Performance of RLMS algorithm in adaptive array beam forming

Srar, Jalal Abdulsayed

doi:10.1109/iccs.2008.4737233

Cited by 19 publications

(10 citation statements)

References 3 publications

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“…Recently, a different approach has been adopted to overcome the drawbacks of both the LMS and RLS algorithms. The new RLMS algorithm converges rapidly and is able to track properly in time varying environments [10]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 88%

Analysis of the RLMS adaptive beamforming algorithm implemented with finite precision

Srar

Mansour

2010

2010 16th Asia-Pacific Conference on Communications (APCC)

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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).

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Section: Introductionmentioning

confidence: 88%

Analysis of the RLMS adaptive beamforming algorithm implemented with finite precision

Srar

Mansour

2010

2010 16th Asia-Pacific Conference on Communications (APCC)

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“…An adaptive configuration involving the use of RLS preceding LMS is presented in [7,8]. This new RLMS set up is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

RLMS algorithm for fixed or adaptive beamforming

Srar

2009

2009 IEEE 9th Malaysia International Conference on Communications (MICC)

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This paper presents a flexible method of achieving either fixed or self-adaptive antenna beamforming. It involves the use of the array image factor ' d A , which interfaces the Recursive Least Squares (RLS) and Least Mean Squares (LMS) sections in cascade to form the RLMS beamforming algorithm. It is shown that an accurate fixed beam can be obtained by simply setting the elements of ' d A with prescribed values corresponding to the required direction. Moreover, the beam direction can also be made adaptive to automatically track the target signal. In this case, a simple method is described for estimating the element values of ' d A based on the estimated RLS output signal and tap weights. Computer simulation results verify these two modes of operation of the RLMS beamforming algorithm. Furthermore, the convergence performance of RLMS is shown to be quite insensitive to variations in SNR of the input signal as well as step sizes associated with the RLS and LMS sections. Keywords-Fixed beam, RLMS algorithm, adaptive antenna array beam forming.

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“…In this regard, various researchers are striving to excel in these prospects. In [1], LLMS algorithm is developed using LMS-LMS algorithms for stable convergence to get optimum results, whereas the same author has proposed another algorithm for beamforming known as RLMS [2,3] using a combined RLS-LMS algorithm to provide a robust performance. Similarly in [4], Feng et al proposed a fast recursive total least squares (TLS) algorithm for adaptive FIR filtering, whereas in [5] a simple variable step size LMS (VSSLMS) adaptive algorithm is presented for simple, robust, efficient, fast convergence and low steady state MSE.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Algorithm Addresses Potential Problems of Blind Algorithm

Yasin

Hussain

2016

International Journal of Antennas and Propagation

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A hybrid algorithm called constant modulus least mean square (CMLMS) algorithm is proposed in order to address the potential problems existing with constant modulus algorithm (CMA) about its convergence. It is a two-stage adaptive filtering algorithm and based on least mean square (LMS) algorithm followed by CMA. A hybrid algorithm is theoretically developed and the same is verified through MatLab Software. Theoretical model is verified through simulation and its performance is evaluated in smart antenna in presence of a cochannel interfering signal and additive white Gaussian noise (AWGN) of zero mean. This is also tested in Rayleigh fading channel using digital modulation technique for Bit Error Rate (BER). Finally, a few computer simulations are presented in order to substantiate the theoretical findings with respect to proposed model. Corresponding results obtained with the use of only CMA and LMS algorithms are also presented for further comparison.

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Performance of RLMS algorithm in adaptive array beam forming

Cited by 19 publications

References 3 publications

Analysis of the RLMS adaptive beamforming algorithm implemented with finite precision

Analysis of the RLMS adaptive beamforming algorithm implemented with finite precision

RLMS algorithm for fixed or adaptive beamforming

A Novel Adaptive Algorithm Addresses Potential Problems of Blind Algorithm

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