Traversal capacity analysis and element configuration optimization method of hybrid RIS auxiliary communication system under hardware damage

Abstract

The invention relates to a system analysis method for traversal capacity and energy efficiency of a hybrid active-passive reconfigurable intelligent reflector assisted wireless communication system under the condition of hardware damage. According to the method, a hybrid RIS (Hybrid Active-Passive Receiving Surveillance) auxiliary communication system model considering non-ideal factors of hardware is established, the signal to interference plus noise ratio of a user is deduced under a Rayleigh channel, an optimal phase is selected, and an analytical expression of traversal capacity and energy efficiency is further given. Based on an energy efficiency maximization criterion, a hybrid RIS element configuration optimization method is provided. A simulation result verifies the correctness of theoretical derivation, which indicates that the analysis and optimization method provided by the invention can effectively evaluate the system performance and effectively improve the system energy efficiency.

Description

Technical field:

[0001] The present invention belongs to wireless communication technology and includes performance analysis of a communication system, mainly focusing on performance analysis of the traversal capacity of a hybrid active-passive RIS-assisted communication system under hardware damage and component configuration optimization. Background technology:

[0002] With the continuous advancement of science and technology, the demand for higher data transmission speeds and communication quality is increasing. Although 5G technology can adapt to changes in the communication environment, the random nature of signal propagation still makes it difficult to fully control the communication environment. Against this backdrop, Reconfigurable Intelligent Surface (RIS) technology, a rising star in communication technology, has demonstrated many impressive advantages. It not only significantly improves the performance of communication systems, but also greatly enhances system flexibility and adaptability, effectively reduces system power consumption, and brings new opportunities for the development of the communications industry.

[0003] However, traditional quasi-passive RIS systems induce multiplicative fading during signal transmission, limiting their signal amplification and processing capabilities. While active RIS systems can amplify signals, they inevitably introduce thermal noise, resulting in higher power consumption. Therefore, recent research has proposed a novel hybrid active-passive RIS (Hybrid RIS) architecture. This technology organically combines the technical advantages of both types of components, retaining the low-noise characteristics of passive systems while also introducing controllable signal enhancement capabilities. Compared to both, the hybrid RIS can reduce system power consumption and improve energy efficiency by rationally configuring the operating modes of active and passive components while still meeting communication requirements. This makes it a highly valuable research option and offers promising prospects.

[0004] Current research on hybrid RIS-assisted wireless communication systems, such as in Reference 1 (M. Jian, G.C. Alexandropoulos, E. Basar, C. Huang, R. Liu, Y. Liu, and C. Yuen, “Reconfigurable intelligent surfaces for wireless communications: Overview of hardware designs, channel models, and estimation techniques,” Intelligent and Converged Networks, vol. 3, pp. 1-32, Mar. 2022), has achieved results in channel modeling, beamforming optimization, and resource allocation. However, the theoretical assumptions remain disconnected from real-world scenarios. Most work is based on ideal hardware environments, ignoring transmitter impairments such as nonlinear power amplifiers, oscillator phase noise, I / Q imbalance, and quantization error, as well as receiver impairments such as oscillator mismatch and Doppler-induced carrier frequency offset (CFO). This results in insufficient robustness of the proposed algorithms in practical applications, and significant discrepancies between simulation and test performance.

[0005] Previous research (T. Schenk, RF Imperfections in High-rate Wireless Systems: Impact and Digital Compensation. Springer Science & Business Media, Jan. 2008) derived closed-form expressions for outage probability and ergodic capacity under hardware impairments and conducted performance analysis in terahertz systems. However, this work has primarily focused on traditional relay systems or single RIS architectures, and research on hardware impairments and system performance analysis in hybrid RIS architectures remains scarce. Therefore, this paper investigates ergodic capacity analysis of a hybrid RIS-assisted communication system under hardware impairments, as well as a component configuration optimization method aimed at maximizing system energy efficiency. Summary of the invention:

[0006] In order to study the system performance more accurately and conveniently, this paper constructs a communication system model under Rayleigh channel, derives the user signal-to-interference-and-noise ratio, selects the optimal phase, gives the ergodic capacity and energy efficiency expressions, and optimizes the component configuration of hybrid RIS with the goal of maximizing energy efficiency.

[0007] The specific implementation technology method is: traversal energy efficiency analysis and component configuration optimization analysis method in hybrid active-passive RIS-assisted system. The specific operation steps are as follows:

[0008] S1. Considering the far-field hybrid active-passive reconfigurable intelligent surface (Hybrid Active-Passive Reconfigurable Intelligent Surface) assisted wireless communication system, the model includes a single-antenna base station, a hybrid active-passive RIS, and a single-antenna user. The hybrid RIS consists of N act Active reflective elements, N pas passive reflective elements.

[0009] S2. All channels are modeled as Rayleigh channels, and there is no direct link between the base station and the user. The channel between the base station and the active or passive components is defined as g B R,i,i∈{act,pas}, the channel between active or passive components and users is defined as h RU,i ,satisfy L BR 、L RU is the path loss, are all small-scale Rayleigh fading channels, σ g ,σ h are the parameters of the Rayleigh channel between the base station and the hybrid RIS, and between the hybrid RIS and the user, that is, the standard deviation of Rayleigh fading. Θ and Ψ represent the reflection coefficient matrices of the passive and active components, respectively. For simplified formulas, let The user-side ergodic capacity expression can be obtained from Jason's inequality:

[0010]

[0011] in,

[0012] P B is the transmission power, φ t ,φ r are transmitter hardware damage and receiver hardware damage respectively, and κ represents the transceiver hardware damage level; z represents the thermal noise introduced by active components, satisfying n s Represents the additive Gaussian white noise at the user, satisfying

[0013] S3. Based on the derived user ergodic capacity and the average total power consumption of the system, the expression for energy efficiency can be obtained:

[0014]

[0015] S4. Establish a hybrid RIS component configuration optimization problem, whose optimization goal is to maximize energy efficiency and the optimization variable is N. act, the optimization constraint is N act ∈[0, N], and N act ∈Z;

[0016] S5: Use the quadratic iteration method to introduce the iteration variable y, and Dinkelbach to introduce the iteration variable z. The two optimization methods are combined to simplify the problem in S3, so that the original formula can find an optimal solution.

[0017] S6. As y and z are updated, the optimized formula is derived to obtain an extreme point in the feasible region and update N act , until the algorithm converges.

[0018] The present invention can bring the following positive effects:

[0019] The present invention takes hardware impairments into account during system modeling, which is consistent with actual application scenarios. According to the implementation method of the present invention, ergodic capacity and energy efficiency can be derived, and the component configuration of the hybrid RIS can be optimized based on the criterion of maximizing energy efficiency. This provides an efficient and simple method for performance analysis and optimization of hybrid active-passive RIS-assisted communication systems under hardware impairments. Description of the drawings:

[0020] Figure 1 This is a theoretical derivation diagram of the ergodic capacity analysis and component configuration optimization method of the hybrid RIS-assisted wireless communication system under hardware damage in an example of the present invention.

[0021] Figure 2 This is a system modeling diagram in an example of the present invention.

[0022] Figure 3 This is a diagram showing how the simulated and theoretical values of energy efficiency change with transmit power under different hardware damage scenarios of the present invention.

[0023] Figure 4 This is a graph showing how energy efficiency varies with the total number of components in the hybrid RIS under different active component ratios of the present invention. Specific implementation method:

[0024] The following is a detailed description of the present invention with reference to the accompanying drawings.

[0025] 1. Analytical Method Process

[0026] The present invention provides a method for traversal capacity analysis and component configuration optimization of a hybrid RIS-assisted communication system under hardware damage. Figure 1 shown.

[0027] 2. System Model

[0028] Consider a hybrid active-passive RIS-assisted wireless communication system with transceiver hardware impairments, such as Figure 2 As shown in Figure 2, downlink communication is established between a single-antenna base station and a user, with the communication connection provided by a hybrid RIS, which is assumed to consist of two co-located sub-surfaces, each containing passive and active reflective elements. Furthermore, the system design assumes that the distance between the base station and the user is large enough that direct communication links are negligible due to path obstructions. The channel state information (CSI) between the base station and the RIS, and between the RIS and the user, is fully known.

[0029] All channels are modeled as Rayleigh channels, and there is no direct link between the base station and the user. The channel between the base station and the active or passive components is defined as g BR,i ,i∈{act,pas}, the channel between active or passive components and users is defined as h RU,i , the channel satisfies and L BR 、L RU is the path loss, are all small-scale Rayleigh fading channels, σ g ,σ h are the parameters of the Rayleigh channel between the base station and the hybrid RIS, and between the hybrid RIS and the user, i.e., the standard deviation of Rayleigh fading. Θ and Ψ represent the reflection coefficient matrices of the passive and active components, respectively, and are defined as To simplify the formula, let Then, the received signal at the user end is:

[0030]

[0031] Where x represents the user's transmission symbol vector, P B is the transmission power, satisfying E{|x| 2 =P B φ t ,φ r are transmitter hardware damage and receiver hardware damage respectively, and κ represents the non-negative parameter of the transmitter error vector; z represents the thermal noise introduced by the active components, satisfying n s Represents the additive Gaussian white noise at the user, satisfying 3. Derivation of Signal-to-Interference-Noise Ratio, Ergodic Capacity, and Energy Efficiency

[0032] On this basis, we substitute the amplitude and phase response of the reflective element and let

[0033]

[0034] The signal-to-interference-noise ratio expression of the system is:

[0035]

[0036] in Given any feasible active or passive component allocation and active component amplitude amplification factor, it can be concluded that when Act and Pas are phase aligned, the above formula reaches its maximum value, and the theoretically optimal phase can be given by:

[0037]

[0038] After substitution, the original formula can be simplified to:

[0039]

[0040] According to the binomial expansion theorem and the approximation of fractional expectation:

[0041]

[0042] Since the channel model is set to Rayleigh fading channel, g BR，i , h RU,i The expectation and variance of can be expressed as:

[0043]

[0044] When the mixed RIS has enough elements, based on the central limit theorem, all g BR,i , h RU,i are all independent and identically distributed. According to this method, we can also derive:

[0045]

[0046] Substituting equations (7), (8), and (9) into equation (5), we can obtain the complete expression of the expected signal-to-interference-noise ratio as follows:

[0047]

[0048] in, The ergodic capacity of the proposed system can be represented as C = E{log2(1+γ)}, and f(x) = log2(1+x) is an upward convex function. According to Jason's inequality, the ergodic capacity expression can be obtained: C = E[R]≤log2(1+E[γ]).

[0049] The average total power consumption of the system can be expressed as:

[0050]

[0051] Among them, P pasP represents the average power consumption of the switch and control circuit at the passive part reflective element; act Represents the average power consumption of active components, P DC Represents the DC bias power consumption of each active component, ξ is also the inverse of the energy conversion coefficient; P BS represents the dissipated power consumed at the base station, is the inverse of the energy conversion coefficient. Based on the above, substitute Then the complete expression of energy efficiency can be obtained.

[0052]

[0053] 4. Hybrid RIS Component Configuration Optimization

[0054] The goal of the optimization is to find the optimal ratio of active RIS components to passive RIS components under the condition that the total number of RIS components is constant, so as to maximize the energy efficiency of the system. To simplify the calculation, it is assumed that the amplification factor α of all active components is n All are fixed values α, and N pas =NN act Substituting in, the mathematical expression of the optimization problem can be obtained as follows:

[0055]

[0056] in,

[0057] By combining the quadratic transform and the Dinkelbach transform, the solution of the original optimization problem can be simplified:

[0058]

[0059] in Will Using the first-order Taylor expansion in N0 and substituting it into:

[0060]

[0061] It can be seen that the original optimization problem meets the concavity condition and has the existence and solvability of the optimal solution.

[0062] During the optimization process, y and z are iterative variables and are updated using the following formula:

[0063]

[0064] Where t is the iteration index. As y and z are updated, the derivative of the reformulated formula is derived to obtain an extreme point N in the feasible region. actAnd update until the algorithm converges, and then the component ratio corresponding to the maximum energy efficiency can be obtained.

[0065] The following computer simulation is used to analyze the ergodic capacity analysis and component configuration optimization method of the hybrid RIS-assisted communication system under hardware damage and verify its correctness. Consider a two-dimensional Cartesian coordinate system with meters as the unit, where the base station is located at (0, 0) m, the hybrid RIS is located at (150, 5) m, and the user is located at (300, 0) m. The transmission power level is set to P DC =10dBm, P c =1.5dBm, P BS =30dBm; receiver noise variance In the channel parameter setting, the reference path loss at the reference distance d0 = 1m is set to -30dB, the path loss factor β = 2.2, and the parameters of the Rayleigh channel between the base station-hybrid RIS-user are Hardware damage parameters The active RIS reflection coefficient is α n =α=6dB, the inverse of the energy conversion coefficient ξ is set to 1.1.

[0066] like Figure 3 As shown in Figure 2, we systematically studied the dynamic impact of transceiver hardware impairments on energy efficiency in hybrid RIS-assisted wireless communication systems through theoretical modeling and Monte Carlo simulation. The established models with and without hardware impairments are highly consistent with the simulation results, verifying the correctness of the theoretical analysis. In addition, the experimental results also show that energy efficiency does not change with the change of P B On the contrary, due to the influence of hardware damage at the transmitting and receiving ends, the energy efficiency will tend to be stable or even decrease.

[0067] Figure 4 Shows the fixed transmission power P B The EE performance of different active reflective element ratios is compared under the condition of ΔV = 20dBm. The results show that increasing the proportion of active elements does not lead to a monotonic improvement in energy efficiency; on the contrary, energy efficiency may decrease due to the amplification of thermal noise introduced by the additional active elements. Notably, the proposed algorithm consistently achieves the highest energy efficiency by determining the optimal ratio. Compared to baseline schemes such as passive RIS and other hybrid RIS with different active ratios, our approach demonstrates superior performance, validating its effectiveness in optimizing energy efficiency.

[0068] In summary, the method proposed in the present invention can effectively analyze the ergodic capacity of the hybrid RIS-assisted communication system under hardware damage and effectively improve the energy efficiency performance. At the same time, the steps for implementing the method are relatively simple, which fully demonstrates the effectiveness of the ergodic capacity analysis and component configuration optimization method of the hybrid RIS-assisted communication system under hardware damage proposed in the present invention.

[0069] The above description is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements can be made without departing from the principles of the present invention. These improvements should also be regarded as the scope of protection of the present invention.

Claims

1. A method for ergodic capacity analysis and component configuration optimization of a hybrid RIS-assisted communication system under hardware impairment, characterized by: S1. Considering the far-field hybrid active-passive reconfigurable intelligent surface (Hybrid Active-Passive Reconfigurable Intelligent Surface) assisted wireless communication system, the model includes a single-antenna base station, a hybrid active-passive RIS, and a single-antenna user. The hybrid RIS consists of N act Active reflective elements, N pas passive reflective elements. S2. All channels are modeled as Rayleigh channels, and there is no direct link between the base station and the user. The channel between the base station and the active or passive components is defined as g BR,i , i∈{act,pas}, the channel between active or passive components and users is defined as h RU,i ,satisfy L BR 、L RU is the path loss, They are all small-scale Rayleigh fading channels. are the parameters of the Rayleigh channel between the base station and the hybrid RIS, and between the hybrid RIS and the user, i.e., the standard deviation of Rayleigh fading. Ψ and Θ represent the reflection coefficient matrices of active and passive components, respectively. To simplify the formula, let The user-side ergodic capacity expression can be obtained from Jason's inequality: in, P B is the transmission power, φ t ,φ r are transmitter hardware damage and receiver hardware damage respectively, and κ represents the transceiver hardware damage level; z represents the thermal noise introduced by active components, satisfying n s Represents the additive Gaussian white noise at the user, satisfying S3. Based on the derived user ergodic capacity and the average total power consumption of the system, the expression for energy efficiency can be obtained: S4. Establish a hybrid RIS component configuration optimization problem, whose optimization goal is to maximize energy efficiency and the optimization variable is N. act , the optimization constraint is N act ∈[0, N], and N act ∈Z; S5: Use the quadratic iteration method to introduce the iteration variable y, and Dinkelbach to introduce the iteration variable z. The two optimization methods are combined to simplify the problem in S3, so that the original formula can find an optimal solution. S6. As y and z are updated, the optimized formula is derived to obtain an extreme point in the feasible region and update N act , until the algorithm converges.

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