A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order, power allocation coefficient vector and number of clusters, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the throughput gain of 35% can be achieved by invoking NOMA technique instead of orthogonal multiple access (OMA).
翻译:提出了智能反映表面(IRS)辅助多投入单输出(MISO)非垂直多存(NOMA)网络的新框架,其中提议了一个基础站(BS)服务于每个组群中未固定用户数的多组群,目标是通过在IRS联合优化被动波束矢量、解码顺序、动力分配系数矢量和组群数量,在符合用户比率要求的情况下,最大限度地实现所有用户的总和率。为了解决所提出的问题,建议了三步方法。特别是,首先采用了基于长期短期内存(LSTM)的算法,以预测用户的流动性。第二,为用户群群提出了基于K平均值的高斯混合物模型(K-GMM)算法。第三,根据深度Q网络算法共同确定阶段转移矩阵和权力分配政策。提供了模拟结果,以证明拟议的算法超越基准,而通过量增加35%的负载法则可以通过援引NOMA的多存取技术来实现。