Signal Detection Algorithm of RIS-Assisted SIMO Communication System with Index Modulation
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摘要: 面对未来无线移动通信对通信质量和频谱效率的更高要求，该文融合索引调制(IM)与可重构智能表面(RIS)技术，建立基于IM的RIS辅助单输入多输出(SIMO)通信系统架构，并提出一种基于变分贝叶斯推断(VBI)的信号检测算法。首先，在该系统中，RIS单元被划分为若干子块，利用RIS子块的激活状态传递附加信息；接着，利用VBI给出激活RIS子块对应的相移矢量与待检测信号的近似后验分布；最后，利用RIS相移矢量近似后验分布的对数零梯度值结合正交匹配追踪算法(OMP)恢复出索引信息比特，进而利用待检测信号对数零梯度值，恢复出发送信号。同时，从理论上推导了基于IM的RIS辅助SIMO系统平均速率。仿真结果表明，与传统RIS辅助的SIMO通信系统相比，基于IM的RIS辅助SIMO系统具有更高的系统平均速率；并且与现有算法相比，该文算法具有更低的误比特率。Abstract: For the higher requirements of communication quality and spectrum efficiency in future wireless mobile communication systems, this paper combines Index Modulation (IM) and Reconfigurable Intelligent Surface (RIS) technologies to establish the RIS-assisted Single-Input Multiple-Output (SIMO) communication systems with IM, and the Variational Bayes Inference (VBI)-based signal detection algorithm is proposed. Firstly, in the system, the RIS units are divided into a number of sub-blocks, and the activation state of the RIS sub-block is used to transmit additional information. Then, the VBI is used to calculate the approximate posterior distribution of the phase shift vector corresponding to the activated RIS sub-block and the signal vector for detection. Finally, the index information bits are recovered by using the logarithmic zero gradient value of the approximate posterior distribution of the RIS phase shift vector combined with the Orthogonal Matching Pursuit (OMP) algorithm, and taking advantage of the logarithmic zero gradient value of the signal to be detected, the transmitted symbols are recovered. Meanwhile, the system average rate is theoretically deduced. The simulation results show that compared with the traditional RIS-assisted SIMO communication systems, the RIS -assisted SIMO systems with IM has a higher system average rate; and in contrast to existing algorithms, the proposed one has lower bit errors rate.
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表 1 索引比特对应两种分组原则下RIS相移矩阵
索引比特 组索引$ k $ 与分组方案1对应的${{\boldsymbol{\theta}} _k}$ 与分组方案2对应的${{\boldsymbol{\theta}} _k}$ $ \left[ {\begin{array}{*{20}{c}} 0&0 \end{array}} \right] $ 1 $ {\left[ {\begin{array}{*{20}{c}} {{\theta _1}}&{{\theta _2}}&0&0&0&0&0&0 \end{array}} \right]^{\text{T}}} $ $ {\left[ {\begin{array}{*{20}{c}} 0&{{\theta _2}}&0&0&0&0&0&{{\theta _8}} \end{array}} \right]^{\text{T}}} $ $ \left[ {\begin{array}{*{20}{c}} 0&1 \end{array}} \right] $ 2 $ {\left[ {\begin{array}{*{20}{c}} 0&0&{{\theta _3}}&{{\theta _4}}&0&0&0&0 \end{array}} \right]^{\text{T}}} $ $ {\left[ {\begin{array}{*{20}{c}} {{\theta _1}}&0&0&{{\theta _4}}&0&0&0&0 \end{array}} \right]^{\text{T}}} $ $ \left[ {\begin{array}{*{20}{c}} 1&0 \end{array}} \right] $ 3 $ {\left[ {\begin{array}{*{20}{c}} 0&0&0&0&{{\theta _5}}&{{\theta _6}}&0&0 \end{array}} \right]^{\text{T}}} $ $ {\left[ {\begin{array}{*{20}{c}} 0&0&{{\theta _3}}&0&0&0&{{\theta _7}}&0 \end{array}} \right]^{\text{T}}} $ $ \left[ {\begin{array}{*{20}{c}} 1&1 \end{array}} \right] $ 4 $ {\left[ {\begin{array}{*{20}{c}} 0&0&0&0&0&0&{{\theta _7}}&{{\theta _8}} \end{array}} \right]^{\text{T}}} $ $ {\left[ {\begin{array}{*{20}{c}} 0&0&0&0&{{\theta _5}}&{{\theta _6}}&0&0 \end{array}} \right]^{\text{T}}} $ 
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