A comparison between different adaptive beamforming techniques

Patra, Rajen Kumar; Nayak, Chinmay Kumar

doi:10.1109/icort46471.2019.9069636

Cited by 16 publications

(12 citation statements)

References 5 publications

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“…The constrained NLMS (CNLMS) method is widely used in sparse array beamforming and has a higher convergence rate than other ABF algorithms [13], [14]. The convergence hindrance can also be overcome through the sample matrix inversion (SMI) method, which, although better in convergence rate than the LMS one [15], it may be prone to misleading outcomes due to potential singularities and the fact that the correlation matrix may be ill-conditioned [11]. Moreover, several ABF algorithms have been examined in [15], where it is shown that the recursive least square (RLS) method can converge faster (at the expense of additional complexity) than the LMS scheme.…”

Section: Algorithms Applied To Smart Antennasmentioning

confidence: 99%

“…The convergence hindrance can also be overcome through the sample matrix inversion (SMI) method, which, although better in convergence rate than the LMS one [15], it may be prone to misleading outcomes due to potential singularities and the fact that the correlation matrix may be ill-conditioned [11]. Moreover, several ABF algorithms have been examined in [15], where it is shown that the recursive least square (RLS) method can converge faster (at the expense of additional complexity) than the LMS scheme. In comparison to the LMS and RLS methods, the SMI method has higher computational cost due to the correlation matrix inversion involved in the SMI process, especially in cases of large numbers of antenna array elements.…”

Section: Algorithms Applied To Smart Antennasmentioning

confidence: 99%

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A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

et al. 2022

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The key objective of this paper is to explore the recent state-of-the-art artificial intelligence (AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented beamforming studies are thoroughly investigated in order to correctly comprehend and profitably interpret the AI contribution in the beamforming performance. Starting from a brief overview of beamforming, including adaptive beamforming algorithms and direction of arrival (DOA) estimation methods, our analysis probes further into the main machine learning (ML) classes, the basic neural network (NN) topologies, and the most efficient deep learning (DL) schemes. Subsequently, and based on the prior aspects, the paper explores several concepts regarding the optimal use of ML and NNs either as standalone beamforming and DOA estimation techniques or in combination with other implementations, such as ultrasound imaging, massive multipleinput multiple-output structures, and intelligent reflecting surfaces. Finally, particular attention is drawn on the realization of beamforming or DOA estimation setups via DL topologies. The survey closes with various important conclusions along with an interesting discussion on potential future aspects and promising research challenges.

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Section: Algorithms Applied To Smart Antennasmentioning

confidence: 99%

Section: Algorithms Applied To Smart Antennasmentioning

confidence: 99%

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

et al. 2022

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“…SMI algorithm can suppress an unknown interference based on an MMSE manner [15]. Compared to other interference suppression algorithms such as LMS and RLS, SMI has higher computational complexity due to the matrix inversion operation but has excellent interference suppression performance [26]. The weight of MMSE is obtained by solving the following optimization problem.…”

Section: Sample Matrix Inversion (Smi)mentioning

confidence: 99%

“…From (26), the proposed weight can be designed with relatively small computational complexity operations less than the conventional NSE. Summation of the correlation matrices…”

Section: Proposed Schemementioning

confidence: 99%

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Uplink Null-Space Expansion for Multiuser Massive MIMO in Time-Varying Channels Under Unknown Interference

2020

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The one‐bit diffusion for distributed beamforming over cell free wireless networks

Mostafapour,

Ghobadi,

Nourinia

et al. 2023

IET Communications

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This paper presents an investigation into the theoretical performance of diffusion adaptive networks within a distributed beam coordination application in a cell‐free communication network that utilizes coarsely quantized data. This analysis assumed data pulses with 1, 2, and 3 bits, and based on this assumption, simulations and theoretical analyses of distributed beamforming are conducted. The consistency between the simulation results and theoretical findings validated the accuracy of the calculations. Additionally, the distributed quantization aware least mean square (DQA‐LMS) algorithm was introduced for distributed beam coordination, demonstrating its superior performance in scenarios involving coarsely quantized data. Convergence conditions were provided and the results through various simulations were presented, including the array network mean square deviation (MSD), mean square error (MSE), and beamforming diagrams.

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A comparison between different adaptive beamforming techniques

Cited by 16 publications

References 5 publications

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

Uplink Null-Space Expansion for Multiuser Massive MIMO in Time-Varying Channels Under Unknown Interference

The one‐bit diffusion for distributed beamforming over cell free wireless networks

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