On the Performance of Efficient Channel Estimation Strategies for Hybrid Millimeter Wave MIMO System

Abstract

Millimeter wave (mmWave) relying upon the multiple output multiple input (MIMO) is a new potential candidate for fulfilling the huge emerging bandwidth requirements. Due to the short wavelength and the complicated hardware architecture of mmWave MIMO systems, the conventional estimation strategies based on the individual exploitation of sparsity or low rank properties are no longer efficient and hence more modern and advance estimation strategies are required to recapture the targeted channel matrix. Therefore, in this paper, we proposed a novel channel estimation strategy based on the symmetrical version of alternating direction methods of multipliers (S-ADMM), which exploits the sparsity and low rank property of channel altogether in a symmetrical manner. In S-ADMM, at each iteration, the Lagrange multipliers are updated twice which results symmetrical handling of all of the available variables in optimization problem. To validate the proposed algorithm, numerous computer simulations have been carried out which straightforwardly depicts that the S-ADMM performed well in terms of convergence as compared to other benchmark algorithms and also able to provide global optimal solutions for the strictly convex mmWave joint channel estimation optimization problem.

A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems

“…Since D is rank deficient, we could not perform a transform D † on ỹk to recover data, as we could in (9). Thus, we resort to precoding.…”

“…Furthermore, analog-to-digital converters (ADCs) at high sampling rates generate a large amount of data that needs to be processed in real time. The expensive ADCs are perceived as a heavy load for the future communication network that will use multiple antennas [8,9].…”