Efficient Beamformer for Low Mobility Indoor Communication Using Sparse Adaptive Algorithm
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Efficient Beamformer for Low Mobility Indoor Communication Using Sparse Adaptive Algorithm Basabadatta Mohanty1 · Harish Kumar Sahoo2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The proposed beamforming model exploits the underlying sparseness of the adaptive filter as impulse response of wireless channel shows some extent of sparse behavior in practice. A new formulation of cost function of fourth order instead of quadratic will help to achieve stable and faster convergence, but the computational complexity is high. Thus the filter design requires a compromise between the quadratic and fourth order cost function to achieve good estimation accuracy. Normalized least mean square fourth (NLMS/F) filter design is based on the compromise to achieve a better performance with a faster convergence. Inclusion of sparsity in the cost function of NLMS/F filter further reduces the computational complexity as less number of nonzero coefficients involve in estimation with a bounded error. IEEE 802.11 and Saleh–Valenzuela models with exponential power delay profile (PDP) are used to implement proposed beamformer for indoor application. The proposed sparse-NLMS/F beamformer is compared with its NLMS/F counterpart, and tested with practical fading condition. Different performance measures like mean square error (MSE), estimated weight convergence, beam pattern are used to test the proposed model under practical channel condition. Keywords IEEE 802.11 model · Saleh–Valenzuela (S–V) model · NLMS · LMF · NLMS/F · Beamforming · l0 −norm
1 Introduction Dispersive nature of communication channel in a wireless medium introduces fading and inter symbol interference (ISI) during transmission of digital symbols. The overall transmission may also be corrupted by additive white Gaussian noise. Proper angle of arrival (AOA) of received signal is highly required to maintain desired level of signal to noise ratio (SNR). Adaptive beam forming can be efficiently used for AOA estimation than MMSE beam former which uses non-recursive optimization [1]. The system capacity has been increased * Harish Kumar Sahoo [email protected] 1
Department of Electronics and Communication Engineering, International Institute of Information Technology, Bhubaneswar, India
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Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, India
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substantially by using an adaptive beamformer with spatial processing techniques to increase the data rate [2]. The beamforming approach also supports multiple users using space division multiple access technique and an increase in system capacity can be obtained which is proportional to the number of antenna. [3]. Adaptive algorithms utilize the training or reference signal for the adjustment of magnitude and phases of each individual weight [4]. The beam former can also be controlled by using by using normal reference or self-referencing [5] which is sensitive to SNR a
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