Mohammad Ali Abedi - Researcher at Imam Hussein University of Officers and Guards Training

The sparse channel estimation which sufficiently exploits the inherent sparsity of wireless channels, is capable of improving the channel estimation performance with less pilot overhead. To reduce the pilot overhead in massive MIMO systems, sparse channel estimation exploring the joint channel sparsity is first proposed, where the channel estimation is modeled as a joint sparse recovery problem. Massive MIMO is a promising technique for future ۵G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatial joint channel estimation scheme to reduce the required pilot overhead, whereby the spatial common sparsity of delay-domain MIMO channels is leveraged. Particularly, a structured orthogonal matching pursuit(SOMP) algorithm at the user is proposed to jointly estimate channels from the limited number of pilots, whereby the spatial common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal least squares estimator.

Massive MIMO, structured compressive sensing(SCS), frequency division duplex (FDD), channel estimation.