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.
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