QoS-oriented Power Allocation Scheme for Multi-user NOMA System Assisted by RIS
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摘要: 可重构智能超表面(RIS)可被视为通信网络中具有特殊功能的“中继”来配合非正交多址接入(NOMA)系统构建一种协同的信息传输方案。考虑到未来物联网(IoT)场景下不同用户设备对服务质量(QoS)的不同需求，该文提出一种RIS辅助的多用户NOMA通信系统模型，并针对两类用户(信息用户和能量用户)的QoS需求设计了一种基于迭代优化的功率分配方法。该方法通过联合设计RIS相移矩阵、基站端波束赋形以及NOMA系统串行干扰消除顺序来最小化系统的总发射功率，以全面减轻通信系统中基站的能耗负担。仿真结果表明，与无RIS的场景相比，RIS辅助的NOMA系统可有效减小基站的能耗；在有RIS的场景下，所提功率分配方法的能耗明显低于RIS端随机选择相位的方式和基站端直接采用迫零波束赋形的方式。Abstract: Reconfigurable Intelligent Surface (RIS) can be regarded as a ‘relay’ with special functions in communication network. It can cooperate with Non-Orthogonal Multiple Access (NOMA) system to construct a coordinated information transmission scheme. Considering the different Quality of Service (QoS) requirements of different user devices in the future Internet of Things (IoT) scenarios, a RIS-assisted multi-user NOMA communication system model is proposed. According to the different QoS requirements of two types of users (information users and energy users), a power allocation method based on iterative optimization is designed. This method minimizes the total transmit power of the system by jointly designing the phase-shift matrix of RIS, the beamforming of base station and the order of successive interference cancellation in NOMA system, so as to reduce comprehensively the energy consumption of base station. Simulation results show that compared with the scenario without RIS, RIS-assisted NOMA system can effectively reduce the energy consumption of base station. In the case with RIS, the energy consumption of the proposed power allocation method is significantly lower than that of random phase selection at RIS and zero-forcing beamforming at base station.
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算法1 基于半正定松弛的迭代优化算法 步骤1 通过解决问题P2确定SIC解调顺序$ \{ s(k)\} $ 步骤2 基站端主动波束赋形、RIS端无源波束赋形优化 (1)初始化${\left\{ { {\boldsymbol{W} } }_{k}\right\} }^{(0)},\;{\left\{ {\theta }_{m}\right\} }^{(0)}$，令$ r = 0,\varepsilon = {10^{ - 3}} $ (2)在给定RIS相移矩阵$ {\{ {\theta _m}\} ^{(r)}} $的前提下，解决问题P3，并获得基站端的有源波束赋形向量$ {\{ {{\boldsymbol{W}}_k}\} ^{(r + 1)}} $ (3)在给定基站波束赋形向量$ {\{ {{\boldsymbol{W}}_k}\} ^{(r + 1)}} $的前提下，解决问题P5，并获得RIS端的无源波束赋形向量$ {\{ {\theta _m}\} ^{(r + 1)}} $ (4)更新r=r+1 (5)问题目标值的下降低于阈值$ \varepsilon $，跳出循环； (6)重复步骤2中的(2)～(5) 返回SIC解调顺序，基站端有源波束赋形向量，RIS端相移矩阵 
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