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KR-102965332-B1 - ACHIEVABLE RATE ANALYSIS OF TWO-HOP INTERFERENCE CHANNEL WITH COORDINATED IRS RELAY

KR102965332B1KR 102965332 B1KR102965332 B1KR 102965332B1KR-102965332-B1

Abstract

The present invention relates to an analysis technique that can improve energy efficiency by maximizing the sum of achievable rates of destinations in an organized IRS relay-based 2-hop interference channel. The technical problem to be solved by the present invention is to provide a technique for analyzing an IRS-based 2-hop interference channel to improve the disadvantages of existing communication systems utilizing IRS, thereby enabling the IRS communication system to have high energy efficiency and a sum of transmission rates. According to the apparatus, method, and computer program of the present invention, the achievable transmission rate region ( R ) of the achievable transmission rates ( R1 (φ), ..., RK ( φ) ) of the destinations ( D1 , ..., DK ) of the 2-hop interference channel can be characterized to obtain a sum -rate, and based on this, iterative updates are performed until the phase difference vector ( φ ) and auxiliary variables ( α, β ) converge to calculate the optimal sum-rate, which is the maximum value of the sum-rate, so that the optimal sum-rate can be applied to the IRS modules of the 2-hop interference channel to optimize the IRS communication system in terms of sum-rate and energy efficiency.

Inventors

  • 조성래
  • 응우옌 더 뷔
  • 이동현
  • 전용인

Assignees

  • 중앙대학교 산학협력단

Dates

Publication Date
20260513
Application Date
20221026

Claims (14)

  1. In an achievable rate analysis device for an organized IRS (Intelligent Reflecting Surface) relay-based 2-hop interference channel, Memory configured to store instructions; and By executing the above commands: Achievable transmission rate region ( R ) is defined based on the achievable transmission rates (R1 ( φ), ..., RK ( φ) ) of the destinations ( D1 , ..., DK ) of the above 2-hop interference channel, and By performing characterization on the above achievable transmission rate region ( R ) , a summated transmission rate representing the sum of the above achievable transmission rates ( R1 (φ), ..., RK ( φ) ), and To maximize the above summed transmission rate, auxiliary variables ( α, β ) are generated by applying a Lagrangian double-quadratic transformation, and By repeating the process of updating the optimal values ( α * , β * ) of the auxiliary variables ( α, β ) while fixing the phase difference vector ( φ ) and the process of updating the optimal value ( φ * ) of the phase difference vector ( φ ) while fixing the auxiliary variables ( α, β ), convergence values of the auxiliary variables ( α, β ) and the phase difference vector ( φ ) are obtained, and An achievable transmission rate analysis device comprising: a processor configured to calculate an optimal summed transmission rate, which is the maximum value of the summed transmission rate, by applying the convergence value of the above phase difference vector ( φ ) to the IRS modules of the above 2-hop interference channel.
  2. In paragraph 1, The above 2-hop interference channel comprises K sources ( S1 , ..., SK ) , K destinations ( D1 , ..., DK ) each paired with the K sources , and L IRS modules (IRS1 , ..., IRSL ) . Each of the above K sources ( S1 , ..., SK ) and each of the above K destinations ( D1 , ..., DK ) has a single antenna, and the above K sources ( S1 , ..., SK ) and the above K destinations ( D1 , ..., DK ) do not have a direct communication link, and An achievable transmission rate analysis device, wherein each of the above L IRS modules ( IRS 1 , ..., IRS L ) has M reflection elements ( IRS i,1 , ..., IRS i,M ; ∀i∈L ).
  3. In paragraph 2, When the processor calculates the optimal summation transmission rate, An achievable transmission rate analysis device configured to achieve the maximum value of the sum of the achievable transmission rates ( R1 ( φ ) , ..., RK( φ )) by applying the convergence value ( φ * i,m ; ∀i∈L, ∀m∈M ) of the above phase difference vector (φ) to each of the M reflection elements ( IRS i ,1 , ..., IRS i ,M ; ∀i∈L ) of the above L IRS modules ( IRS 1 , ..., IRS L) .
  4. In paragraph 1, When the above processor calculates the summed transmission rate, An achievable transmission rate analysis device configured to perform characterization of the achievable transmission rate region ( R ) using a Successive Convex Approximation (SCA) algorithm.
  5. In paragraph 4, When the above processor calculates the summed transmission rate, A relaxation constraint is generated by relaxing the unit-modulus constraint regarding the above achievable transmission rate region ( R ), and Under the above relaxation constraint, the convex approximation sequence is iteratively solved based on the transmission rate profile vector ( μ ) until the convergence condition is satisfied to obtain the transmission rate tuple of the Pareto boundary, which is the outer boundary of the achievable transmission rate region ( R ), and An achievable transmission rate analysis device configured to recover final values through projection from result values derived as approximations due to the relaxation of the above unit modulus constraint.
  6. In paragraph 1, The processor, when obtaining the convergence values of the auxiliary variables ( α, β ) and the phase difference vector ( φ ): Based on the AO (Alternating Optimization) algorithm, the optimal values ( α * , β * ) of the auxiliary variables ( α, β ) are updated while keeping the phase difference vector ( φ ) fixed, and An achievable transmission rate analysis device configured to update the optimal value ( φ * ) of the phase difference vector ( φ ) while fixing the auxiliary variables ( α, β ) based on the ADMM (Alternating Direction Method of Multipliers) algorithm.
  7. In paragraph 1, An achievable transmission rate analysis device, wherein the achievable transmission rate ( R k (φ) ) of the k-th destination is calculated according to the following equation: R k (φ) = log 2 (1+SINR k ) , and said SINR k represents the Signal-to-Interference-plus-Noise Ratio (SINR) of said k-th destination.
  8. A method for analyzing the achievable transmission rate of an organized IRS relay-based 2-hop interference channel, performed by a processor executing instructions stored in memory, A step of defining an achievable transmission rate region (R) based on achievable transmission rates (R1 ( φ ) , ..., RK ( φ) ) having destinations ( D1 , ..., DK ) of the above 2-hop interference channel; A step of obtaining a summated transmission rate representing the sum of the achievable transmission rates ( R1 (φ), ..., RK ( φ) ) by performing characterization on the achievable transmission rate region ( R ) above; A step of generating auxiliary variables ( α, β ) by applying a Lagrangian double-quadratic transformation to maximize the above summed transmission rate; A step of obtaining convergence values of the auxiliary variables ( α, β ) and the phase difference vector ( φ ) by repeating the process of updating the optimal values ( α * , β * ) of the auxiliary variables ( α, β ) while fixing the phase difference vector ( φ ) and the process of updating the optimal value ( φ * ) of the phase difference vector ( φ ) while fixing the auxiliary variables ( α, β ); and A method for analyzing an achievable transmission rate, comprising the step of applying the convergence value of the phase difference vector ( φ ) to the IRS modules of the 2-hop interference channel to calculate the optimal summed transmission rate, which is the maximum value of the summed transmission rate.
  9. In paragraph 8, The above 2-hop interference channel comprises K sources ( S1 , ..., SK ) , K destinations ( D1 , ..., DK ) each paired with the K sources , and L IRS modules (IRS1 , ..., IRSL ) . Each of the above K sources ( S1 , ..., SK ) and each of the above K destinations ( D1 , ..., DK ) has a single antenna, and the above K sources ( S1 , ..., SK ) and the above K destinations ( D1 , ..., DK ) do not have a direct communication link, and A method for analyzing achievable transmission rates, wherein each of the above L IRS modules ( IRS 1 , ..., IRS L ) has M reflection elements ( IRS i,1 , ..., IRS i,M ; ∀i∈L ).
  10. In Paragraph 9, The step of calculating the optimal sum transmission rate above is, A method for analyzing achievable transmission rates, comprising the step of applying the convergence value ( φ * i,m ; ∀i∈L, ∀m∈M ) of the phase difference vector ( φ ) to each of the M reflection elements ( IRS i,1 , ..., IRS i,M ; ∀i∈L ) of the L IRS modules ( IRS 1 , ..., IRS L ) to achieve the maximum value of the sum of the achievable transmission rates ( R 1 (φ), ..., R K (φ) ).
  11. In paragraph 8, The step of calculating the sum transmission rate above is, A method for analyzing an achievable transmission rate, comprising the step of performing characterization of the achievable transmission rate region ( R ) using a Successive Convex Approximation (SCA) algorithm.
  12. In Paragraph 11, The step of calculating the sum transmission rate above is, A step of generating a relaxation constraint by relaxing the unit-modulus constraint regarding the above achievable transmission rate region ( R ); A step of iteratively solving a convex approximation sequence based on a transmission rate profile vector ( μ ) until a convergence condition is satisfied under the above relaxation constraint to obtain transmission rate tuples of Pareto boundaries that are the outer boundaries of the achievable transmission rate region ( R ); and A method for analyzing achievable transmission rates, comprising the step of recovering result values derived as approximations due to the relaxation of the unit modulus constraint into final values through projection.
  13. In paragraph 8, The step of obtaining the convergence values of the above auxiliary variables ( α, β ) and the above phase difference vector ( φ ) is, A step of updating the optimal values ( α * , β * ) of the auxiliary variables ( α, β ) while fixing the phase difference vector ( φ ) based on the AO (Alternating Optimization) algorithm; and A method for analyzing an achievable transmission rate, comprising the step of updating the optimal value ( φ * ) of the phase difference vector ( φ ) while fixing the auxiliary variables ( α, β ) based on the ADMM (Alternating Direction Method of Multipliers) algorithm.
  14. In paragraph 8, A method for analyzing an achievable transmission rate, wherein the achievable transmission rate ( R k (φ) ) of the k-th destination is calculated according to the following equation: R k (φ) = log 2 (1+SINR k ) , and the SINR k represents the Signal-to-Interference-plus-Noise Ratio (SINR) of the k-th destination.

Description

Accomplishable Rate Analysis of Two-Hop Interference Channel with Coordiminated IRS Relay The present invention relates to an analysis technique that can improve energy efficiency by maximizing the sum of achievable rates of destinations in an organized IRS relay-based 2-hop interference channel. Existing cellular networks are under heavy load due to unprecedented demands for high-quality and ubiquitous wireless services. In particular, the goal of 6th generation (6G) wireless communication systems is to provide a network that encompasses applications such as data-driven, instantaneous, ultra-large, ubiquitous wireless connectivity and connected intelligence. To achieve these goals, Intelligent Reflecting Surfaces (IRS) have emerged as a new solution, and various studies on them are currently underway. IRS possesses the following advantages. First, IRS can be easily installed on building exteriors, interior walls, elevated spaces, roadside billboards, highway polling stations, vehicles, and more. Second, to enhance spectrum efficiency, it is possible to improve the Signal-to-Noise Ratio (SINR) by forming a Line of Sight (LoS) link between the Base Station (BS) and the mobile user. Third, composed primarily of passive devices without an active transmitting RF chain, it can be densely deployed in wireless networks at low cost and with low energy consumption. Finally, it is environmentally friendly and offers higher energy efficiency than existing amplify-and-forward (AF) and decode-and-forward (DF) systems. In particular, it can be operated in full-duplex (FD) and full-band transmission without additional power for signal amplification/regeneration and sophisticated processing for self-interference cancellation. As such, IRS communication systems are gaining prominence as a general communication approach in future networks due to the rapid increase in the number of users. However, conventional technology is based on single-hop IRS communication where a direct link exists between the source and destination, whereas IRS communication networks with multiple link pairs communicating through multiple distributed and cooperative IRS repeaters have not yet been sufficiently studied. When a direct link exists, there is a problem where the direct communication link can be severed by thick walls indoors or by trees and large buildings outdoors. Furthermore, because the focus has been on wireless networks with a single IRS, the use of IRS has only considered the improvement of Line of Service (LoS) for a single user, making it difficult to apply to real-world situations involving multiple users. Additionally, it is assumed that the entire Channel State Information (CSI) is known and utilized; however, in reality, incomplete CSI always occurs, and the impact of such incomplete CSI on system performance is not considered. Moreover, IRS channel estimation and optimization generally require high complexity and computational load, which poses a problem as it makes practical use difficult in 6G, where high speeds and fast response times are essential. FIG. 1 is a diagram illustrating an organized IRS relay-based 2-hop interference channel according to the present invention. FIG. 2 is a diagram illustrating the elements constituting an achievable transmission rate analysis device for an organized IRS relay-based 2-hop interference channel according to the present invention. FIG. 3 is a diagram illustrating a Successive Convex Approximation (SCA) algorithm used to characterize the achievable transmission rate region ( R ) according to the present invention. FIG. 4 is a diagram illustrating the Alternating Optimization (AO) algorithm and the Alternating Direction Method of Multipliers (ADMM) algorithm used to obtain convergence values of auxiliary variables ( α, β ) and phase difference vector ( φ ) according to the present invention. FIG. 5 is a diagram illustrating the achievable transmission rate region ( R ) defined in an organized IRS relay-based 2-hop interference channel according to the present invention. Figure 6 is a diagram illustrating the results of performance improvement of the maximum sum transmission rate compared with existing methods for various settings of the 2-hop interference channel according to the present invention. FIG. 7 is a diagram illustrating the results of performance improvement in energy efficiency compared with existing methods for various settings of the 2-hop interference channel according to the present invention. FIG. 8 is a diagram illustrating that the achievable transmission rate analysis technique according to the present invention can be utilized even under incomplete Channel State Information (CSI). FIG. 9 is a diagram illustrating the steps constituting a method for analyzing the achievable transmission rate of an organized IRS relay-based 2-hop interference channel according to the present invention. The present invention is susceptible to various modifications and may have various