DropNet: An Improved Dropping Algorithm Based on Neural Networks for Line-of-Sight Massive MIMO

Farsaei, Amirashkan; Sheikh, Alireza; Gustavsson, Ulf; Alvarado, Alex; Willems, F.M.J.

doi:10.1109/access.2020.3037562

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…One of the most important NN contributions is the treatment of the SRM problem, which is critical in massive MIMO systems [53], [54]. So, the drop algorithm based on NNs is used in [55] to select users that would be dropped, therefore leading to SRM. In [56], the sampling function NN has been proposed to manage the weights calculation of an adaptive antenna array thus resulting in an improved performance compared to the conventional RBFNN.…”

Section: Literature Review Of Neural Network-based Beamformingmentioning

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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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: Literature Review Of Neural Network-based Beamformingmentioning

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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“…Hence, the CBS aims to maximize the SINR gain of a particular user and does not guarantee the best achievable overall SINR gain for the whole system. Other classes of algorithms that can be used to perform user selection in massive MIMO are the user grouping algorithms [8], [34], [35] and recently proposed machine learning-based selection algorithms [36]. These algorithms separate users into clusters, serving a reduced number of users per cluster in the same time-frequency resource in order to decrease the interference between users within the same cluster.…”

Section: A User Selection In Massive Mimo Systemsmentioning

confidence: 99%

“…The complexity of SOS presented in Algorithm 1 is measured in terms of the number of operations used to select the best set of users. The most expensive operations are in (35), (36), (38), and (39). At the lth iteration, Algorithm 1 requires 2(M 3 +M 2 −M )(K−l+1) additions and 4M 3 (K−l+1) multiplications to compute (35).…”

Section: A Semi-orthogonal Selectionmentioning

confidence: 99%

“…At the lth iteration, Algorithm 1 requires 2(M 3 +M 2 −M )(K−l+1) additions and 4M 3 (K−l+1) multiplications to compute (35). Moreover, it requires 4M 2 (l − 1) additions and 4M 2 (l − 1) multiplications to compute (36). Additionally, Algorithm 1 requires 2M divisions, (K − l + 1) 2 -norm calculations, and one linear search in a (K − l + 1)dimensional space to compute (38) and (39).…”

Section: A Semi-orthogonal Selectionmentioning

confidence: 99%

“…For the S-SOS the most expensive operations are equations (35), (38), (39), and (43). The only difference between S-SOS and SOS is in (36) that is replaced with (43), which leads to the following required number of additions A S-SOS l , multiplications M S-SOS l , divisions D S-SOS l , and square root S S-SOS l computations per iteration are given by…”

Section: B Simplified Semi-orthogonal Selectionmentioning

confidence: 99%

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User Selection for Massive MIMO under Line-of-Sight Propagation

Chaves

Lima

Cetin

et al. 2022

IEEE Open J. Commun. Soc.

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This paper provides a review of user selection algorithms for massive multiple-input multiple-output (MIMO) systems under the line-of-sight (LoS) propagation model. Although the LoS propagation is extremely important to some promising technologies, like in millimeter-wave communications, massive MIMO systems are rarely studied under this propagation model. This paper fills this gap by providing a comprehensive study encompassing several user selection algorithms, different linear precoders and simulation setups, and also considers the effect of partial channel state information (CSI). One important result is the existence of practical cases in which the LoS propagation model may lead to significant levels of interference among users within a cell; these cases are not satisfactorily addressed by the existing user selection algorithms. Motivated by this issue, a new user selection algorithm based on inter-channel interference (ICI) called ICI-based selection (ICIBS) is proposed. Unlike other techniques, the ICIBS accounts for the ICI in a global manner, thus yielding better results, especially in cases where there are many users interfering with each other. In such scenarios, simulation results show that when compared to the competing algorithms, the proposed approach provided an improvement of at least 10.9% in the maximum throughput and 7.7% in the 95%-probability throughput when half of the users were selected.

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Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

et al. 2022

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Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the potential of challenging large-scale problems in conventional massive multiple-input-multiple-output MIMO (CM-MIMO) systems. This introduces the concept of intelligent mMIMO (I-mMIMO) systems. Due to the surge of application of different ML techniques in the enhancement of mMIMO systems for existing and emerging use cases beyond fifth-generation (B5G) networks, this article aims to provide an overview of the different aspects of the I-mMIMO systems. First, the characteristics and challenges of the CM-MIMO have been identified. Secondly, the most recent efforts aimed at applying ML to a different aspect of CM-MIMO systems are presented. Thirdly, the deployment of I-mMIMO and efforts towards standardization are discussed. Lastly, the future trends of I-mMIMO enabled application systems are presented. The aim of this paper is to assist the readers to understand different ML approaches in CM-MIMO systems, explore some of the advantages and disadvantages, identify some of the open issues, and motivate the readers toward future trends.

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DropNet: An Improved Dropping Algorithm Based on Neural Networks for Line-of-Sight Massive MIMO

Cited by 6 publications

References 13 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

User Selection for Massive MIMO under Line-of-Sight Propagation

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

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