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Enhanced RLS in Smart Antennas for Long Range Communication Networks
Author(s) -
Peter Nnabugwu Chuku,
Thomas O. Olwal,
Karim Djouani
Publication year - 2018
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2018.04.030
Subject(s) - computer science , mean squared error , recursive least squares filter , maximization , wireless , antenna (radio) , computation , residual , telecommunications , algorithm , mathematical optimization , adaptive filter , mathematics , statistics
The utilisation of smart antenna (SA) techniques in future wireless and cellular networks is expected to have an impact on the efficient use of the spectrum and the optimization of service quality. This is because SAs can enhance the maximization of output power of the signal in desired directions amongst a whole lot of functions. Despite these benefits of SAs, long range communications still face unsolved challenges such as signal fading. Therefore, this paper focuses on enhancing the recursive least squares (RLS) in SA design for long range communication networks. The conventional RLS algorithm does not need any matrix inversion computations because the inverse correlation matrix is determined directly. Therefore, the RLS saves computational power. Hence, we have enhanced the RLS algorithm by introducing a constant m to the gain factor in order to yield an improved gain vector. Results from our simulations show that the enhanced RLS reduces mean square error (MSE), smoothens filter output and improves signal-to-noise ratio (SNR). These benefits further result in the antenna’s gain improvement leading to an increased range and directivity of the smart antenna over a long range communication networks.

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