论文标题
使用卷积神经网络中的用户选择毫米波大量的MIMO系统
User Selection in Millimeter Wave Massive MIMO System using Convolutional Neural Networks
论文作者
论文摘要
毫米波（MMW）的混合体系结构大量MIMO系统由于功耗低和能源效率高而被认为是可以实现的。但是，由于RF链的数量有限，因此对于此类体系结构来说，使用用户选择。传统的用户选择算法具有很高的计算复杂性，因此在5G和无线移动通信之外可能无法扩展。为了解决这个问题，在这封信中，我们提出了一个低复杂性CNN框架用于用户选择。提出的CNN接受作为输入通道矩阵，并作为输出所选用户的输出。仿真结果表明，所提出的CNN在可实现的速率方面执行接近最佳的详尽搜索，并且计算复杂性可忽略不计。此外，基于CNN的用户选择优于进化算法和贪婪算法，从可实现的速率和计算复杂性方面。最后，仿真结果还表明，所提出的基于CNN的用户选择方案对于通道不完美是可靠的。
A hybrid architecture for millimeter wave (mmW) massive MIMO systems is considered practically implementable due to low power consumption and high energy efficiency. However, due to the limited number of RF chains, user selection becomes necessary for such architecture. Traditional user selection algorithms suffer from high computational complexity and, therefore, may not be scalable in 5G and beyond wireless mobile communications. To address this issue, in this letter we propose a low complexity CNN framework for user selection. The proposed CNN accepts as input the channel matrix and gives as output the selected users. Simulation results show that the proposed CNN performs close to optimal exhaustive search in terms of achievable rate, with negligible computational complexity. In addition, CNN based user selection outperforms the evolutionary algorithm and the greedy algorithm in terms of both achievable rate and computational complexity. Finally, simulation results also show that the proposed CNN based user selection scheme is robust to channel imperfections.
