Comparative analysis of adaptive beamforming algorithm LMS, SMI and RLS for ULA smart antenna

Patel, Dhaval N.; Makwana, Balvant J.; Parmar, Poonam

doi:10.1109/iccsp.2016.7754305

Cited by 20 publications

(8 citation statements)

References 5 publications

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“…This problem is solved using RLS algorithm which minimizes a weighted linear least square cost F I G U R E 7 Flowchart of SMI algorithm function relating to the input signals. [15][16][17]28 However, we can recursively calculate the required correlation matrix and the correlation vector as…”

Section: Rls Algorithmmentioning

confidence: 99%

“…The convergence speed of the SMI algorithm depends on the autocorrelation matrix with large eigenvalue which results in slow convergence. This problem is solved using RLS algorithm which minimizes a weighted linear least square cost function relating to the input signals 15‐17,28 . However, we can recursively calculate the required correlation matrix and the correlation vector as

{\overset{R}{true}}_{italicZZ} false(t false) = {false\sum}_{j = 1}^{t} \overset{true‾}{Z} false(j false) {\overset{Z}{true}}^{H} false(j false)

and

\overset{true‾}{p} false(t false) = {false\sum}_{j = 1}^{t} r^{*} false(j false) \overset{true‾}{Z} false(j false)

where “ t ” is the block length,

{\overset{R}{true}}_{italicZZ} false(t false)

and

\overset{true‾}{p} false(t false)

are the correlation estimates ending at time sample “ t .”…”

Section: Adaptive Beamformingmentioning

confidence: 99%

See 1 more Smart Citation

Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results

Imtiaj

Misra

Bhattacharya

2020

Engineering Reports

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Smart antennas are becoming popular in the area of cellular wireless communication for capacity enhancement and reduction of the multipath effect and interference. The demand for smart antennas is widely increasing as 5G cellular communication evolves to support the higher data rate and bandwidth. The basic principle of smart antenna design is adaptive beamforming using the best suited digital signal processing algorithms, such as least mean square (LMS), normalized least mean square (NLMS), sample matrix inversion (SMI), and recursive least square (RLS), each having its pros and cons. Among these, the LMS and NLMS are iterative approaches while SMI is a block adaptive method and RLS is a recursive method. The contribution of this article includes easy implementation of four adaptive beamforming algorithms, namely LMS, NLMS, SMI, and RLS. Furthermore, an exhaustive comparative performance analysis is carried out under five interferers and evaluated in terms of beamwidth, null depth, maximum sidelobe level, rate of convergence, error variation for the number of antenna elements, and spacing. Finally, a contrast table is presented to demonstrate the pros and cons of the listed algorithms. Time and dynamic space complexity are studied for RLS and SMI beamforming algorithms. Performance results from a designed reconfigurable testbed model for weight adaptation and smart beamforming are also presented for analysis and a better understanding of the real implementation of the smart algorithms through testbed design. The simulation and testbed results create the ground work for future exploration in the design of smart antenna beamforming.

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Section: Rls Algorithmmentioning

confidence: 99%

{\overset{R}{true}}_{italicZZ} false(t false) = {false\sum}_{j = 1}^{t} \overset{true‾}{Z} false(j false) {\overset{Z}{true}}^{H} false(j false)

and

\overset{true‾}{p} false(t false) = {false\sum}_{j = 1}^{t} r^{*} false(j false) \overset{true‾}{Z} false(j false)

where “ t ” is the block length,

{\overset{R}{true}}_{italicZZ} false(t false)

and

\overset{true‾}{p} false(t false)

are the correlation estimates ending at time sample “ t .”…”

Section: Adaptive Beamformingmentioning

confidence: 99%

Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results

Imtiaj

Misra

Bhattacharya

2020

Engineering Reports

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show abstract

“…There are a few anti-jamming algorithms with the adaptive antenna array, such as the least mean-square (LMS), recursive least-squares (RLS), simple matrix inversion (SMI) [19], uniform linear array(ULA), space-time adaptive processing (STAP) et.al [20]. Different algorithms have different anti-jamming performance and require different inputs [21]. In this study, we use the power inversion (PI) algorithm to form the nulling notch, which does not need prior interference information [22].…”

Section: B Beamforming With the Power Inversion Algorithmmentioning

confidence: 99%

A Real-Time Phase Center Variation Compensation Algorithm for the Anti-Jamming GNSS Antennas

et al. 2020

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The anti-jamming antenna is one of the most efficient ways to resist the adversary effect of the interference signal in GNSS positioning. Most anti-jamming antennas can adaptively adjust the radiation figure of the antenna to minimize the interference impact. A side effect of changing the radiation figure is that the antenna phase center (APC) is also changed accordingly. Meanwhile, a stable phase center is a prerequisite for high precision GNSS positioning, thus the anti-jamming and high-precision becomes a dilemma for the GNSS antenna design. This study proposed an efficient antenna phase center variation (PCV) compensation algorithm for the anti-jamming antennas which enables real-time high-precision applications with the anti-jamming antennas. The performance of the algorithm is evaluated with the short baseline tests, and the results indicate that the proposed algorithm can reduce the carrier phase residuals by compensation of the anti-jamming antenna PCV and consequently, the 3D root mean squares (RMS) of real-time kinematic (RTK) positioning in two experiments is improved by 42.3% and 9.7% respectively. After PCV correction, the RTK positioning with the anti-jamming antennas achieves better than 4 cm accuracy.

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“…The LA has excellent direct and narrowest main beam lobe in a wanted direction, but in all azimuthal directions it does not work equally well, a major drawback of the PA is the question of presence on the opposite side of an additional large lobe of the same strength [6] the symmetry of the CA structure provides an obvious benefit since, it has no edge components, without a major change in the beamform, directional patterns synthesized with a CA can be rotated electronically in the array plane [7]. Many algorithms have been introduced to applicants on SA [8,9], one of the most public algorithms is least mean square (LMS) A commonly employed algorithm due to its low computational complexity and ease of implementation [10]. Other less square algorithms, such as recursive least squares (RLS), conjugate gradient (CG), will converge more easily and have a lower stable mean square error (MSE) than LMS [11].…”

Section: Introductionmentioning

confidence: 99%

Rabid Euclidean direction search algorithm for various adaptive array geometries

2021

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One of the exciting technologies used to meet the increasing demand for wireless communication services is a smart antenna. A smart antenna is basically confirmed by an array of antennas and a digital beamformer unit through which cellular base station can direct the beam toward the desired user and set nulls toward interfering users. In this paper, different array configurations (linear, circular, and planer) with the REDS algorithm are implemented in the digital beam-forming unit. The wireless system performance is investigated to check the smart antenna potentials assuming Rayleigh fading channel environment beside the AWGN channel. Results show how the REDS algorithm offers a significant improvement through antenna radiation pattern optimization, sidelobe level, and interference reduction, and also the RDES algorithm proves fast convergence with minimum MSE and better sidelobe level reduction comparing with other algorithms.

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Comparative analysis of adaptive beamforming algorithm LMS, SMI and RLS for ULA smart antenna

Cited by 20 publications

References 5 publications

Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results

Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results

A Real-Time Phase Center Variation Compensation Algorithm for the Anti-Jamming GNSS Antennas

Rabid Euclidean direction search algorithm for various adaptive array geometries

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