Abstract
Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculate fewer dominant paths in mmWave channels. However, conventional CS techniques require a higher training overhead for efficient recovery. In this paper, an efficient extended alternation direction method of multipliers (Ex-ADMM) is proposed for mmWave channel estimation. In the proposed scheme, a joint optimization problem is formulated to exploit low rank and channel sparsity individually in the antenna domain. Moreover, a relaxation factor is introduced which improves the proposed algorithm’s convergence. Simulation experiments illustrate that the proposed algorithm converges at lower Normalized Mean Squared Error (NMSE) with improved spectral efficiency. The proposed algorithm also ameliorates NMSE performance at low, mid and high Signal to Noise (SNR) ranges.
Highlights
In accordance with recent research trends, millimeter-wave communication has been found to be a potential candidate for next-generation Wireless Local Area Network (WLAN) and Considering the hardware architecture complexities of mmWave Multiple Input Multiple Output (MIMO) systems, channel estimation becomes a difficult task
The mmWave channel is estimated by exploiting the sparsity of the channel matrix in the virtual beamspace domain, whereas, in the second approach, the estimation is performed by exploiting the low rank properties of the channel matrix in the antenna domain
The antennas were assumed to be in a uniform linear array (ULA) configuration
Summary
In accordance with recent research trends, millimeter-wave (mmWave) communication has been found to be a potential candidate for next-generation Wireless Local Area Network (WLAN) and Considering the hardware architecture complexities of mmWave MIMO systems, channel estimation becomes a difficult task. Two types of approaches are used for mmWave channel estimation. The mmWave channel is estimated by exploiting the sparsity of the channel matrix in the virtual beamspace domain, whereas, in the second approach, the estimation is performed by exploiting the low rank properties of the channel matrix in the antenna domain. In [11,12,13,14], a compressive sensing (CS)-based channel estimation approach was used for a mmWave. The basic idea behind this approach is based on the technique in which estimators have.
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