be transformed into a resource allocation model with the optimization variable being the RIS phase shift matrix, that is, into a sub-problem of optimizing φ. Specifically, define ψ=[φ,β] T , Ψ=ψψ H , β is the phase shift auxiliary variable; H 6,m =α m |c m b m | 2 H 7,m , H 8,m =|b m |2 H 7,m . P2 can be transformed into:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] C10: rank(Ψ)=1.

[0103] in, is the second equivalent rate of the secondary reflection node m, is the second equivalent rate of the primary user, is the second energy equivalent value collected by the secondary reflection node m, [Ψ] n,n represents the element at (n,n) of the transformed RIS phase shift matrix.

[0104] S4: Solve the three sub-problems to obtain a resource allocation plan; the system allocates resources according to the resource allocation plan.

[0105] The process of solving the three sub-problems includes:

[0106] Using semi-positive relaxation and continuous convex approximation, the resource allocation model with the optimization variable as the active beamforming vector is transformed into the first convex optimization problem; specifically:

[0107] To solve Due to the non-convexity brought by this, P3 can be transformed into the first convex optimization problem by using the continuous convex approximation method and Taylor expansion:

[0108]

[0109]

[0110]

[0111] in, r m , m , is the slack variable, and They are and It can be seen that P4 is a standard semi-definite programming problem, and convex optimization theory can be used to solve P4 to obtain the analytical solution of W and then the analytical solution of the active beamforming vector w of the main transmitter.

[0112] The quadratic transformation method is used to transform the resource allocation model with the optimization variable being the reflection coefficient of the secondary reflection node into a second convex optimization problem; specifically:

[0113] To solve The non-convexity brought by the secondary transformation, P5 can be transformed into:

[0114]

[0115]

[0116] in, It is the equivalent rate of the secondary reflection node m, y m is the secondary transformation auxiliary variable, and its optimal value is P6 can be transformed into the first convex optimization problem:

[0117]

[0118]

[0119] The second convex optimization problem is solved by using convex optimization theory to obtain the reflection coefficient α of the secondary reflection node m Analytical solution of .

[0120] Using semi-positive definite relaxation and continuous convex approximation, the resource allocation model with the optimization variable as the RIS phase shift matrix is ​​transformed into a third convex optimization problem; specifically:

[0121] Using continuous convex approximation and Taylor expansion, P8 can be transformed into:

[0122]

[0123]

[0124]

[0125] in, τ m ,ω m , is the slack variable, and They are and It can be seen that P9 is a standard semi-positive definite programming problem. Convex optimization theory can be used to solve P9 to obtain the analytical solution of Ψ, and then the Gaussian randomization method is used to obtain the RIS phase shift matrix φ.

[0126] After the above process, the active beamforming vector of the main transmitter, the reflection coefficient of the secondary reflection node and the RIS phase shift matrix, that is, the resource allocation scheme, are obtained; the system can allocate resources according to the resource allocation scheme.

[0127] Evaluation of the present invention:

[0128] The application effect of the present invention is described in detail in combination with simulation:

[0129] 1) Simulation conditions

[0130] Assume that there are two secondary reflection nodes in this system. The coordinates of the main transmitter, RIS, main receiver, secondary reflection node 1 and secondary reflection node 2 are: (0,0), (25,37), (23,34), (28,35), (27,36). The path loss model is: Where ξ represents the path loss index, l0 is the path loss when the transmission distance is the reference distance d0, and the values ​​here are: l0 = -20dB, d0 = 1m. Other simulation parameters are given in Table 1:

[0131] Table 1 Simulation parameters

[0132]

[0133] 2) Simulation results

[0134] Figure 2 The relationship between the system energy efficiency and the primary user rate of the present invention is shown. It can be seen that as the primary user rate increases, the system energy efficiency decreases. Figure 3 The relationship between the system energy efficiency and energy conversion efficiency of the present invention is shown. It can be seen that as the energy conversion efficiency increases, the system energy efficiency increases, and Figure 2 and Figure 3 It shows that the energy efficiency of the algorithm proposed in the present invention is significantly improved compared with the traditional RIS-free algorithm and the random phase algorithm. In summary, compared with the traditional method, the performance of the present invention is better.

[0135] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation modes 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 in the protection scope of the present invention.

Claims

1. A resource allocation method for a RIS-assisted MISO symbiotic radio system, characterized in that: include: S1: Introduce the intelligent reflector RIS into the MISO symbiotic radio system to build a RIS-assisted MISO symbiotic radio system; S2: Establish an energy efficiency maximization resource allocation model for RIS-assisted MISO symbiotic radio system; the energy efficiency maximization resource allocation model is expressed as: Among them, R sum is the system sum rate, E total is the total power consumption of the system, Φ is the RIS phase shift matrix, R m represents the rate of the mth secondary reflection node, represents the minimum rate required by the mth secondary reflection node; R s Indicates the primary user rate, represents the minimum rate required by the primary user, φ n Represents the nth element of the diagonal vector of the RIS phase shift matrix; α m is the reflection coefficient of the mth secondary reflection node; E m is the energy collected by the mth secondary reflection node; is the circuit power consumption of the mth secondary reflection node; w is the active beamforming vector of the main transmitter; P max is the maximum transmit power of the main transmitter; S3: Using the block coordinate descent method based on Dinkelbach, the energy efficiency maximization resource allocation is transformed into three sub-problems; the process of transforming the resource optimization model into three sub-problems includes: S31, fix the RIS phase shift matrix and the secondary reflection node reflection coefficient, and establish a resource allocation model with the optimization variable being the active beamforming vector; the resource allocation model with the optimization variable being the active beamforming vector is expressed as: Where W = ww H , w is the active beamforming vector of the main transmitter; η is the energy efficiency auxiliary variable, P cir is the total power consumption of the system circuit, represents the first equivalent rate of the mth secondary reflection node, represents the first equivalent rate of the primary user rate, M is the number of secondary reflection nodes in the system, represents the first energy equivalent value collected by the mth secondary reflection node, Tr() represents the trace of the matrix, is the circuit power consumption of the mth secondary reflection node, rank() represents the rank of the matrix; S32, fix the RIS phase shift matrix and the active beamforming vector, and establish a resource allocation model with the optimization variable being the reflection coefficient of the secondary reflection node; the resource allocation model with the optimization variable being the reflection coefficient of the secondary reflection node is expressed as: S33, fixing the active beamforming vector and the secondary reflection node reflection coefficient, and establishing a resource allocation model whose optimization variable is the RIS phase shift matrix; the resource allocation model whose optimization variable is the RIS phase shift matrix is ​​expressed as: in, represents the second equivalent rate of the mth secondary reflection node, Indicates the second equivalent rate of the primary user rate, represents the second energy equivalent value collected by the mth secondary reflection node; N represents the total number of RIS reflection units; Ψ=[φ,β] T [φ, β], φ represents the diagonal vector of the RIS phase shift matrix, β represents the phase shift auxiliary variable; S4: Solve the three sub-problems to obtain a resource allocation plan; the system allocates resources according to the resource allocation plan.

2. The resource allocation method of a RIS-assisted MISO symbiotic radio system according to claim 1, characterized in that: The RIS-assisted MISO symbiotic radio system includes: a RIS with N reflection units, a primary transmitter equipped with Q antennas, a single-antenna primary receiver and a plurality of single-antenna secondary reflection nodes; The main transmitter sends a signal to the main receiver; Each secondary reflection node modulates its own information onto the incident main signal from the main transmitter and sends the modulated signal to the main receiver by adjusting the reflection coefficient; Each secondary reflection node is equipped with energy collection circuit and passive backscatter circuit, and all signals from the main transmitter will be reflected by RIS for signal enhancement.

3. The resource allocation method of a RIS-assisted MISO symbiotic radio system according to claim 1, characterized in that: The process of solving the three sub-problems includes: By using semi-positive definite relaxation and continuous convex approximation, the resource allocation model with the optimization variable being the active beamforming vector is transformed into the first convex optimization problem. The resource allocation model with the optimization variable as the reflection coefficient of the secondary reflection node is transformed into a second convex optimization problem by using the quadratic transformation method. By using semi-positive definite relaxation and continuous convex approximation, the resource allocation model with the optimization variable being the RIS phase shift matrix is ​​transformed into a third convex optimization problem. The convex optimization theory is used to solve the first convex optimization problem, the second convex optimization problem and the third convex optimization problem to obtain the active beamforming vector of the main transmitter, the reflection coefficient of the secondary reflection node and the RIS phase shift matrix, that is, the resource allocation scheme.

Images