Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing

摘要: Abstract Millimeter-wave communication (mmWC) is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue channel propagation characteristics this band, high dimensional antenna arrays need to be deployed at both base station (BS) mobile sets (MS). Unlike conventional MIMO systems, (mmW) lay away employ power predatory equipment such ADC or RF chain each branch system because hardware constraints. Such leverage hybrid precoding (combining) architecture downlink deployment. Because there a large array transceiver, it impossible estimate by methods. This paper develops new algorithm mmW exploiting sparse nature channel. The main contribution representation model exploitation modified approach based on Multiple Measurement Vector (MMV) greedy framework subspace method Signal Classification (MUSIC) which work together recover indices non-zero elements an unknown matrix when rank defected. In practical rank-defective channels, MUSIC fails, we propose extended approaches enhancement compensate limitation MUSIC. Simulation results indicate that our proposed algorithms will have proper performances moderate computational speeds, they are even able channels with sparsity level.