Non-orthogonal multiple access communication system resource optimization method based on reconfigurable intelligent surface

Abstract

The invention discloses a non-orthogonal multiple access communication system resource optimization method based on an information bearing reconfigurable intelligent surface, and relates to a non-orthogonal multiple access communication system resource optimization method. The objective of the invention is to solve the problem that the research based on the combination of the IB-RIS technology and the Internet of Things communication system is still in the starting stage, and the balance of the energy efficiency and the spectrum efficiency is still insufficient. The invention provides a power domain non-orthogonal multiple access technology network for wireless access points by Internet of Things nodes of an information bearing reconfigurable intelligent surface, a plurality of Internet of Things nodes are configured with IB-RISs, and the IB-RISs take energy signals radiated by power beacons as carrier signals and transmit information to the APs in a passive backscattering mode; based on an alternative optimization algorithm thought, beam forming vectors of PB and a plurality of IB-RIS are jointly designed. The feasibility, accessibility and rate of the proposed system are verified through simulation. The invention belongs to the technical field of wireless communication physical layers.

Description

Technical Field

[0001] This invention relates to a resource optimization method for a non-orthogonal multiple access communication system, belonging to the field of wireless communication physical layer technology. Background Technology

[0002] In recent years, the rapid development of the Internet of Things (IoT) has greatly improved people's lives and promoted the development of various research and technologies. For IoT communication systems, the ever-increasing number of IoT devices brings extremely high demands for spectrum resources and energy. However, existing IoT communication systems are mainly battery-powered and rely on active radio frequency links for information transmission, resulting in significant hardware deployment costs and energy consumption. Furthermore, limited spectrum resources are insufficient to meet the communication needs of the exponentially growing number of IoT devices. Under this trend, IB-RIS and NOMA technologies have demonstrated significant advantages in low-power, high-spectral-efficiency communication systems due to their passive low-power operating modes and power domain multiplexing technology, respectively. An IB-RIS system consists of an ambient energy source and an IB-RIS. The IB-RIS acts as a backscattering device, modulating the information to be transmitted onto the incident carrier signal and transmitting the signal to the receiver through the RIS's passive beamforming capability.

[0003] However, research on combining IB-RIS technology with IoT communication systems is still in its early stages, and there are shortcomings in balancing energy efficiency and spectral efficiency. Furthermore, how to improve spectral utilization through NOMA communication technology when spectrum resources are limited is also a problem that needs to be solved. Therefore, this invention focuses on improving the data transmission efficiency of IB-RIS-based NOMA communication networks. Summary of the Invention

[0004] This invention addresses the fact that research on the integration of IB-RIS technology with IoT communication systems is still in its early stages and there are shortcomings in the trade-off between energy efficiency and spectral efficiency. Therefore, it proposes a resource optimization method for non-orthogonal multiple access communication systems based on information-carrying reconfigurable smart surfaces.

[0005] The technical solution adopted by the present invention to solve the above problems is as follows: The steps of the present invention include:

[0006] Step 1: Assume all links in the system experience flat quasi-static Ricean fading. Correspondingly, PB and wireless device u... j Between, between PB and AP and wireless devices u j The baseband equivalent channel between the AP and the AP can be expressed as: and

[0007] Step 2: After passive backscattering by IB-RIS at multiple IoT nodes, the signal received at the AP is represented as follows:

[0008]

[0009] Where z represents the zero mean at AP, and the variance is σ. 2 Additive white Gaussian noise;

[0010] Step 3: Define Ψ(j) as the decoding order of the j-th WD signal;

[0011] Step 4: The problem (P1) of maximizing the weighted sum rate of the proposed system by jointly optimizing the active beamforming vector at PB and the passive beamforming of multiple IB-RIS is formulated as follows:

[0012]

[0013] stTr(ww H )≤P,

[0014]

[0015] j∈D, k∈K j

[0016] Where P is the set of IB-RIS reflection coefficient vectors, and P is the maximum transmit power budget at PB;

[0017] Step 5: Weighted sum rate maximization.

[0018] Furthermore, the information modulation process at the j-th RIS in step 1 is represented as follows:

[0019]

[0020] Where w is the beamforming vector at PB, Θ j =diag(θ) j ) represents the backscattering matrix at the j-th RIS, θ j For the corresponding backscattering vector, s is the carrier signal transmitted by PB to activate the wireless device for backscattering, and it needs to satisfy E[|s| 2 ] = 1, x j This represents the signal to be sent to the AP after the j-th RIS modulation, and satisfies E[|x] j | 2 ] = 1; the backscattering process at IB-RIS is considered as a combination of passive beamforming and information modulation; by θ j s becomes φ j x jThe carrier signal is encoded by IB-RIS to generate new data x. j And φ j Let be the passive beamforming vector at RIS; considering the passive reflection characteristics of RIS, constrain... It must be satisfied, where [A] is one of the following conditions. m,n This represents the element in the m-th row and n-th column of matrix A.

[0021] Furthermore, step 3 specifically includes:

[0022] According to the NOMA communication scheme, the AP first decodes the signals of users with good signal conditions and treats the signals of users with poor channel conditions as interference. Then, it decodes the signal x of the j-th WD. j At this point, the signals of wireless device i with a decoding order satisfying Ψ(i) < Ψ(j) have all been decoded and eliminated, while the signals of wireless device j with a decoding order satisfying Ψ(j) > Ψ(p) are considered interference. Without loss of generality, assume Ψ(j) = j, meaning the information from the j-th IoT node is the signal decoded by the j-th node; the decoding at the AP originates from IoT node u. j The signal-to-interference-plus-noise ratio (SIR) of the data stream is expressed as:

[0023]

[0024] Accordingly, the reachable weighted sum rate at AP is Where ω j is a weighting factor, representing the priority of the j-th IB-RIS in transmitting IoT node information.

[0025] Furthermore, step 5 specifically includes:

[0026] The weighted sum of multiple logarithmic functions is handled using the Lagrange dual transformation, specifically expressed as:

[0027]

[0028] Where, α j The auxiliary variable introduced, for a given α, the original objective function is equivalent to maximizing

[0029] Applying quadratic transformation and fractional programming theory, the objective function in problem (P1) is equivalently expressed as:

[0030]

[0031] Where β=[β1,...,β j ,...,β M ] T As an auxiliary variable, variable A j and B j The expression is

[0032]

[0033] Substituting the equivalent transformed objective function into problem (P1), the original weighted sum rate maximization problem is expressed as problem (P2):

[0034]

[0035] stTr(ww H )≤P,

[0036]

[0037] α j ≥0,

[0038] j∈D, k∈K j

[0039] The original problem is decomposed into three subproblems and processed separately. Then, the optimization is performed iteratively and alternately until the weighted sum rate converges.

[0040] Subproblem 1: Solving for the optimal auxiliary variable:

[0041] For a given set of feasible solutions w, P, the optimal solution for auxiliary variables α and β can be obtained by setting the partial derivatives of the objective function in problem (P2) with respect to α and β to zero. The optimal expressions for α and β are:

[0042]

[0043] Sub-problem 2: PB beamforming design:

[0044] For a given α, β, and P, the objective function in the original optimization problem can be equivalently rewritten as:

[0045]

[0046] in, Defined to simplify formula expressions,

[0047]

[0048] Therefore, the original problem (P2) is rewritten as problem (P3):

[0049]

[0050] stTr(ww H )≤P,

[0051] Solving this problem using the Lagrange multiplier method, the Lagrange function corresponding to the objective function of problem (P3) is:

[0052] L(w,λ)=w H Vw-2Re(w H x)+λ(w H Iw-P),

[0053] Where λ is the corresponding Lagrange multiplier, the optimal solution for the active beamforming vector w can be obtained by differentiating the function:

[0054] w opt =(λI+V) -1 x,

[0055] λ opt =min{λ≥0:w H Iw≤P}.

[0056] Sub-problem 3: IB-RIS reflectance design:

[0057] For a given α, β, and w, the objective function in the original weighted sum rate maximization problem (P1) is equivalently simplified to the following form:

[0058]

[0059] in, definition Based on the theory of quadratic constraints and quadratic transformations, the original optimization problem is equivalently transformed into problem (P4):

[0060]

[0061] j∈D, k∈K,

[0062] Among them, variables For vectors The autocorrelation matrix satisfies

[0063] The beneficial effects of this invention are:

[0064] 1. This invention innovatively configures the IB-RIS to an IoT node as a backscattering device to transmit node information, the PB as an energy source to provide radio frequency signals to the IB-RIS, and the AP as an information receiver to receive the backscattered information;

[0065] 2. This invention establishes an IoT collaborative wireless communication system based on IB-RIS and NOMA technologies. The passive working mode of this system improves the system's energy efficiency and extends the lifespan of the IoT collaborative network.

[0066] 3. Based on the proposed system, this invention designs a high-efficiency data transmission scheme. By jointly optimizing the transmit beam of PB and the reflection coefficient of IB-RIS, the system efficiency and data rate are effectively improved. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of a NOMA communication IoT system based on IB-RIS;

[0068] Figure 2 This is a simulation graph showing the relationship between the weighted sum rate and the number of iterations;

[0069] Figure 3 This is a simulation graph showing the relationship between the weighted sum rate and PB transmit power;

[0070] Figure 4 This is a simulation graph showing the relationship between the weighted sum rate and the number of IB-RIS components;

[0071] Figure 5 This is a flowchart of the overall algorithm of this invention. Detailed Implementation

[0072] Specific implementation method one: as follows Figures 1 to 4 As shown, the resource optimization method for a non-orthogonal multiple access communication system based on an information-bearing reconfigurable smart surface includes the following steps:

[0073] Step 1: Assume all links in the system experience flat quasi-static Ricean fading. Correspondingly, PB and wireless device u... j Between, between PB and AP and wireless devices u j The baseband equivalent channel between the AP and the AP can be expressed as: and

[0074] The information modulation process at the j-th RIS is represented as follows:

[0075]

[0076] Where w is the beamforming vector at PB, Θ j =diag(θ) j ) represents the backscattering matrix at the j-th RIS, θ j For the corresponding backscattering vector, s is the carrier signal transmitted by PB to activate the wireless device for backscattering, and it needs to satisfy E[|s| 2 ] = 1, x j This represents the signal to be sent to the AP after the j-th RIS modulation, and satisfies E[|x] j | 2 ] = 1; the backscattering process at IB-RIS is considered as a combination of passive beamforming and information modulation; by θ j s becomes φ j x jThe carrier signal is encoded by IB-RIS to generate new data x. j And φ j Let be the passive beamforming vector at RIS; considering the passive reflection characteristics of RIS, constrain... It must be satisfied, where [A] is one of the following conditions. m,n This represents the element in the m-th row and n-th column of matrix A;

[0077] Step 2: After passive backscattering by IB-RIS at multiple IoT nodes, the signal received at the AP is represented as follows:

[0078]

[0079] Where z represents the zero mean at AP, and the variance is σ. 2 Additive white Gaussian noise;

[0080] Step 3: Define Ψ(j) as the decoding order of the j-th WD signal; specifically including:

[0081] According to the NOMA communication scheme, the AP first decodes the signals of users with good signal conditions and treats the signals of users with poor channel conditions as interference. Then, it decodes the signal x of the j-th WD. j At this point, the signals of wireless device i with a decoding order satisfying Ψ(i) < Ψ(j) have all been decoded and eliminated, while the signals of wireless device j with a decoding order satisfying Ψ(j) > Ψ(p) are considered interference. Without loss of generality, assume Ψ(j) = j, meaning the information from the j-th IoT node is the signal decoded by the j-th node; the decoding at the AP originates from IoT node u. j The signal-to-interference-plus-noise ratio (SIR) of the data stream is expressed as:

[0082]

[0083] Accordingly, the reachable weighted sum rate at AP is Where ω j , where is the weighting factor, representing the priority of the j-th IB-RIS in transmitting IoT node information;

[0084] Step 4: The problem (P1) of maximizing the weighted sum rate of the proposed system by jointly optimizing the active beamforming vector at PB and the passive beamforming of multiple IB-RIS is formulated as follows:

[0085]

[0086] stTr(ww H )≤P,

[0087]

[0088] j∈D, k∈Kj

[0089] Where P is the set of IB-RIS reflection coefficient vectors, and P is the maximum transmit power budget at PB;

[0090] Step 5, weighted sum rate maximization; specifically including:

[0091] The weighted sum of multiple logarithmic functions is handled using the Lagrange dual transformation, specifically expressed as:

[0092]

[0093] Where, α j The auxiliary variable introduced, for a given α, the original objective function is equivalent to maximizing

[0094] Applying quadratic transformation and fractional programming theory, the objective function in problem (P1) is equivalently expressed as:

[0095]

[0096] Where β=[β1,...,β j ,...,β M ] T As an auxiliary variable, variable A j and B j The expression is

[0097]

[0098] Substituting the equivalent transformed objective function into problem (P1), the original weighted sum rate maximization problem is expressed as problem (P2):

[0099]

[0100] stTr(ww H )≤P,

[0101]

[0102] α j ≥0,

[0103] j∈D, k∈K j

[0104] The original problem is decomposed into three subproblems and processed separately. Then, the optimization is performed iteratively and alternately until the weighted sum rate converges.

[0105] Subproblem 1: Solving for the optimal auxiliary variable:

[0106] For a given set of feasible solutions w, P, the optimal solution for auxiliary variables α and β can be obtained by setting the partial derivatives of the objective function in problem (P2) with respect to α and β to zero. The optimal expressions for α and β are:

[0107]

[0108] Sub-problem 2: PB beamforming design:

[0109] For a given α, β, and P, the objective function in the original optimization problem can be equivalently rewritten as:

[0110]

[0111] in, Defined to simplify formula expressions,

[0112]

[0113] Therefore, the original problem (P2) is rewritten as problem (P3):

[0114]

[0115] stTr(ww H )≤P,

[0116] Solving this problem using the Lagrange multiplier method, the Lagrange function corresponding to the objective function of problem (P3) is:

[0117] L(w,λ)=w H Vw-2Re(w H x)+λ(w H Iw-P),

[0118] Where λ is the corresponding Lagrange multiplier, the optimal solution for the active beamforming vector w can be obtained by differentiating the function:

[0119] w opt =(λI+V) -1 x,

[0120] λ opt =min{λ≥0:w H Iw≤P}.

[0121] Sub-problem 3: IB-RIS reflectance design:

[0122] For a given α, β, and w, the objective function in the original weighted sum rate maximization problem (P1) is equivalently simplified to the following form:

[0123]

[0124] in, definition Based on the theory of quadratic constraints and quadratic transformations, the original optimization problem is equivalently transformed into problem (P4):

[0125]

[0126] j∈D, k∈K,

[0127] Among them, variables For vectors The autocorrelation matrix satisfies

[0128] The pseudocode for the overall algorithm to solve problem (P1) is shown below:

[0129] Step 1. Initialize the beamforming vector w at PB 0 The reflection coefficient matrix P0 and weighted sum rate of IB-RIS

[0130] Step 2. Set the iteration number n = 0, and calculate the weighted rate sum for the nth iteration using the following formula.

[0131]

[0132] Step 3. Solve subproblems 1, 2, and 3 by iteratively calculating. Until

[0133] Step 4. Let R max =R (i) .

[0134] like Figure 2 As shown, Figure 2 The relationship between the performance of the proposed algorithm and the number of iterations is presented, reflecting the algorithm's convergence performance. Figure 2 It can be seen that the more IoT nodes there are, the greater the weighted sum rate performance of the proposed algorithm. Although the parameters involved are different, all the proposed resource optimization schemes converged within 8 iterations, which verifies that the proposed scheme has good convergence performance.

[0135] like Figure 3 As shown, Figure 3 The relationship between the weighted sum rate and the transmit power at PB is described. Figure 3As can be seen, the proposed scheme exhibits optimal performance when the PB transmit power exceeds 26 dBm. The performance of the proposed scheme at a PB transmit power of 24 dBm is inferior to that of traditional active antennas. This can be attributed to the fact that the limited transmit power at PB directly affects the signal power reaching the IB-RIS, indirectly impacting the modulation and backscattering performance of the IB-RIS. Furthermore, due to… Figure 3 It can be seen that the proposed scheme based on NOMA communication outperforms the scheme based on time division multiple access communication, which verifies the necessity of selecting the NOMA scheme.

[0136] like Figure 4 As shown, Figure 4 The effect of the number of reflective elements in IB-RIS on the system's weighted sum rate was verified. Figure 4 It can be seen that as the number of reflective elements in the IB-RIS increases, the sum rate of the system also increases accordingly. This is because the increased number of reflective elements enhances the spatial degrees of freedom of the IB-RIS, thereby improving its passive beamforming capability. The proposed scheme exhibits the best performance, and as the number of reflective elements increases, the performance gap between the proposed scheme and traditional active antenna schemes continues to widen, further verifying the effectiveness and superiority of the proposed scheme.

[0137] Example

[0138] The working process of jointly optimizing PB and IB-RIS active and passive beamforming to maximize the weighted sum rate of NOMA communication IoT systems, under the following assumptions:

[0139] 1) The channel is a flat quasi-static Ricean fading channel;

[0140] 2) The path loss model is PL(dB)=PL0-20ρlg(d / d0), PL0=-30dB, where ρ, d, and d0 are the path loss coefficient, transmission distance, and reference distance, respectively.

[0141] 3) PB is located at (0m, 0m), AP is located at (15m, 20m), and all IB-RIS are randomly distributed in a circle with AP as the center and a radius of 10m.

[0142] Other simulation parameters are set as follows: Rice factor is 2, path loss coefficient ρ = 1, reference distance d0 = 1m, number of transmit antennas N = 4, number of IoT nodes M = 4, weighting factor ω j =1, noise power σ at AP 2= -100dBW, PB's transmit power P = 30dBm. All simulation results were obtained by averaging 500 times. To better verify the performance of the proposed scheme, a passive optimization scheme that optimizes only the IB-RIS reflection coefficient, an active optimization scheme that optimizes only the PB beamforming, an IB-RIS joint optimization scheme based on time-division multiple access communication, and a traditional active base station scheme were also proposed. In the traditional base station scheme, a traditional base station with 4 antennas and a transmit power of 6dBm replaces IB-RIS to send road information to the AP.

[0143] like Figure 5 As shown, the specific steps include:

[0144] Step 1: Initialize the beamforming vector w at PB 0 The reflection coefficient matrix p of IB-RIS 0 ;

[0145] Step 2: Set the iteration number n = 0, and calculate the weighted rate sum for the nth iteration using the following formula.

[0146] Step 3: Let n = n + 1;

[0147] Step 4, using P n-1 and w n-1 Solving subproblem 1 yields α. n and β n ;

[0148] Step 5, using α n ,β n and P n-1 Solve subproblem 2 to obtain w. n ;

[0149] Step 6, using α n ,β n and w n Solving subproblem 3, we obtain P. n ;

[0150] Step 7, Calculation

[0151] Step 8: Determine if there is... If the condition is met, proceed to step 9; otherwise, return to step 3.

[0152] Step 9, let

[0153] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A resource optimization method for a non-orthogonal multiple access communication system based on an information-carrying reconfigurable smart surface, characterized in that: The specific steps include: Step 1: Assume all links in the system experience flat, quasi-static Ricean fading. Correspondingly, PB and wireless device u... j Between, between PB and AP and wireless devices u j The baseband equivalent channel between the AP and the AP can be expressed as: and Step 2: After passive backscattering by IB-RIS at multiple IoT nodes, the signal received at the AP is represented as follows: Where z represents the zero mean at AP, and the variance is σ. 2 Additive white Gaussian noise; Step 3: Define Ψ(j) as the decoding order of the j-th WD signal; Step 4: The problem (P1) of maximizing the weighted sum rate of the proposed system by jointly optimizing the active beamforming vector at PB and the passive beamforming of multiple IB-RIS is formulated as follows: (P1) s.t.Tr(ww H )≤P, j∈D,k∈K j Where P is the set of IB-RIS reflection coefficient vectors, and P is the maximum transmit power budget at PB; Step 5: Weighted sum rate maximization.

2. The resource optimization method for a non-orthogonal multiple access communication system based on an information-bearing reconfigurable smart surface according to claim 1, characterized in that, The information modulation process at the j-th RIS in step 1 is represented as follows: Where w is the beamforming vector at PB, Θ j =diag(θ) j ) represents the backscattering matrix at the j-th RIS, θ j For the corresponding backscattering vector, s is the carrier signal transmitted by PB to activate the wireless device for backscattering, and it needs to satisfy E[|s| 2 ] = 1, x j This represents the signal to be sent to the AP after the j-th RIS modulation, and satisfies E[|x] j | 2 ] = 1; the backscattering process at IB-RIS is considered as a combination of passive beamforming and information modulation; by θ j s becomes φ j x j The carrier signal is encoded by IB-RIS to generate new data x. j And φ j Let be the passive beamforming vector at RIS; considering the passive reflection characteristics of RIS, constrain... It must be satisfied, where [A] is one of the following conditions. m,n This represents the element in the m-th row and n-th column of matrix A.

3. The resource optimization method for a non-orthogonal multiple access communication system based on an information-bearing reconfigurable smart surface according to claim 1, characterized in that, Step 3 specifically includes: According to the NOMA communication scheme, the AP first decodes the signals of users with good signal conditions and treats the signals of users with poor channel conditions as interference. Then, it decodes the signal x of the j-th WD. j At this point, the signals of wireless device i with a decoding order satisfying Ψ(i) < Ψ(j) have all been decoded and eliminated, while the signals of wireless device j with a decoding order satisfying Ψ(j) > Ψ(p) are considered interference. Without loss of generality, assume Ψ(j) = j, meaning the information from the j-th IoT node is the signal decoded by the j-th node; the decoding at the AP originates from IoT node u. j The signal-to-interference-plus-noise ratio (SIR) of the data stream is expressed as: Accordingly, the reachable weighted sum rate at AP is Where ω j is a weighting factor, representing the priority of the j-th IB-RIS in transmitting IoT node information.

4. The resource optimization method for a non-orthogonal multiple access communication system based on an information-bearing reconfigurable smart surface according to claim 1, characterized in that, Step 5 specifically includes: The weighted sum of multiple logarithmic functions is handled using the Lagrange dual transformation, specifically expressed as: Where, α j The auxiliary variable introduced, for a given α, the original objective function is equivalent to maximizing Applying quadratic transformation and fractional programming theory, the objective function in problem (P1) is equivalently expressed as: Where β=[β1,...,β j ,...,β M ] T As an auxiliary variable, variable A j and B j The expression is Substituting the equivalent transformed objective function into problem (P1), the original weighted sum rate maximization problem is expressed as problem (P2): (P2) s.t.Tr(ww H )≤P, α j ≥0, j∈D,k∈K j The original problem is decomposed into three subproblems and processed separately. Then, the optimization is performed iteratively and alternately until the weighted sum rate converges. Subproblem 1: Solving for the optimal auxiliary variable: For a given set of feasible solutions w, P, the optimal solution for auxiliary variables α and β can be obtained by setting the partial derivatives of the objective function in problem (P2) with respect to α and β to zero. The optimal expressions for α and β are: a j =c jj , Sub-problem 2: PB beamforming design: For a given α, β, and P, the objective function in the original optimization problem can be equivalently rewritten as: in, Defined to simplify formula expressions, Therefore, the original problem (P2) is rewritten as problem (P3): (P3) s.t.Tr(ww H )≤P, Solving this problem using the Lagrange multiplier method, the Lagrange function corresponding to the objective function of problem (P3) is: L(w,λ)=w H Vw-2Re(w H x)+λ(w H Iw-P), Where λ is the corresponding Lagrange multiplier, the optimal solution for the active beamforming vector w can be obtained by differentiating the function: w opt =(λI+V) -1 x, l opt =min{λ≥0:w H Iw≤P}. Sub-problem 3: IB-RIS reflectance design: For a given α, β, and w, the objective function in the original weighted sum rate maximization problem (P1) is equivalently simplified to the following form: in, definition Based on the theory of quadratic constraints and quadratic transformations, the original optimization problem is equivalently transformed into problem (P4): (P4) j∈D, k∈K, Among them, variables For vectors The autocorrelation matrix satisfies

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