Smart Antennas for Multiuser DS/CDMA Communications in Multipath Fading Channels

Ueng, Fang‐Biau; Chen, Junda; Cheng, Sheng-Han

doi:10.1093/ietcom/e88-b.7.2944

Cited by 14 publications

(14 citation statements)

References 7 publications

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“…2a -2c show the convergence behaviors of the four adaptive schemes for SNR values of 5, 10, and 15 dB, respectively. For the proposed LLMS scheme, the theoretical convergence error calculated using (16) and (17) for SNR=10 dB is also shown in Fig. 2b.…”

Section: Simulationsmentioning

confidence: 98%

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Adaptive array beamforming using a combined LMS-LMS algorithm

Srar¹,

Mansour²

2010

2010 IEEE Aerospace Conference

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Abstract-A new adaptive algorithm, called LLMS, which employs two Least Mean Square (LMS) sections in tandem, is proposed for different applications of array beamforming. 12The convergence of the LLMS algorithm is analyzed, in terms of mean square error, in the presence of Additive White Gaussian Noise (AWGN) for two different operation modes; normal referencing and self-referencing. Computer simulation results show that the convergence performance of LLMS is superior to the conventional LMS algorithms as well some of the more recent LMS based algorithms, such as constrained-stability LMS (CSLMS), and Modified Robust Variable Step Size LMS (MRVSS) algorithms. It is shown that the convergence of LLMS is quite insensitive to variations in both the input signal-to-noise ratio and the step size used. Also, the operation of the proposed algorithm remains stable even when its reference signal is corrupted by AWGN noise. Furthermore, the fidelity of the signal at the output of the LLMS beamformer is demonstrated through the Error Vector Magnitude (EVM) and the scatter plot obtained.

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Section: Simulationsmentioning

confidence: 98%

“…where • signifies modulus; 1 2 ( ) ( ) ( 1) D j d j e j = − − , and Q is the correlation matrix of the input signals given by [17] as…”

Section: Analysis With An External Referencementioning

confidence: 99%

Adaptive array beamforming using a combined LMS-LMS algorithm

Srar¹,

Mansour²

2010

2010 IEEE Aerospace Conference

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“…In [2], dual orthogonal polarization diversity antenna elements are used in the front-end of discrete cylindrical lens array to generate a fan shaped fixed beam. On the other hand, different adaptive algorithms, including LMS and RLS, have been introduced for realizing steerable beam adaptive antennas [3]. Recently, variants of LMS and RLS have been investigated for improving the convergence speed and tracking ability in time varying environments.…”

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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“…The signals picked up by these individual elements are combined through the use of a signal processing unit to form a beam pattern that can be steered toward the desired coverage direction [1]. The performance of the signal processing unit is generally dictated by the beam forming algorithm used.…”

Section: Introductionmentioning

confidence: 99%

“…The former has good tracking performance with low computational complexity, and is robust against numerical errors. On the other hand, the RLS algorithm can achieve a faster convergence which is independent of the eigen-value spread variations of the input signal correlation matrix [1]. These desirable features offered by both the LMS and RLS algorithms can be jointly realized through the use of a new algorithm, called RLMS [2].…”

Section: Introductionmentioning

confidence: 99%

Performance of RLMS algorithm in adaptive array beam forming

Srar

2008

2008 11th IEEE Singapore International Conference on Communication Systems

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Smart Antennas for Multiuser DS/CDMA Communications in Multipath Fading Channels

Cited by 14 publications

References 7 publications

Adaptive array beamforming using a combined LMS-LMS algorithm

Adaptive array beamforming using a combined LMS-LMS algorithm

RLMS algorithm for fixed or adaptive beamforming

Performance of RLMS algorithm in adaptive array beam forming

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