Wireless channel estimation and beamforming by using block sparse adaptive filtering
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ORIGINAL PAPER
Wireless channel estimation and beamforming by using block sparse adaptive filtering Basabadatta Mohanty1 · Harish Kumar Sahoo2
· Bijayananda Patnaik1
Received: 10 May 2020 / Revised: 28 August 2020 / Accepted: 26 September 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Channel estimation normally provides information about indoor and outdoor fading channel statistics. The adaptive channel estimation models play an important role to generate the required channel state information (CSI) using the estimated channel coefficient vector. The CSI can be utilized to generate an angle vector that controls the steering mechanism of a beamformer. The beamformer provides better directive gain for linear antenna array and helps to improve the signal to noise ratio of the wireless receiver. The proposed estimation model process the transmitted quadrature amplitude modulation (QAM) data samples in the frequency domain. The adaptive design incorporates norm-based sparsity through block recursive least square (BRLS) algorithm to develop a computationally efficient model. The proposed sparse-FBRLS (Fast BRLS) model has simultaneously addressed the problems of channel estimation and beamforming in case of indoor and outdoor communication. The performance of the model is tested by different performance measures under practical mobility conditions. Keywords CSI · RLS · Sparse modeling · Channel capacity · Beamforming · Power delay profile
1 Introduction Different channel estimation models are proposed by researchers to acquire accurate CSI for the indoor and outdoor environments. Because CSI provides the information regarding time varying and statistical behavior of wireless channels. As the wireless channel is frequency selective and time varying for wideband communication, the generation of optimal CSI is difficult [1]. Adaptive techniques are preferred than the non-adaptive methods to estimate CSI due to the adjustment of model coefficients according to time-varying statistics of the wireless channel. Channel estimation can be carried out by the use of expectation maximization (EM) algorithm for a time-varying flat-fading channel to obtain maximum likelihood estimate in an iterative manner [2]. A decision-directed channel estimation (DDCE) technique is analyzed using sample spaced (SS) and fractionally spaced
B
Harish Kumar Sahoo [email protected]
1
Department of Electronics and Communication Engineering, IIIT, Bhubaneswar, India
2
Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, India
(FS) methods for an OFDM wireless communication system [3]. Channel estimation is performed by a improved least mean square (LMS) algorithm which reduces the noise effect during data transmission better than LMS and normalized LMS (NLMS) [4]. A recursive least square (RLS)-based channel estimation for a spatial modulation system is proposed by Yusuf Acar et al. [5], where the iterative receiver offers better performance
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