Exploiting smart antennas in wireless mesh networks using contention access

Stine, John A.

doi:10.1109/mwc.2006.1632479

Cited by 55 publications

(43 citation statements)

References 9 publications

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“…The resultant "filtered" signal is further processed by the second LMS section, LMS 2 . For the adaptation process, the error signal of LMS 2 , 2 e , is fed back to combine with that of LMS 1 , to form the overall error signal, LLMS e , for updating the tap weights of LMS 1 . As shown in Fig.…”

Section: Lms Ymentioning

confidence: 99%

“…1, a common external reference signal is used for both the two LMS sections, i.e., 1 d and 2 d . Moreover, this external reference signal may be replaced by 1 LMS y in place of 2 d , and LLMS y for 1 d to produce a self-referenced version of the LLMS scheme, as described in Section II B.…”

Section: Lms Ymentioning

confidence: 99%

“…Beamforming is central to all antenna arrays, and a summary of beamforming techniques is presented in [2]. An overview of signal processing techniques used for adaptive antenna array beamforming is described in [3].…”

Section: Introductionmentioning

confidence: 99%

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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: Lms Ymentioning

confidence: 99%

Section: Lms Ymentioning

confidence: 99%

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Adaptive array beamforming using a combined LMS-LMS algorithm

Srar¹,

Mansour²

2010

2010 IEEE Aerospace Conference

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“…The ability for an antenna to adjust (steer) the radiation pattern towards the transmitter is called Beamforming [4]. Beamforming is central to all antenna arrays.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2018

IJANS

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In recent times, the use of Smart Antennas (SAs) The performance of an enhanced Recursive Least Squares (RLS) algorithm which takes into account an enhanced gain factor has been evaluated in this paper for adaptive array beamforming. Simulation results have shown that the enhanced RLS algorithm reduces a mean square error (MSE), smoothens a filter output and improves the Signal to Noise Ratio (SNR). The impact of these results can imply an increased range of beamforming and directivity of the smart antenna over a long range communication network. KEYWORDSSmart antennas; direction of arrival; adaptive arrays.

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“…Its use leads to an increase in the detection range of radar and sonar systems and it also increases the capacity of mobile Communication systems. Adaptive filtering [2] has been, and still is, an area of active research that plays an active role in an ever increasing number of applications such as noise cancellation, channel estimation, channel equalization and acoustic echo cancellation. The Least Mean Square (LMS) and its normalized version (NLMS) are the workhorses of adaptive filtering.…”

Section: Introductionmentioning

confidence: 99%

A New TURBO LMS Beamforming for Mobile Communication

Kumaraswamy¹,

Aaquib²,

Younus³

2010

IJCA

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Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce co-channel interferences and pointing independent beams towards various users, as well as its ability to provide estimate of directions of radiating sources, make it attractive to the mobile communications system designer.In this paper existing Least Mean Square (LMS) algorithm is modified to obtain lesser Mean Square Error (MSE) by using leaky factor in the weight updation.A novel approach named as TURBO LMS is proposed which reduces the Mean Square Error and increases the convergence speed by a large amount as compared to existing beamforming algorithms. Simulation of Beamforming algorithms namely Least Mean Square (LMS), Leaky Least Mean Square (LLMS) and novel TURBO LMS algorithm are done for various look directions and jammer configurations and their MSE characteristics are compared. MATLAB simulation shows that the proposed TURBO LMS algorithm has improved convergence as compared to existing beamforming algorithms. Performance of TURBO LMS algorithm is measured by varying number of array elements. KeywordsAdaptive array beamforming, LMS algorithm, TURBO LMS and Mean Square Error.

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Exploiting smart antennas in wireless mesh networks using contention access

Cited by 55 publications

References 9 publications

Adaptive array beamforming using a combined LMS-LMS algorithm

Adaptive array beamforming using a combined LMS-LMS algorithm

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A New TURBO LMS Beamforming for Mobile Communication

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