AP selection in Cell-Free Massive MIMO system using Machine Learning Algorithm

Biswas, Shreyashri; Ponnusamy, Vijayakumar

doi:10.1109/wispnet51692.2021.9419450

Cited by 23 publications

(11 citation statements)

References 12 publications

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“…• Generalization of models: Transfer learning allows the development of models trained from different sources and tasks to be generalized. Generalized models could perform well on different communications scenarios and new paradigms such as NOMA [113] or cell-free schemes [114].…”

Section: Exploring Different Learning Approachesmentioning

confidence: 99%

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Ibarra-Hernández,

Castillo-Soria,

Gutiérrez

et al. 2024

Future Internet

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Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic properties for controlling the propagation of the signals in the wireless channel. The ML-optimized (RIS)-assisted wireless communication systems can be an effective alternative to mitigate the degradation suffered by the signal in the wireless channel, providing significant advantages in the system’s performance. However, the variety of approaches, system configurations, and channel conditions make it difficult to determine the best technique or group of techniques for effectively implementing an optimal solution. This paper presents a comprehensive review of the reported frameworks in the literature that apply ML and RISs to improve the overall performance of the wireless communication system. This paper compares the ML strategies that can be used to address the RIS-assisted system design. The systems are classified according to the ML method, the databases used, the implementation complexity, and the reported performance gains. Finally, we shed light on the challenges and opportunities in designing and implementing future RIS-assisted wireless communication systems based on ML strategies.

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Section: Exploring Different Learning Approachesmentioning

confidence: 99%

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Ibarra-Hernández,

Castillo-Soria,

Gutiérrez

et al. 2024

Future Internet

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“…Meanwhile, K-means clustering algorithms have been utilized for AP selection in a CF-mMIMO network due to the ease of implementation [31], [32]. Biswas and Vijayakumar [31] introduced a K-means based AP selection aiming to reduce the computation workload and pilot contamination. Riera-Palou et al [32] developed a user association method based on K-means with the objective of minimizing pilot contamination.…”

Section: A Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for Energy Efficiency Maximization in Cache-Enabled Cell-Free Massive MIMO Networks: Single- and Multi-Agent Approaches

Chuang

Chiu

Chang

et al. 2023

IEEE Trans. Veh. Technol.

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Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging beyond fifth-generation (5G) technology that improves energy efficiency (EE) and removes cell structure limitation by using multiple access points (APs). This study investigates the EE maximization problem. Forming proper cooperation clusters is crucial when optimizing EE, and it is often done by selecting AP-user pairs with good channel quality or aligning AP cache contents with user requests. However, the result can be suboptimal if we determine the clusters based solely on either aspect. This motivates our joint design of user association and content caching. Without knowing the user content preferences in advance, two deep reinforcement learning (DRL) approaches, i.e., single-agent reinforcement learning (SARL) and multi-agent reinforcement learning (MARL), are proposed for different scenarios. The SARL approach operates in a centralized manner which has lower computational requirements on edge devices. The MARL approach requires more computation resources at the edge devices but enables parallel computing to reduce the computation time and therefore scales better than the SARL approach. The numerical analysis shows that the proposed approaches outperformed benchmark algorithms in terms of network EE in a small network. In a large network, the MARL yielded the best EE performance and its computation time was reduced significantly by parallel computing.

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“…Results indicate that the proposed approach can improve all considered performance metrics (sum user rate, minimum user rate and SINR) with significantly reduced computational complexity compared to the complex optimization solver. In [95], a k-means++ clustering algorithm has been proposed for AP selection in cell-free m-MIMO systems, based on the maximization of SE. According to the presented results the performance of the proposed approach is improved compared to other wellknown approaches with reduced computational complexity.…”

Section: Distributed and Cell-free Massive Mimo Configurationsmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

Gkonis

2023

IEEE Access

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The deployment of fifth-generation (5G) broadband wireless cellular networks has enabled the support of highly demanding applications, paving the way towards global broadband connectivity. In the same context, related advances in network infrastructure, such as network function virtualization via the decoupling of related functionalities from hardware equipment has leveraged end-to-end service support in highly heterogeneous environments. To this end, uninterrupted provision of service necessitates a holistic network redesign where access points (APs) should be placed and configured dynamically. One of the key supporting technologies of 5G communications are massive multiple input multiple output (m-MIMO) configurations, deployed over various APs, either in a centralized or in a distributed manner. However, such a configuration would pose an additional degree of complexity during network planning, since on one hand accurate channel estimation for all potential links is limited due to the pilot contamination effect, and on the other hand the number of actually deployed radio frequency chains is again limited when compared to the fully digital approach, in an effort to relax hardware and computational burden. Moreover, additional issues should be taken into consideration as well, such as the coexistence of m-MIMO configurations with other novel technologies in the 5G air interface as well as the need for provision of acceptable quality of service to a large number of mobile users. In an effort to deal with the aforementioned issues, machine learning (ML) algorithms have emerged over the last decade as a promising solution that can deal with complex optimization problems. In this context, the main goal of this survey paper is to present the state-of-the-art approaches in the deployment of ML methods in m-MIMO configurations. Unlike other related survey papers, this is the first scientific attempt to cover all aspects of m-MIMO practical implementations (i.e., channel estimation, MIMO precoding and signal detection, hybrid beamforming, distributed configurations, user pairing and power allocation in non-orthogonal multiple access), in the context of ML-aided calculations. To this end, all presented works are categorized according to the studied aspect, while in the same context potential limitations and future challenges are highlighted as well.INDEX TERMS Massive MIMO, machine learning, channel estimation, hybrid beamforming, cell-free orientations, NOMA.

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AP selection in Cell-Free Massive MIMO system using Machine Learning Algorithm

Cited by 23 publications

References 12 publications

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Deep Reinforcement Learning for Energy Efficiency Maximization in Cache-Enabled Cell-Free Massive MIMO Networks: Single- and Multi-Agent Approaches

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

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