Channel Estimation for TDD Uplink Massive MIMO Systems via Compressed Sensing

Abstract

Massive Multiple-Input Multiple-Output (Massive MIMO) is expected to be one of the key feature technology for the next cellular communication networks due to its significant benefits in terms of energy efficiency and increased spectral. To fully exploit these advantages, an accurate estimation of the Channel Impulse Response (CIR) between each transmit-receive antenna pair is crucial. In literature, it has been demonstrated that wireless communication tends to have a sparse structured CIR in delay and/or spatial domains. Several channel estimation strategies have exploited the sparsity in the massive MIMO channel based on additional assumptions on shared common support between uplink channels. In this paper, we propose a general approach which highlights the individual sparsity in the different uplink channels without any other additional assumption. To achieve this purpose, a training sequence, also known as pilot sequence and compressed sensing based CIR estimation techniques have been proposed for the massive MIMO sparse uplink channels. In order to underline the performance of proposed techniques, a comparison in terms of Bit Error Rate (BER) and Normalized Mean Square Error (NMSE) is performed with other CS algorithms. The simulation results have shown that proposed algorithm presents a performance gain between 5 dB and 10 dB in terms of NMSE, and a gain of 1dB to 9dB in terms of BER.