RIS auxiliary passive communication system robust transmission method based on distortion noise

Abstract

The invention relates to a distortion noise-based RIS auxiliary passive communication system robust transmission method, which belongs to the technical field of low-power-consumption Internet of Things, and comprises the following steps: S1, initializing parameters of a distortion noise-based RIS auxiliary passive communication system; s2, according to the maximum transmitting power of a base station, the minimum safety rate of backscattering equipment, energy collection, transmission time, the phase shift of an RIS reflection unit and channel uncertainty constraints, constructing a resource optimization model based on channel uncertainty by taking the maximization of the total energy efficiency of the RIS assisted passive communication system based on distortion noise as an optimization target; s3, converting the resource allocation model containing the channel uncertainty into a deterministic optimization problem model by utilizing a worst criterion and binary Taylor expansion; and then an alternating optimization method, a variable substitution method and a continuous convex approximation method are used for solving, so that the optimal total energy efficiency is obtained.

Description

Technical Field

[0001] The invention belongs to the technical field of low-power Internet of Things, and relates to a robust transmission method of a RIS-assisted passive communication system based on distortion noise. Background Art

[0002] Backscatter communication technology has attracted wide attention in low-power IoT applications due to its low power consumption and low cost. This technology uses existing radio frequency signals for data transmission in a passive way without the need for additional power supply, thereby significantly extending the service life of the device, and is particularly suitable for scenarios with limited resources. However, in complex actual environments, environmental obstacles can interfere with signal propagation, affecting the reliability and transmission efficiency of the system. Since RIS can dynamically optimize the signal path by adjusting the phase offset and power coefficient of the reflector unit, the performance of the passive communication system is significantly improved.

[0003] However, in actual deployment, RIS-assisted passive communication systems still face many challenges. In actual environments, channel state information is difficult to obtain accurately, which makes traditional resource allocation strategies ineffective when dealing with channel uncertainties and increases the possibility of transmission interruptions. In addition, the existence of eavesdropping devices poses a serious threat to information security, especially in the absence of effective protection measures, information transmission is vulnerable to illegal monitoring. Moreover, hardware damage is inevitable in actual communication systems, such as distortion noise, nonlinear distortion, etc. These factors will lead to a decrease in communication performance, and completely idealized transceiver hardware is difficult to achieve in reality. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a robust transmission method for a RIS-assisted passive communication system based on distortion noise. Aiming at the problems of distortion noise, security threats and channel uncertainty in the RIS-assisted passive communication system, the maximum transmission power constraint of the base station, the minimum safe rate constraint of the backscattering device, the energy collection constraint, the transmission time constraint and the phase shift constraint of the RIS are considered, and a network model is established for the RIS-assisted passive communication system with maximizing the system energy efficiency as the optimization goal; the original non-convex optimization problem with parameter uncertainty is converted into a deterministic convex optimization problem for solution by using methods such as the worst criterion, variable substitution, continuous convex approximation and alternating optimization.

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

[0006] A robust transmission method for a RIS-assisted passive communication system based on distortion noise comprises the following steps:

[0007] S1: Initialize the parameters of the RIS-assisted passive communication system based on distorted noise;

[0008] S2: According to the maximum transmission power of the base station, the minimum safe rate of the backscattering device, energy collection, transmission time, phase shift of the RIS reflection unit and channel uncertainty constraints, a resource optimization model based on channel uncertainty is constructed with the total energy efficiency maximization of the RIS-assisted passive communication system based on distortion noise as the optimization goal;

[0009] S3: The resource allocation model with channel uncertainty is converted into a deterministic optimization problem model using the worst-case criterion and binary Taylor expansion. Then, the problem is solved using the alternating optimization method, variable substitution method, and continuous convex approximation method to obtain the optimal overall energy efficiency.

[0010] Furthermore, the parameters of the RIS-assisted passive communication system based on distortion noise in step S1 include: the number of backscattering devices K, the number of reflection units of RIS L, the time frame length T, the maximum transmission power P of the base station max , the minimum safe transmission rate of the kth backscatter link Base station hardware damage factor κ b , the hardware damage factor κ of the information receiver u , the noise variance of the information receiver σ k , the noise variance of the eavesdropping device Circuit energy consumption of the kth backscattering device The circuit energy consumption E of the lth reflection unit of RIS l , maximum number of iterations I max , convergence accuracy ω and number of iterations i.

[0011] Further, the resource allocation model based on channel uncertainty in step S2 is:

[0012]

[0013] C5:0≤θ l ≤2π,

[0014]

[0015] Among them, R k represents the throughput of the kth backscatter device, E sum Represents the total energy consumption of the system, P k represents the transmission power of the base station, Z represents the artificial noise power, represents the distortion noise variance of the base station, t k represents the kth time slot, r k represents the transmission rate of the kth backscattering device, represents the eavesdropping rate of the eavesdropping device in the kth time slot, represents the energy collected by the kth backscattering device, θ lrepresents the phase shift of the lth reflector unit of RIS, Δh e and Δf e,k They represent the actual interference channel estimation error from the base station to the eavesdropping device and the actual interference channel estimation error from the kth backscattering device to the eavesdropping device, respectively. and The corresponding channel uncertainty sets respectively; where:

[0016] R k =t k r k

[0017]

[0018]

[0019] and They represent the uncertainty set between the base station and the eavesdropping device, and the uncertainty set between the kth backscattering device and the eavesdropping device, respectively. and Respectively represent the corresponding channel estimation values, ∈ e and ε e,k They represent the corresponding upper bounds of the channel estimation error respectively;

[0020] C1 represents the maximum transmit power constraint of the base station, C2 ensures the minimum safe rate of the backscatter device, C3 and C4 represent the energy collection constraint and transmission time constraint of the backscatter device respectively, C5 represents the phase shift constraint of the RIS reflection unit, and C6 represents the channel uncertainty set constraint.

[0021] Further, the worst-case criterion and binary Taylor expansion are used in step S3 to convert the resource allocation model containing channel uncertainty into a deterministic optimization problem model, which specifically includes the following steps:

[0022] Based on the worst criterion, C2 is transformed as follows:

[0023]

[0024] in,

[0025] Introduce the binary Taylor expansion:

[0026]

[0027] C2 is rewritten as:

[0028]

[0029] in,

[0030] Using Dinkelbach to process the fractional objective function, we get:

[0031]

[0032] Further, the solution using the alternating optimization method, the variable substitution method and the continuous convex approximation method described in step S3 specifically includes the following steps:

[0033] S31: Fixed RIS phase shift matrix Θ and transmission time t of backscattering device k k , the uncertain non-convex optimization problem model is transformed into a deterministic convex optimization problem model by using the variable substitution method, continuous convex approximation method and alternating optimization method, and the transmission power P of the base station in time slot k is calculated. k and artificial noise power Z;

[0034] S32: Fixed P k and Z, and the uncertain non-convex optimization problem model is transformed into a deterministic convex optimization problem by using the variable substitution method and the continuous convex approximation method to calculate Θ and t k ;

[0035] S33: Determine whether the total energy efficiency of the RIS-assisted passive communication system based on twisted noise converges; if so, output the optimal total energy efficiency η of the RIS-assisted passive communication system based on twisted noise, and then end; otherwise, enter step S34;

[0036] S34: Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output η and end; otherwise, update the current number of iterations i, then enter the next iteration and return to step S31.

[0037] Further, in step S31, the time slot t is calculated. k The transmission power P of the internal base station k And the expression of artificial noise power Z is:

[0038]

[0039] in, and represents the set of slack variables, and They are and The value of the previous iteration.

[0040] Further, in step S32, the phase shift matrix θ of the RIS and the transmission time t of the backscattering device k are calculated. k The expression is:

[0041]

[0042] Further, in step S33, the determination of whether the total energy efficiency of the RIS-assisted passive communication system based on distortion noise converges is specifically as follows:

[0043] When the total energy efficiency of the RIS-assisted passive communication system based on distortion noise in the i-th iteration satisfies When , it converges, otherwise it does not converge.

[0044] The beneficial effects of the present invention are as follows: the present invention provides a resource optimization method that takes into account distortion noise, eavesdropping threats and channel uncertainty to address problems such as communication interruption and information leakage, thereby improving the communication stability of the system in complex environments while ensuring maximization of the overall energy efficiency of the system.

[0045] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] 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 in conjunction with the accompanying drawings, wherein:

[0047] Figure 1 This is a structural diagram of the RIS-assisted passive communication system based on distorted noise according to the present invention;

[0048] Figure 2 This is a flow chart of the robust transmission method of the RIS-assisted passive communication system based on distortion noise according to the present invention;

[0049] Figure 3 The robust transmission method of the RIS-assisted passive communication system based on distortion noise of the present invention is performed at different maximum transmission powers P of the base station. max Energy efficiency convergence diagram below;

[0050] Figure 4 This is a robustness diagram comparing different methods. DETAILED DESCRIPTION

[0051] The following describes the embodiments of the present invention by 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 only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0052] 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 thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0053] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0054] The present invention provides a robust transmission method for a RIS-assisted passive communication system based on distortion noise, considering the downlink transmission scenario of the RIS-assisted passive communication system, such as Figure 1 As shown, the RIS-assisted passive communication system based on twisted noise includes: a base station, K backscattering devices, a RIS with L reflection units, an information receiver, and an eavesdropping device; the base station sends radio frequency information in a broadcasting manner; the backscattering device modulates its information onto the incident signal and reflects it to the information receiver in a time division multiple access manner; the RIS is deployed around the base station to enhance the received signal; at the same time, the eavesdropping device deliberately eavesdrops on the information from the backscattering device.

[0055] like Figure 2 As shown, the method specifically comprises the following steps:

[0056] S1: Initialize the parameters of the RIS-assisted passive communication system based on distorted noise;

[0057] Preferably, the parameters of the RIS-assisted passive communication system based on twisted noise include: the number of backscattering devices K, the number of reflection units of RIS L, the time frame length T, the maximum transmission power P of the base station max , the minimum safe transmission rate r of the kth backscatter link kmin , the hardware damage factor of the base station κ b , the hardware damage factor κ of the information receiver u , the noise variance of the information receiver σ k , the noise variance of the eavesdropping device Circuit energy consumption of the kth backscattering device The circuit energy consumption E of the lth reflection unit of RIS l , maximum number of iterations I max , convergence accuracy ω and number of iterations i.

[0058] S2: Based on channel uncertainty, according to the constraints such as the maximum transmission power of the base station, the minimum safe rate of the backscattering device, energy collection, transmission time, phase shift of the RIS reflection unit and channel uncertainty, a resource optimization model based on channel uncertainty is constructed with the total energy efficiency maximization of the RIS-assisted passive communication system based on distortion noise as the optimization goal;

[0059] Since the location of the eavesdropping device is random, it is difficult to obtain accurate CSI. Therefore, based on the bounded channel uncertainty model, the channel uncertainty set from the base station to the eavesdropping device and from the backscattering device to the eavesdropping device is:

[0060]

[0061] in, and They represent the uncertainty set between the base station and the eavesdropping device, and the uncertainty set between the kth backscattering device and the eavesdropping device, respectively. and They represent the corresponding channel estimation values, Δh e and Δf e,k They represent the corresponding channel estimation errors, ∈ e and ε e,k They respectively represent the corresponding upper bounds of the channel estimation error.

[0062] The resource allocation model constructed based on channel uncertainty can be described as:

[0063]

[0064] C5:0≤θ l ≤2π,

[0065]

[0066] Among them, P k represents the transmission power of the base station, β k represents the reflection coefficient of backscattering device k, ζ krepresents the energy conversion efficiency of the backscattering device k, represents the transmission rate of backscatter device k, R k =t k r k represents the throughput of backscatter device k, represents the transmission rate of the eavesdropping device, represents the energy collected by backscatter device k, Represents the total energy consumption of the system. C1 represents the maximum transmit power constraint of the base station, C2 ensures the minimum safe rate of the backscatter device, C3 and C4 represent the energy collection constraint and transmission time constraint of the backscatter device, respectively, C5 represents the phase shift constraint of the RIS reflection unit, and C6 represents the channel uncertainty set constraint. Due to the uncertainty constraints, coupled optimization variables and non-convex objective function, this problem is a non-convex problem that is difficult to solve directly.

[0067] S3: Use the worst-case criterion and binary Taylor expansion to transform the resource allocation model with channel uncertainty into a deterministic optimization problem model;

[0068] Based on the worst criterion, C2 is transformed as follows:

[0069]

[0070] in, Due to the uncertain parameters of coupling, The value of is difficult to obtain, so the binary Taylor expansion is introduced:

[0071]

[0072] Therefore, C2 can be rewritten as:

[0073]

[0074] in,

[0075] Using Dinkelbach to process the fractional objective function, we can get:

[0076]

[0077] At this point, the optimization problem has been transformed into a deterministic optimization problem. However, due to the objective function and The problem is still a non-convex optimization problem. Therefore, an alternating iterative method based on variable substitution and continuous convex approximation is used to solve it.

[0078] S4: Fixed RIS phase shift matrix Θ and backscatter device k’s transmission time t k, the non-convex optimization problem is transformed into a convex optimization problem by using variable relaxation, variable substitution method and continuous convex approximation method, and the transmission power P of the base station in time slot k is calculated. k and artificial noise power Z;

[0079] r k and The numerator and denominator of k ,b k ) and (c k ,d k ) instead:

[0080]

[0081] a k ≤β k (H k +h b,k )f u,k P k ,

[0082]

[0083] However, due to the fractional form of the optimization variable, the constraint is still non-convex. Therefore, using the continuous convex approximation method and the first-order Taylor expansion method, it can be re-expressed as

[0084]

[0085] in, and They are and The value of the previous iteration. Define the set of slack variables and The problem can be transformed into

[0086]

[0087] This problem is transformed into a convex optimization problem and can be solved directly using the CVX toolbox;

[0088] S5: Fixed P k and Z, and the uncertain non-convex optimization problem model is transformed into a deterministic convex optimization problem by using the variable substitution method and the continuous convex approximation method to calculate Θ and t k :

[0089] Get the variables Θ and t k The sub-optimization problem is:

[0090]

[0091] Since this problem is a convex optimization problem, it can be solved directly using the CVX toolbox.

[0092] S6: Determine whether the total energy efficiency of the RIS-assisted passive communication system based on twisted noise converges; if so, output the optimal total energy efficiency η of the RIS-assisted passive communication system based on twisted noise, and then end; otherwise, enter S7;

[0093] S7: Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output η and end; otherwise, update the current number of iterations i, then enter the next iteration and return to S4.

[0094] The application effect of the present invention is described in detail below in conjunction with simulation.

[0095] Simulation conditions: Assume that the path loss model is represents the fading coefficient, α=3 represents the path loss exponent, d k The other simulation parameters are given in Table 1.

[0096] Table 1

[0097]

[0098] In this embodiment, Figure 3 The energy efficiency convergence diagram of the iterative method in this example is given. Figure 4 The robustness diagram of the iterative method of this example is given. Figure 3 It is shown that the method of the present invention can quickly achieve convergence, thereby proving that the method of the present invention has good convergence. Figure 4 It shows that as the upper bound of the channel estimation error increases, the outage probability of the backscattering device of all methods also increases. Compared with the non-robust algorithm, the outage probability is reduced by 8.78%, which proves that the method of the present invention has strong robustness.

[0099] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0100] In the above-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0101] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0102] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. 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 solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A robust transmission method for a RIS-assisted passive communication system based on distortion noise, characterized in that: The following steps are involved: S1: Initialize the parameters of the RIS-assisted passive communication system based on distorted noise; S2: According to the maximum transmission power of the base station, the minimum safe rate of the backscattering device, energy collection, transmission time, phase shift of the RIS reflection unit and channel uncertainty constraints, a resource optimization model based on channel uncertainty is constructed with the total energy efficiency maximization of the RIS-assisted passive communication system based on distortion noise as the optimization goal; S3: The resource allocation model with channel uncertainty is converted into a deterministic optimization problem model using the worst-case criterion and binary Taylor expansion. Then, the problem is solved using the alternating optimization method, variable substitution method, and continuous convex approximation method to obtain the optimal overall energy efficiency.

2. The method for robust transmission of a RIS-assisted passive communication system based on distortion noise according to claim 1, characterized in that: The parameters of the RIS-assisted passive communication system based on distortion noise in step S1 include: the number of backscattering devices K, the number of reflection units of RIS L, the time frame length T, the maximum transmission power P of the base station max , the minimum safe transmission rate of the kth backscatter link Base station hardware damage factor κ b , the hardware damage factor κ of the information receiver u , the noise variance of the information receiver σ k , the noise variance of the eavesdropping device Circuit energy consumption of the kth backscattering device The circuit energy consumption E of the lth reflection unit of RIS l , maximum number of iterations I max , convergence accuracy ω and number of iterations i.

3. The method for robust transmission of a RIS-assisted passive communication system based on distortion noise according to claim 1, characterized in that: The resource allocation model based on channel uncertainty in step S2 is: C5:0≤θ l ≤2π, Among them, R k represents the throughput of the kth backscatter device, E sum Represents the total energy consumption of the system, P k represents the transmission power of the base station, Z represents the artificial noise power, represents the distortion noise variance of the base station, t k represents the kth time slot, r k represents the transmission rate of the kth backscattering device, represents the eavesdropping rate of the eavesdropping device in the kth time slot, represents the energy collected by the kth backscattering device, θ l represents the phase shift of the lth reflector unit of RIS, Δh e and Δf e,k They represent the actual interference channel estimation error from the base station to the eavesdropping device and the actual interference channel estimation error from the kth backscattering device to the eavesdropping device, respectively. and The corresponding channel uncertainty sets respectively; where: R k =t k r k and They represent the uncertainty set between the base station and the eavesdropping device, and the uncertainty set between the kth backscattering device and the eavesdropping device, respectively. and Respectively represent the corresponding channel estimation values, ∈ e and ε e,k They represent the corresponding upper bounds of the channel estimation error respectively; C1 represents the maximum transmit power constraint of the base station, C2 ensures the minimum safe rate of the backscatter device, C3 and C4 represent the energy collection constraint and transmission time constraint of the backscatter device respectively, C5 represents the phase shift constraint of the RIS reflection unit, and C6 represents the channel uncertainty set constraint.

4. The method for robust transmission of a RIS-assisted passive communication system based on distortion noise according to claim 1, characterized in that: Step S3 uses the worst-case criterion and binary Taylor expansion to convert the resource allocation model containing channel uncertainty into a deterministic optimization problem model, which specifically includes the following steps: Based on the worst criterion, C2 is transformed as follows: in, Introduce the binary Taylor expansion: C2 is rewritten as: in, Using Dinkelbach to process the fractional objective function, we get:

5. The method for robust transmission of a RIS-assisted passive communication system based on distortion noise according to claim 4, characterized in that: The solution using the alternating optimization method, the variable substitution method and the continuous convex approximation method described in step S3 specifically includes the following steps: S31: Fixed RIS phase shift matrix Θ and transmission time t of backscattering device k k , the uncertain non-convex optimization problem model is transformed into a deterministic convex optimization problem model by using the variable substitution method, continuous convex approximation method and alternating optimization method, and the transmission power P of the base station in time slot k is calculated. k and artificial noise power Z; S32: Fixed P k and Z, and the uncertain non-convex optimization problem model is transformed into a deterministic convex optimization problem by using the variable substitution method and the continuous convex approximation method to calculate Θ and t k ; S33: Determine whether the total energy efficiency of the RIS-assisted passive communication system based on twisted noise converges; if so, output the optimal total energy efficiency η of the RIS-assisted passive communication system based on twisted noise, and then end; otherwise, enter step S34; S34: Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output η and end; otherwise, update the current number of iterations i, then enter the next iteration and return to step S31.

6. The method for robust transmission of a RIS-assisted passive communication system based on distortion noise according to claim 5, characterized in that: In step S31, the time slot t is calculated. k The transmission power P of the internal base station k And the expression of artificial noise power Z is: in, and represents the set of slack variables, and They are and The value of the previous iteration.

7. The method for robust transmission of a RIS-assisted passive communication system based on distortion noise according to claim 6, characterized in that: In step S32, the phase shift matrix θ of the RIS and the transmission time t of the backscattering device k are calculated. k The expression is:

8. The method for robust transmission of a RIS-assisted passive communication system based on distortion noise according to claim 7, characterized in that: In step S33, the determination of whether the total energy efficiency of the RIS-assisted passive communication system based on distortion noise converges is specifically as follows: When the total energy efficiency of the RIS-assisted passive communication system based on distorted noise in the i-th iteration satisfies When , it converges, otherwise it does not converge.

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