大规模多天线(L-MIMO)无线通信系统中预编码和低复杂度检测方法

未来移动通信系统面临的最重要问题仍然是有限的频谱资源和持续增长的无线服务需求之间的矛盾。基于空时处理的大规模天线技术在解决与异构无线网络相关的覆盖、吞吐量、频谱效率和能量消耗等关键问题中将发挥重要作用。本课题就是针对蜂窝无线通信系统基站端采用大规模天线阵列时存在的问题，研究大规模多输入多输出(L-MIMO)系统的信道模型与估计；下行链路预编码与波束形成方法；接收端低复杂度信号处理与检测算法和实际约束条件下的性能限与干扰管理等关键技术。同时，针对L-MIMO系统上行链路的特点，研究先进的用户调度机制和信号分离算法；针对大规模分布式天线的干扰问题，研究协作波束成形、基站协作和上/下行接入控制方法。利用随机矩阵理论、机器学习和凸优化等新的数学工具研究大规模天线阵列信号处理关键技术，为提出具有低成本、大容量、高频谱效率和高能量效率的新一代无线通信标准提供理论参考和技术支撑。

In the future mobile communication systems, the conflict between the available spectrum and the sharply increased data rate in wireless service still remains unresolved. Large-scale antennas with simple space-time signal processing will potentially play a more and more important role in solving heterogeneous related covering, throughput, spectrum efficiency and energy efficiency. In reaction to the new problems of employing large-scale antenna array at the base station, the project will focus on the key technologies such as channel modeling and estimation of large-scale multiple input and multiple output (L-MIMO) wireless communication systems, precoding, beamforming, low-complexity detection in L-MIMO, performance limitations and interference coordinate under the practical constraints, etc. Simultaneously, the project will also study the mechanism of advanced user schedule and algorithms of signal separation, cooperative beamforming, base station cooperation and linear interference alignment for L-MIMO in heterogeneous and multi-user environment. New mathematical tools such as random matrix theory, machine learning and convex optimization will be employed to develop key signal processing algorithms, which will be theoretical reference and technical support for the standards of new generation wireless communications with inexpensive, large capacity, as well as higher spectrum and energy efficiency infrastructure.