Approximate Kernel Orthogonalization for Antenna Array Processing

Navia-Vázquez, Ángel; Martínez‐Ramón, Manel; García-Muñoz, Luis Enrique; Christodoulou, C.G.

doi:10.1109/tap.2010.2078458

Cited by 8 publications

(4 citation statements)

References 35 publications

(39 reference statements)

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“…The optimal directive gain of adaptive array is determined by the optimality of the weights applied to the individual elements excitation signals. Least Mean Square (LMS) algorithm is one of the most popular algorithms to determine these optimal weights [10][11][12][13]. The LMS algorithm is a gradient-based approach, and it incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error at the current time [10][11][12][13].…”

Section: Weight Estimation For Beam Steering Using Lms Algorithm and mentioning

confidence: 99%

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Design of Adaptive Array With E-Shape Slot Radiator for Smart Antenna System

Kodgirwar¹,

Deosarkar²,

Joshi³

2020

PIER M

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This paper presents the design of an 8-element linear array for Adaptive Antenna applications using the Least Mean Square (LMS) algorithm towards improving the directive gain, beam steering capabilities, half-power beam-width, side-lobe level, and bandwidth of array. A conventional patch antenna is optimized to operate at 3.6 GHz (5G applications) with two symmetrical slots and Quarter Wave Transformer for feeding, and this design is extended up to 8 elements using CST Microwave Studio parameterization. The Return Loss (S 11), Directivity, HPBW, and VSWR of the antenna array are observed for the 2, 4, and 8 elements adaptive arrays. The inter-element spacing for resulting eight-element antenna array geometry is optimized to obtain maximum directive gain. This geometry appears promising in improving the directive gain from 7.6 dBi to 15.1 dBi for a single element to eight elements, respectively. Further, the LMS algorithm is used to compute the optimal complex weights, considering different angles for the desired User (+45 • and −45 •) and Interferer (+20 • and −20 •) during MATLAB simulation, and then these optimal weights are fed to antenna elements using CST for beam steering in a different direction. Maxima in the direction of user and nulls in the direction of interferer are obtained using CST software and found closely matching with MATLAB results.

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Section: Weight Estimation For Beam Steering Using Lms Algorithm and mentioning

confidence: 99%

“…At the (n + 1)th iteration, the array operates with weights w(n) computed at the previous iteration; however, the array signal vector is x(n + 1); the reference signal sample is r(n + 1); and the array output is as given in Equation (10).…”

Section: Weight Estimation For Beam Steering Using Lms Algorithm and mentioning

confidence: 99%

Design of Adaptive Array With E-Shape Slot Radiator for Smart Antenna System

Kodgirwar¹,

Deosarkar²,

Joshi³

2020

PIER M

View full text Add to dashboard Cite

show abstract

“…Least Mean Square (LMS) algorithm is one of the most popular algorithms to determine these optimal weights. The LMS algorithm is a gradient-based approach and it incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error at the current time [11].…”

Section: Weight Estimation Using Lms Algorithmmentioning

confidence: 99%

Adaptive Array Design using Least Mean Square Algorithm

Kodgirwar*,

Deosarkar,

Joshi

et al. 2020

IJRTE

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 Abstract: This paper presents the designing and testing of an 8-element linear array for Adaptive Antenna applications using the Least Mean Square (LMS) algorithm towards improving the directive gain of the array. A conventional patch antenna is optimized to operate at 2.35 GHz (4G applications) and this design is extended up to 8 elements using CST Microwave Studio parameterization. The S-parameters, Return Loss, Gain and VSWR of the antenna array are studied for the 2, 4, and 8 elements adaptive array. The simulation results are validated on the hardware setup and found closely matching with the experimental results. The resulting eight-element antenna array geometry is optimized with a coaxial feeding technique. This geometry appears promising in improving the gain from 6.13 to 23.5 dBi for a single element to eight elements respectively. Further, the LMS algorithm is used to compute the optimal complex weights considering different angles for desired User (60 o and 30 o ) and Interferer (10 o and 15 o ) during MATLAB simulation and then these optimal weights are fed to antenna elements using CST for beam steering in a different direction.Maximas are obtained at 54 o and 28 o when nulls are at 10 o and 15 o using CST software which is closely matching with MATLAB results.

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“…The applications of such arrays span a wide spectrum, including commercial wireless systems such as long-term evolution (LTE) and IEEE 802. 16, radar for beam scanning, mobile system, satellite system, and multiple-input multiple-output (MIMO) systems [8]− [10]. The benefits of adopting adaptive antenna array beamforming encompass enhancements in mean square error (MSE), signal to interference plus noise ratio (SINR), interference mitigation, counteraction of multipath fading, and directive gain [11]− [13].…”

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confidence: 99%

Design of adaptive array using least mean square beamformer

Pramod Kodgirwar,

R. Joshi,

B. Deosarkar

2024

IJEECS

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<span>This paper introduces an 8-element linear array designed for adaptive array applications, using least mean square (LMS) algorithm to enhance the directivity of the array. Microstrip antenna has been optimized at 2.3 GHz, a pivotal frequency ranges relevant to 4G and 5G applications. This design is thoughtfully extended to encompass 8-elements, achieved through the art of parameterization using computer simulation technology (CST) microwave studio. This geometry of 8-element exhibits considerable promise, significantly elevating the gain from 6.13 dBi for a single element to an impressive 15.5 dBi for all eight-element array. To further empower the array’s adaptability and beam-steering capabilities, the LMS algorithm is simulated. This intelligent algorithm computes complex weights, thoughtfully with various angles, including those for the interested user at 60° and 30°, as well as potential interferers at 10° and 15°, as simulated in MATLAB. These meticulously calculated weights are effectively applied to antenna elements using CST, facilitating beam steering in various directions. During CST simulations, notable peaks in performance emerge at 54° and 28°, strategically aligned with nulls at 10° and 15°. Remarkably, these results exhibit a remarkable degree of concurrence with those obtained through MATLAB simulations, affirming effectiveness of the proposed adaptive array design.</span>

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Approximate Kernel Orthogonalization for Antenna Array Processing

Cited by 8 publications

References 35 publications

Design of Adaptive Array With E-Shape Slot Radiator for Smart Antenna System

Design of Adaptive Array With E-Shape Slot Radiator for Smart Antenna System

Adaptive Array Design using Least Mean Square Algorithm

Design of adaptive array using least mean square beamformer

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