Analysis of the RLMS adaptive beamforming algorithm implemented with finite precision

Abstract

This paper studies the influence of the use of finite wordlength on the operation of the RLMS adaptive beamforming algorithm. The convergence behavior of RLMS, based on the minimum mean square error (MSE), is analyzed for operation with finite precision. Computer simulation results verify that a wordlength of nine bits is sufficient for the RLMS algorithm to achieve performance close to that provided by full precision. The performance measures used include residual MSE, rate of convergence, error vector magnitude (EVM), and beam pattern. Based on all these measures, it is shown that the RLMS algorithm outperforms other earlier algorithms, such as least mean square (LMS), recursive least square (RLS), modified robust variable step size (MRVSS) and constrained stability LMS (CSLMS).

Beamforming Based Algorithm for 5G Applications

“…The performance of the smart antennas or beamformers is highly dependent on the implemented adaptive beamforming algorithms. In [17,18], a new adaptive beamforming algorithm is proposed, studied, and evaluated. This algorithm is called Recursive Least Mean Square Algorithm (RLMS).…”

Design of digital signal processor based adaptive beamformer using hybrid residual least mean square algorithm for improved performance

“…It increases the computational complexity and sometimes the correlation matrix may be ill conditioned and hence, inversion may not be possible at all, owing to singularities of the matrix. Another hybrid algorithm has been proposed in Reference for antenna beamforming, which employs a recursive least squares (RLS) section followed by an LMS section for the attainment of positive features of LMS and RLS algorithms. This algorithm could able to overcome the drawbacks of LMS algorithm, but the drawbacks of RLS such as computational complexity, higher memory usage and implementation difficulties remained unaltered.…”

“…This algorithm could able to overcome the drawbacks of LMS algorithm, but the drawbacks of RLS such as computational complexity, higher memory usage and implementation difficulties remained unaltered. Both the algorithms as proposed in References have not addressed the minimization of SLL, which is very important in antenna array applications. Evolutionary algorithms like particle swarm optimization (PSO) algorithm can be used to synthesize the antenna arrays for the desired radiation pattern by optimizing the amplitude and progressive phase shifts of array elements.…”