Secure Energy Efficiency in Communication Systems Based on Deep Learning
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摘要: 该文研究了基于可重构智能超表面(RIS)赋能的无线通信系统的安全传输问题。在研究的模型中，一个用户通过智能超表面连接到接入点，而窃听者窃听了从用户发送到基站的信号。因此，有必要设计一个合适的智能超表面反射向量来解决窃听问题。这个问题被表述为一个优化问题，其目标是通过共同优化智能超表面反射向量和智能超表面元件的数量来最大化安全能效，该安全能效定义为安全速率与系统总能耗的比值。这是一个非凸优化问题，传统方法是难以解决的。为了解决这个问题，提出了一种利用新兴的深度学习(DL)技术的新算法，以找到近似最优的智能超表面反射向量并确定近似最优的genie-aided反射元素数量。仿真结果表明，该方法达到了近似最优算法的最佳安全能效的96%。Abstract: The secure transmission for Reconfigurable Intelligent Surface (RIS) assisted wireless communication systems is investigated in this paper. In the studied model, one user connects to the access point via a RIS while an eavesdropper eavesdrops on the signal sent from the user to the access point. Therefore, it is necessary to design an appropriate RIS reflection vector to solve the eavesdropping problem. This problem is formulated as an optimization problem whose goal is to maximize the secure energy efficiency which is defined via jointly optimizing the RIS reflection reflector as well as the number of RIS elements, which results in a non-convex optimization problem that is intractable to solve by the traditional methods. To tackle this issue, a new algorithm by leveraging the advance of the emerging Deep Learning (DL) technique is proposed so as to find the near optimal RIS reflection vector and determine the near optimal number of RIS reflection elements. Simulation results show that the proposed method reaches 96% of the near optimal secure energy efficiency of the genie-aided algorithm.
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表 1 基于深度神经网络的DeepRIS(算法1)
阶段(1)：训练阶段 for $s = 1$ to $S$ do //第$s$个信道相干块，$s = 1,2, \cdots ,S$ RIS系统获得信道状态描述符${\mathbf{h}}(s)$ for $n = 1$ to $ \left| \mathcal{P} \right| $ do RIS基于$ {{\mathbf{\theta }}_n} $和RIS激活单元个数$ M $计算出对应的安全能量效率
${\eta _n}(s)$end for RIS构造安全能量效率向量$ {\mathbf{\eta }}(s) = \left[ {{\eta _1}(s),{\eta _2}(s), \cdots ,{\eta _{\left| \mathcal{P} \right|}}(s)} \right] $ 将所获得的数据添加到训练数据集$\mathcal{D} \leftarrow \left\langle {{\mathbf{h}}(s),{\mathbf{\eta }}(s)} \right\rangle $ RIS使用最佳反射向量$ {{\mathbf{\theta }}_{{n^*}}} $($ {n^*} = \arg \mathop {\max }\limits_n {\eta _n}(s) $)传输数据 end for 使用训练数据集$\mathcal{D}$训练深度神经网络 阶段(2)：预测阶段 while true do //针对每个信道相干块 RIS系统获得信道状态描述符${\mathbf{h}}$ RIS利用深度神经网络模型预测反射向量
$ {{\mathbf{\theta }}_{{n^{{\text{D}}L}}}} $($ {n^{{\text{DL}}}} = \arg \mathop {\max }\limits_n {\hat \eta _n}(s) $)和激活的反射单元个数
$ {M_{{\text{DL}}}} $($ {M_{{\text{DL}}}} = \arg \mathop {\max }\limits_M \hat \eta _{\max }^M(s) $)RIS使用最佳反射向量$ {{\mathbf{\theta }}_{{n^{{\text{D}}L}}}} $传输数据 end while 
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表 2 系统及深度MIMO数据集参数
Deep MIMO数据集参数 数值 基站编号(作为RIS) 3 作为发射器的用户 从R1000到R1200的网格范围内 作为AP的用户 R850行，第90列 作为Eve的用户 R850行，第100列 发射器(用户)/接收器(AP/Eve)天线数 均为1 BS天线(RIS单元)数 8×8($ M = 64 $)，10×10($ M = 100 $)，
12×12($ M = 144 $)，14×14($ M = 196 $)，
16×16($ M = 256 $)，18×18($ M = 324 $)，
20×20($ M = 400 $)，22×22($ M = 484 $)，
24×24($ M = 576 $)系统带宽 100 MHz OFDM子载波数 256 OFDM使用子载波数 4 OFDM采样系数 1 路径数 1 天线间距 0.5 天线单元增益 3 dBi 发射机天线发射功率 35 dBm 用户设备的噪声系数 5 dB 每个RIS单元的功率 0.005W
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