论文标题
IRSS中的资源分配有助于Miso-Noma网络：机器学习方法
Resource Allocation in IRSs Aided MISO-NOMA Networks: A Machine Learning Approach
论文作者
论文摘要
提出了一个新型的智能反射表面（IRS）多输入单输出（MISO）非正交多访问（NOMA）网络的框架，其中基站（BS）为每个集群中的多个用户提供了多个群集。目的是通过在IRS上共同优化无源波束成形向量，解码订单和电源分配系数向量，但要遵守用户的利率要求，从而最大化所有用户的总和率。为了解决该法式问题，提出了三步方法。更特别的是，首先采用了基于短期的短期内存（LSTM）算法来预测用户的移动性。其次，为用户聚类提出了基于K-均值的高斯混合模型（K-GMM）算法。第三，调用了基于Q-Network（DQN）的算法，以共同确定相移矩阵和功率分配策略。提供了模拟结果，以证明所提出的算法优于基准，而IRS-NOMA系统的性能优于IRS-soma System。
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 and power allocation coefficient vector, 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 performance of IRS-NOMA system is better than IRS-OMA system.
