基于压缩感知的mMTC系统信道估计

Compressive Sensing Based Chanel Estimation for mMTC System

为解决海量机器类通信(massive machine-type communications,mMTC)上行传输中活跃用户设备检测和信道估计问题,提出一种基于分布式多矢量测量的子空间追踪(distributed multiple measurement vector subspace pursuit,DMMV-SP)算法.采用免授权海量随机接入的方案,以降低海量机器类通信的时延和信号开销.考虑多载波传输方案并结合子空间追踪算法,利用接收天线和子载波存在的结构稀疏性,检测活跃用户设备的同时进行信道估计.通过计算检测错误概率以及均方误差对活跃用户设备检测和信道估计性能进行评估.仿真结果显示,提出的DMMV-SP算法相较于传统正交匹配追踪(orthogonal matching pursuit,OMP)算法取得更理想的结果.

In order to solve the problem of active user equipment detection and channel estimation in massive machine-type communications(mMTC)uplink transmission,a distributed multiple measurement vector subspace pursuit(DMMV-SP)algorithm was proposed in this paper. Firstly,a grant-free massive random access scheme was adopted to reduce the latency and signal overhead of mMTC. Secondly,considering the multi-carrier transmission scheme and combining the subspace pursuit algorithm,the structural sparsity of the receiving antennas and sub-carriers were utilized for the active user equipment detection and channel estimation simultaneously. Finally,the active user equipment detection and channel estimation performance were evaluated by calculating the error probability and the mean square error. The simulation results show that the proposed DMMV-SP algorithm can achieve better results than the traditional orthogonal matching pursuit(OMP)algorithm.