Time Domain Channel Estimation for Time and Frequency Selective Millimeter Wave MIMO Hybrid Architectures: Sparse Bayesi
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Time Domain Channel Estimation for Time and Frequency Selective Millimeter Wave MIMO Hybrid Architectures: Sparse Bayesian Learning‑Based Kalman Filter K. Shoukath Ali1 · P. Sampath1 Accepted: 11 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In this paper, a Sparse Bayesian Learning (SBL) based channel estimation technique for frequency selective millimeter wave (mmWave) channel in the time domain approach is developed. Further, SBL based Kalman filter (SBL-KF) for time and frequency selective mmWave multiple-input multiple-output (MIMO) hybrid architecture is presented. Hybrid precoders and combiners are designed to estimate the channel of mmWave MIMO systems. The hybrid precoding technique provides low power consumption and high achievable rate performance at mmWave frequencies. mmWave channels are sparse in nature, and the sparse recovery problem is estimated using the channel estimation technique. A simulation result of SBL-KF is improved by 4 dB and 10 dB of SNR compared to the conventional SBL and Orthogonal Matching Pursuit based scheme, respectively. The proposed SBL-KF scheme provides low estimation error at smaller training overheads M = 50 compared to the other existing work. Keywords Orthogonal Matching Pursuit · Millimeter wave · Sparse Bayesian Learning · Multiple-input multiple-output
1 Introduction Millimeter-wave (mmWave) wireless technology plays a vital role in the short-range communication systems. mmWave provides high data rates in 5G wireless technology with the frequency band from 30 GHz–300 GHz. In case of practical implementation of mmWave technology faces many challenges and is discussed in [1–4]. To overcome the limitation of realistic scenarios and to improve the system performance. In mmWave MIMO transceivers, Hybrid architecture is used and it requires a reduced number of Radio Frequency (RF) chains when compared to the number of receiving and transmitting antennas [5, 6]. The
* K. Shoukath Ali [email protected] P. Sampath [email protected] 1
Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, India
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mmWave MIMO architecture with large antenna arrays provides Gigabit-per-second data rates [7]. All-digital architecture is expensive and consumes high power when compared to the hybrid mmWave MIMO architecture [8]. In hybrid architecture, before the beamforming process, the Signal to Noise Ratio (SNR) is low [9].
1.1 Prior Work Beam training and channel estimation are two different novel methods used to increase the SNR in spatial processing. Beam training strategies are restricted to be used only for single-stream communication. This strategy fails to improve the high data rates at spatial multiplexing [10–14]. The drawback of beam training methods can be overcome by using channel estimation methods, which allows multiple data stream communication. Recently, channel estimation for frequency flat mmWave MIMO architectures is proposed
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