无蜂窝大规模MIMO网络下基于联邦学习的用户接入策略及能耗优化

Federated learning-based user access strategy and energy consumption optimization in cell-free massive MIMO network

针对无蜂窝大规模多输入多输出(CF-mMIMO)网络中用户如何选择接入点的问题,提出了一种基于信道排序的较差用户优先接入策略。首先,用户进行信道感知后对其信道质量和稳定性进行评估和排序,用户按照信道状态信息的顺序依次选择合适的接入点;其次,考虑到用户的能耗与数据安全等问题,采用联邦学习框架以增强用户的数据隐私安全,并提出一种基于能耗优化的交替优化变量算法,对多维变量进行优化,使系统总能耗最小化。仿真结果表明,相较于传统的大规模MIMO中用户为中心的接入策略,所提接入策略可使用户平均上行可达速率提升20%,信道较差用户的上行速率可提升2倍;在能耗优化方面,优化后的总能耗可降低50%以上。

To solve the problem that how users choose access points in cell-free massive multiple-input multiple-output(CF-mMIMO)network,a prioritized access strategy for poorer users based on channel coefficient ranking was proposed.First,users were evaluated and ranked for their channel quality and stability after channel sensing,and suitable access points were selected in sequence according to the order of the channel state information.Second,considering issues such as users'energy consumption and data security,a federal learning framework was used to enhance user's data privacy and security.Meanwhile,an alternating optimization variables algorithm based on energy consumption optimization was proposed to optimize the multi-dimensional variables,for the purpose of minimizing the total energy consumption of the system.Simulation results show that compared with the traditional user-centric in massive MIMO,the proposed access strategy can improve the average uplink reachable rate of users by 20%,and the uplink rate of users with poor channels can be double improved;in terms of energy consumption optimization,the total energy consumption can be reduced by much more than 50%after optimization.