传统认知MIMO环境下的多天线频谱感知算法设计通常假定天线数目很少而快拍数目很大，而在认知Massive MIMO环境下天线数目的增大使得该假定通常不再成立。本项目考虑Massive MIMO环境下多天线频谱感知算法设计问题，内容包括：（1）经典的多天线频谱感知算法在Massive MIMO场景中的适用性研究；（2）中等天线数目配置时Massive MIMO场景中基于GLRT技术的频谱感知算法设计；（3）大天线数目配置时Massive MIMO场景中基于GLRT技术的频谱感知算法设计；（4）Massive MIMO场景中基于信号相关特征的多天线频谱感知算法设计；（5）Massive MIMO场景中具有低计算复杂度的频谱感知算法设计。研究成果将为Massive MIMO环境下多天线频谱感知算法设计提供参考依据和理论支持，将极大丰富多天线频谱感知算法设计的理论与方法，具有重要的理论和实际意义。

The design of the traditional multi-antenna spectrum sensing algorithms in cognitive MIMO radios is usually based on the assumption that the number of antennas is very small while the number of snapshots is very large. However, this assumption is usually not applicable in the cognitive Massive MIMO environment. This project considers the design of the multi-antenna spectrum sensing algorithms in cognitive Massive MIMO radios. The specific contents include: (1) investigate the applicability of the classical multi-antenna spectrum sensing algorithms in Massive MIMO scenarios; (2) design spectrum sensing algorithms based on the GLRT technique for the applications with medium number of antennas in Massive MIMO scenarios; (3) design spectrum sensing algorithms using GLRT technique for the applications with large number of antennas in the Massive MIMO scenarios; (4) design spectrum sensing algorithms based on the signal correlation characteristics in Massive MIMO scenarios; (5) design spectrum sensing algorithms with low computational complexity for the Massive MIMO applications. Research results will provide the reference basis and theoretical support for the design of multi-antenna spectrum sensing algorithms under the Massive MIMO environment, which will greatly enrich the theory and method of the design of multi-antenna spectrum sensing algorithms and have great theoretical and practical significance.