Resource optimization method for symbiotic radio networks under separation channel uncertainty

Abstract

The present invention relates to a method for optimizing symbiotic radio network resources under separation channel uncertainty, and belongs to the field of mobile communication technology. The present invention addresses the problem of transmission link instability caused by the limited signal processing capability of backscattering devices in symbiotic radio networks. Considering constraints such as the minimum energy collection of backscattering devices, the maximum transmission power of the main base station, the transmission time / reflection coefficient of the backscattering devices, the uncertainty of all link channels, and the service quality of the main base station and the backscattering devices, a throughput maximization robust resource allocation model based on separation channel uncertainty and nonlinear energy collection model is constructed. The worst-case criterion, Lagrangian duality theory, continuous convex approximation method, and variable substitution method are used to transform the original resource allocation model into a deterministic convex optimization model for solution. The present invention can effectively improve the total throughput of the system while reducing the transmission interruption probability of the device.

Description

Technical Field

[0001] The invention belongs to the technical field of mobile communications and relates to a method for optimizing symbiotic radio network resources under separation channel uncertainty. Background Art

[0002] With the rapid growth of data traffic and IoT devices, spectrum shortages and high energy consumption have become major challenges facing the IoT. Symbiotic radio, by allowing primary and secondary users to share the same spectrum resources and utilizing energy harvesting at the secondary user site, effectively improves network spectrum and energy efficiency, becoming a key technology for sixth-generation IoT applications. Resource allocation technology dynamically adjusts power, time, and channel allocation within the network to achieve optimal network performance while ensuring user quality of service.

[0003] However, due to the weak signal processing capabilities of backscatter devices in symbiotic radio networks, channel estimation for the reflection links is inaccurate. Furthermore, the channel estimation for the cascaded reflection links does not reflect actual channel variations, leading to communication interruptions for users. Furthermore, channel delay and quantization errors introduced during channel estimation are unavoidable. Therefore, a method for optimizing throughput performance and managing resources in symbiotic radio network scenarios that considers separation channel uncertainty is urgently needed to address these issues. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a symbiotic radio network resource optimization method under separated channel uncertainty. Aiming at the problem of transmission link instability caused by the limited signal processing capability of the backscattering device in the symbiotic radio network, the present invention considers the constraints such as the minimum energy collection of the backscattering device, the maximum transmission power of the main base station, the transmission time / reflection coefficient of the backscattering device, the uncertainty of all link channels, the service quality of the main base station and the backscattering device, and takes maximizing the total system throughput as the optimization goal. A network model is established for the symbiotic radio system based on separated channel uncertainty and nonlinear energy collection. The worst-case criterion, Lagrangian duality theory, continuous convex approximation method and variable substitution method are used to transform the original uncertain non-convex problem into a deterministic convex optimization problem for solution.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for optimizing symbiotic radio network resources under separation channel uncertainty, the method specifically comprising the following steps:

[0007] S1: Initialize the parameters of the co-existing radio network under separation channel uncertainty;

[0008] The symbiotic radio network under separation channel uncertainty includes an information receiver, K backscatter devices, and a master base station, all of which are equipped with a single antenna; the master base station sends a signal to the information receiver; the backscatter device modulates its information onto the incident signal using time division multiple access and transmits it to the information receiver;

[0009] S2: Considering the constraints of minimum energy collection of backscatter devices, maximum transmit power of the primary base station, transmission time / reflection coefficient of backscatter devices, channel uncertainty of all links, and service quality of the primary base station and backscatter devices, a resource allocation model is constructed with the optimization goal of maximizing the total throughput of the symbiotic radio network under separated channel uncertainty.

[0010] S3: Use the worst-case criterion, Lagrangian duality theory, continuous convex approximation method and variable substitution method to transform the non-convex resource allocation model into a deterministic convex optimization resource allocation model to obtain the optimal total throughput.

[0011] Furthermore, in said S1, the parameters of the symbiotic radio network under the separation channel uncertainty include: the number of backscattering devices K, the time frame length T, the noise variance σ at the information receiver 2 , the minimum rate threshold of the direct link in time slot k Minimum rate threshold of reflection link in time slot k Maximum transmit power threshold P of the primary base station max , the minimum energy collection threshold of backscatter device k Constants a, b, and c related to circuit specifications, and direct link channel estimation Channel estimation value of the kth forward reflection link Channel estimation value of the kth backreflection link The upper bound of the direct link channel estimation error ε, the upper bound of the k-th forward reflection link channel estimation error ν k , the upper bound of the channel estimation error of the kth backreflection link ∈ k , the total throughput Q of the symbiotic radio network under separation channel uncertainty, the maximum number of iterations D max , convergence accuracy ω and number of iterations d.

[0012] Furthermore, in S2, the total throughput maximization resource allocation model constructed is:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018]

[0019] C6:0≤β k ≤1,

[0020]

[0021] Among them, P k represents the primary base station transmission power in time slot k, t k represents the transmission time of backscatter device k, β k represents the reflection coefficient of backscattering device k, represents the total throughput including parameter uncertainty, represents the direct link rate including parameter uncertainty in time slot k, h represents the direct link channel gain, g k represents the kth forward reflection link channel gain, f k represents the kth back-reflection link channel gain, represents the reflection link rate including parameter uncertainty in time slot k, represents the incident signal power of backscatter device k including parameter uncertainty.

[0022] Furthermore, in S3, the non-convex resource allocation model is converted into a deterministic convex optimization resource allocation model, specifically:

[0023]

[0024]

[0025]

[0026]

[0027] C4-C6,

[0028] in, represents the deterministic total throughput, represents the deterministic direct link rate in time slot k, represents auxiliary variables, represents the deterministic reflection link rate in time slot k, represents auxiliary variables, represents the energy collected by backscatter device k, represents the nonlinear energy harvesting model for backscattering device k, represents the deterministic input signal power of the backscatter device k.

[0029] Furthermore, the step S3 specifically includes the following steps:

[0030] S31: Fixed t k and β k , using Lagrange duality theory to obtain P k ;

[0031] S32: P obtained according to S31 k , the non-convex optimization problem is transformed into a convex optimization problem by using the variable substitution method and the continuous convex approximation method, and t is calculated. k and β k ;

[0032] S33: Based on the obtained P k , t k and β k , update the total throughput Q of the symbiotic radio network under separation channel uncertainty;

[0033] S34: Determine whether the total throughput of the symbiotic radio network under the separation channel uncertainty converges; if so, output the optimal total throughput Q of the symbiotic radio network under the separation channel uncertainty * , then end; otherwise, enter S35;

[0034] S35: Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output Q * , then end, otherwise, update the current iteration number d, then enter the next iteration, and return to S31.

[0035] Furthermore, in S31, P is calculated k The expression is:

[0036]

[0037] Where [x] + =max(0,x) means taking x greater than 0, and Represents P k The d-th iteration and the d-1-th iteration of , Δ1 represents a non-negative step size, γ, γ k,1 , γ k,2 and γ k,3 represents non-negative Lagrange multipliers;

[0038]

[0039] Furthermore, in S32, t is calculated k and β k The convex optimization problem is:

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] in, represents the slack variable,

[0046] in, and Respectively represent t k and The dth iteration of

[0047]

[0048] Furthermore, in S33, the total throughput Q of the symbiotic radio network under the separation channel uncertainty is updated to:

[0049]

[0050] Furthermore, in S34, it is determined whether the total throughput of the symbiotic radio network under the separated channel uncertainty converges. Specifically, when the total throughput of the symbiotic radio network under the separated channel uncertainty in the d-th iteration satisfies Q(d)-Q(d-1)|≤ω, it converges; otherwise, it does not converge.

[0051] The present invention improves network throughput while ensuring device quality of service (QoS) and reduces device outage probability. Compared to existing non-robust methods, linear energy harvesting methods, and cascaded channel uncertainty methods, the proposed solution offers higher throughput performance and greater robustness.

[0052] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0054] Figure 1 A diagram of a symbiotic radio network model under separation channel uncertainty in the present invention;

[0055] Figure 2 This is a flow chart of the method for optimizing symbiotic radio network resources under separation channel uncertainty according to the present invention;

[0056] Figure 3 is a throughput performance diagram of the symbiotic radio network resource optimization method under separation channel uncertainty of the present invention;

[0057] Figure 4 This is a robustness diagram of the coexisting radio network resource optimization method under separated channel uncertainty of the present invention. DETAILED DESCRIPTION

[0058] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0059] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0060] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0061] See also Figures 1 to 4 The present invention provides a robust optimization method for a symbiotic radio network under separation channel uncertainty, wherein Figure 1As shown in FIG, a symbiotic radio system for physical layer security includes an information receiver, K backscatter devices, and a master base station, all of which are equipped with a single antenna. The master base station sends a signal to the information receiver. The backscatter devices modulate their information onto the incident signal using time division multiple access and transmit it to the information receiver.

[0062] like Figure 2 As shown, the method specifically includes the following steps:

[0063] S1: Initialize the parameters of the co-existing radio network under separation channel uncertainty;

[0064] The parameters of the symbiotic radio network under separation channel uncertainty include: the number of backscattering devices K, the time frame length T, the noise variance σ at the information receiver 2 , the minimum rate threshold of the direct link in time slot k Minimum rate threshold of reflection link in time slot k Maximum transmit power threshold P of the primary base station max , the minimum energy collection threshold of backscatter device k Constants a, b, and c related to circuit specifications, and direct link channel estimation Channel estimation value of the kth forward reflection link Channel estimation value of the kth backreflection link The upper bound of the direct link channel estimation error ε, the upper bound of the k-th forward reflection link channel estimation error ν k , the upper bound of the channel estimation error of the kth backreflection link ∈ k , the total throughput Q of the symbiotic radio network under separation channel uncertainty, the maximum number of iterations D max , convergence accuracy ω and number of iterations d.

[0065] S2: Considering the constraints of minimum energy harvesting of backscatter devices, maximum transmit power of the primary base station, transmission time / reflection coefficient of backscatter devices, channel uncertainty of all links, and quality of service of the primary base station and backscatter devices, a resource allocation model is constructed based on a nonlinear energy harvesting model with the optimization goal of maximizing the total throughput of the symbiotic radio network under separated channel uncertainty.

[0066] Since the nonlinear energy harvesting model can well capture the nonlinear characteristics of the actual energy harvester, it can improve the energy harvesting efficiency. By making full use of the nonlinear relationship, the nonlinear energy harvesting model can more effectively convert the input energy into output electrical energy and provide higher energy conversion efficiency. Therefore, the nonlinear energy harvesting model is modeled as

[0067]

[0068] in, represents the input signal power of backscatter device k, P k represents the transmit power of the primary base station in time slot k, and h represents the direct link channel gain.

[0069] Due to the limited signal processing capabilities of passive backscatter devices and the existence of channel delay and quantization errors, it is difficult to obtain perfect CSI in a symbiotic radio network. Therefore, based on the bounded CSI error model, the following channel uncertainty set is established:

[0070]

[0071] in, represents the channel uncertainty set, and Denote the channel estimation values ​​of the direct link, the kth forward reflection link, and the kth backward reflection link, Δh and Δg respectively. k and Δf k Respectively represent the corresponding channel estimation errors, ε, ∈ k and ν k They represent the corresponding upper bounds of the channel estimation error.

[0072] The total throughput maximization resource allocation model constructed is:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] C6:0≤β k ≤1,

[0080]

[0081] Among them, t k represents the transmission time of backscatter device k, β k represents the reflection coefficient of backscattering device k, represents the total throughput including parameter uncertainty, represents the direct link rate including parameter uncertainty in time slot k, g k represents the kth forward reflection link channel gain, f k represents the kth back-reflection link channel gain, represents the reflection link rate including parameter uncertainty in time slot k, represents the incident signal power at backscatter device k, including parameter uncertainty. C1 and C2 guarantee the quality of service of the primary base station and backscatter device, respectively. C3 is the minimum energy collection constraint for each backscatter device. C4 is the maximum transmit power constraint for the primary base station. C5 is the transmission time constraint. C6 is the reflection coefficient constraint for each backscatter device. C7 is the channel uncertainty constraint. Because the objective function and constraints contain coupled optimization variables and the constraints contain channel uncertainty, this problem is a difficult non-convex optimization problem.

[0082] S3: Use the worst-case criterion, Lagrangian duality theory, continuous convex approximation method and variable substitution method to transform the non-convex resource allocation model into a deterministic convex optimization resource allocation model:

[0083] Based on the worst-case scenario, C2 is converted as follows

[0084]

[0085] in, Due to the uncertainty of the coupling parameter, f(Δf k ,Δg k ) is difficult to obtain. Therefore, the following problem is constructed

[0086]

[0087] st|Δf k |≤ν k ,|Δg k |≤∈ k .

[0088] Based on the Carlo-Kuhn-Tucker condition, we can obtain f(Δf k ,Δg k ) has a minimum value of Therefore, C2 can be rewritten as

[0089]

[0090] in, Represents auxiliary variables.

[0091] Similarly, C1 and C3 can also be rewritten as

[0092]

[0093]

[0094] in, represents auxiliary variables,

[0095] Therefore, the deterministic convex optimization problem model is:

[0096]

[0097]

[0098]

[0099]

[0100] C4-C6,

[0101] in, represents the deterministic total throughput. However, since the objective function and constraints contain coupled optimization variables, this problem remains a non-convex optimization problem. Therefore, based on the alternating optimization method, the solution of the optimization variable in each subproblem is obtained through iterative optimization.

[0102] S4: Fixed t k and β k , using Lagrange duality theory to obtain P k ;

[0103] Get information about P k Suboptimization problem

[0104]

[0105]

[0106] This subproblem is a convex optimization problem, so the Lagrange duality theory can be used to obtain P k The closed-form solution of this subproblem is

[0107]

[0108] Among them, γ, γ k,1 , γ k,2 and γ k,3 represents the non-negative Lagrange multiplier. Based on the Carlos-Kuhn-Tucker condition, P k The closed-form solution expression is

[0109]

[0110] Where [x] + =max(0,x) means taking x greater than 0, and Represents P k At the d-th and d-1-th iterations of , Δ1 represents a non-negative step size.

[0111]

[0112] Update the Lagrange multiplier using subgradient descent

[0113]

[0114]

[0115]

[0116]

[0117] Where Δ2, Δ3, Δ4, and Δ5 represent non-negative step sizes.

[0118] S5: Based on the obtained P k , the non-convex optimization problem is transformed into a convex optimization problem by using the variable substitution method and the continuous convex approximation method, and t is calculated. k and β k ;

[0119] make Then we get the value of t k and Suboptimization problem

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] in, Since in the objective function Due to the non-convexity of , this sub-problem is a non-convex optimization problem. Therefore, the continuous convex approximation method is used in an iterative manner to obtain its suboptimal solution.

[0126]

[0127] in, and Respectively represent t k and The dth iteration of

[0128]

[0129] Therefore, the above subproblems can be transformed into

[0130]

[0131]

[0132] It is easy to see that the converted problem is a convex optimization problem, so it can be solved using standard convex optimization methods.

[0133] S6: Based on t k 、P k and β k , update the total throughput Q of the symbiotic radio network under separation channel uncertainty, where

[0134] S7: Determine whether the total throughput of the symbiotic radio network under the separation channel uncertainty converges; if so, output the optimal total throughput Q of the symbiotic radio network under the separation channel uncertainty * , then end; otherwise, enter S8.

[0135] S8: Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output Q * , then end, otherwise, update the current iteration number d, then enter the next iteration and return to S4.

[0136] The application effect of the present invention is described in detail below with reference to simulation.

[0137] Simulation conditions: Assume that the path loss model is Where ρ represents the path loss factor, d k represents the distance between any two devices, and α=3 represents the path loss exponent. Other simulation parameters are given in Table 1:

[0138] Table 1

[0139]

[0140] Simulation results: In this simulation experiment, Figure 3 The change of total system throughput under the influence of channel uncertainty is given. Figure 4 The interruption probability change of the direct link is given. Figure 3 It can be seen from the above that compared with the linear energy harvesting method, the method of the present invention has a significant improvement in the total system throughput. Figure 4 It can be seen from the figure that the proposed method has the best robustness because it takes the separated channel uncertainty into consideration. In addition, compared with the linear energy harvesting method, the probability of direct link interruption is reduced by 25.28%.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing symbiotic radio network resources under separation channel uncertainty, characterized by: The method specifically comprises the following steps: S1: Initialize the parameters of the co-existing radio network under separation channel uncertainty; The symbiotic radio network under separation channel uncertainty includes an information receiver, K backscatter devices, and a master base station, all of which are equipped with a single antenna; the master base station sends a signal to the information receiver; the backscatter device modulates its information onto the incident signal using time division multiple access and transmits it to the information receiver; S2: Considering the constraints of minimum energy collection of backscatter devices, maximum transmit power of the primary base station, transmission time / reflection coefficient of backscatter devices, channel uncertainty of all links, and service quality of the primary base station and backscatter devices, a resource allocation model is constructed with the optimization goal of maximizing the total throughput of the symbiotic radio network under separated channel uncertainty. S3: Use the worst-case criterion, Lagrangian duality theory, continuous convex approximation method, and variable substitution method to transform the non-convex resource allocation model into a deterministic convex optimization resource allocation model to obtain the optimal total throughput; In S1, the parameters of the symbiotic radio network under the separation channel uncertainty include: the number of backscatter devices K, the time frame length T, the noise variance σ at the information receiver 2 , the minimum rate threshold of the direct link in time slot k Minimum rate threshold of reflection link in time slot k Maximum transmit power threshold P of the primary base station max , the minimum energy collection threshold of backscatter device k Constants a, b, and c related to circuit specifications, and direct link channel estimation Channel estimation value of the kth forward reflection link Channel estimation value of the kth backreflection link The upper bound of the direct link channel estimation error ε, the upper bound of the k-th forward reflection link channel estimation error ν k , the upper bound of the channel estimation error of the kth backreflection link ∈ k , the total throughput Q of the symbiotic radio network under separation channel uncertainty, the maximum number of iterations D max , convergence accuracy ω and number of iterations d; In S2, the total throughput maximization resource allocation model constructed is: C6:0≤β k ≤1, Among them, P k represents the primary base station transmission power in time slot k, t k represents the transmission time of backscatter device k, β k represents the reflection coefficient of backscattering device k, represents the total throughput including parameter uncertainty, represents the direct link rate including parameter uncertainty in time slot k, h represents the direct link channel gain, g k represents the kth forward reflection link channel gain, f k represents the kth back-reflection link channel gain, represents the reflection link rate including parameter uncertainty in time slot k, represents the incident signal power of backscatter device k including parameter uncertainty; In S3, the non-convex resource allocation model is converted into a deterministic convex optimization resource allocation model, specifically: C4-C6, in, represents the deterministic total throughput, represents the deterministic direct link rate in time slot k, represents auxiliary variables, represents the deterministic reflection link rate in time slot k, represents auxiliary variables, represents the energy collected by backscatter device k, represents the nonlinear energy harvesting model for backscattering device k, represents the deterministic input signal power of backscatter device k; The S3 specifically includes the following steps: S31: Fixed t k and β k , using Lagrange duality theory to obtain P k ; Calculate P k The expression is: Where [x] + =max(0,x) means taking x greater than 0, and Represents P k The d-th iteration and the d-1-th iteration of , Δ1 represents a non-negative step size, γ, γ k,1 , γ k,2 and γ k,3 represents non-negative Lagrange multipliers; S32: P obtained according to S31 k , the non-convex optimization problem is transformed into a convex optimization problem by using the variable substitution method and the continuous convex approximation method, and t is calculated. k and β k Calculate t k and β k The convex optimization problem is: in, represents the slack variable, in, and Respectively represent t k and The dth iteration of S33: Based on the obtained P k , t k and β k , update the total throughput Q of the symbiotic radio network under separation channel uncertainty; S34: Determine whether the total throughput of the symbiotic radio network under the separation channel uncertainty converges; if so, output the optimal total throughput Q of the symbiotic radio network under the separation channel uncertainty * , then end; otherwise, enter S35; S35: Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output Q * , then end, otherwise, update the current iteration number d, then enter the next iteration, and return to S31.

2. The method for optimizing symbiotic radio network resources under separation channel uncertainty according to claim 1, characterized in that: In S33, the total throughput Q of the symbiotic radio network under the separation channel uncertainty is updated to:

3. The method for optimizing symbiotic radio network resources under separation channel uncertainty according to claim 2, characterized in that: In the S34, whether the total throughput of the symbiotic radio network under the separated channel uncertainty converges is determined. Specifically, when the total throughput of the symbiotic radio network under the separated channel uncertainty in the d-th iteration satisfies |Q(d)-Q(d-1)|≤ω, it converges; otherwise, it does not converge.

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