Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

Hoffmann, Marcin; Kryszkiewicz, Paweł

doi:10.1109/access.2021.3113461

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…This balance is essential for network operators aiming to optimize energy use without compromising service quality. The comparative analysis of our model with two studies [6,21], which used reinforcement Learning in massive MIMO networks underscores different optimization strategies. Our model focuses on a mathematical approach to balance active base stations and load factors for enhanced energy efficiency.…”

Section: Results and Dissectionmentioning

confidence: 99%

“…The energy-efficient solutions outlined in this study demonstrate considerable potential for fostering a sustainable and resource-conscious future in wireless communication networks, as the demand for wireless connectivity continues to experience substantial growth. The comparative analysis of our model with two studies [6,21], which used reinforcement Learning in massive MIMO networks underscores different optimization strategies. Our model focuses on a mathematical approach to balance active base stations and load factors for enhanced energy efficiency.…”

Section: Discussionmentioning

confidence: 99%

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Variational Optimization for Sustainable Massive MIMO Base Station Switching

Al-Samawi,

Nissirat

2024

Sensors

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Massive MIMO networks are a promising technology for achieving ultra-high capacity and meeting future wireless service demand. Massive MIMO networks, on the other hand, consume intensive energy. As a result, energy-efficient operation of massive MMO networks became a requirement rather than a luxury. Many NP-hard concavity search algorithms for optimal base station switching on-off scheme have been developed. This paper demonstrates the formulation of massive MIMO networks energy efficiency as a constrained variational problem. Our proposed method solution’s uniqueness and boundedness are demonstrated and proven. The developed system is a total energy optimization problem formulation. Furthermore, the order in which the base stations are switched on and off is specified for minimal handover overhead signaling and fair user capacity sharing. Results showed that variational optimization yielded optimal base station switching on and off with considerable energy saving achieved and maintaining the user capacity demand. Moreover, the proposed base station selection criteria provided suboptimal handover overhead signaling.

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Section: Results and Dissectionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Variational Optimization for Sustainable Massive MIMO Base Station Switching

Al-Samawi,

Nissirat

2024

Sensors

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“…Figure 3 presents the summarized important blocks that construct communication networks namely as: input/output transducers, transmitter (Tx), channel, and receiver (Rx). 7,[57][58][59][60] The system-level description of general communication networks comprises assembled sections where Tx and Rx sections includes various sub-blocks, and diverse parameters must be optimized to meet the required design specifications. Some of output specifications that can be optimized for obtaining the optimal solutions are taken from Reference 61.…”

Section: Optimization Methods In Communicationsmentioning

confidence: 99%

Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

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The sixth generation (6G) of mobile networks must support the huge growth of mobile connections and provides various intelligent services in future mobile networks. For that, designing high-performance network architecture and communication systems (CSs) as well as finding coherent solutions for the determined problems is critical demand that needs to be achieved in 6G networks.Optimal solutions are mainly sought by using modifiable, smart, and perceptive algorithms aimed at optimizing more specific tasks. Therefore, advanced optimization methods are highly required to accommodate the requirements of CSs efficiently. That will be in various parts of the future networks such as advanced mobility management, multi communication links, efficient power consumption, ultra-lower latency, ultra-security, high speed, and reliable connectivity.Accordingly, highly accurate and smart functions must be modeled to optimize the required communication parameters in the network. This study provides a comprehensive overview of optimization methods that may need further investigations and developments to be applied in 6G networks at various parts of networks. A detailed theoretical description for each method is presented and discussed to elucidate future research directions for optimizing specific characteristics with multi-objective optimizations. Moreover, this article illustrates the conceptual and structural viewpoints of reported optimization methods. Also, the capability of various optimization methods that can offer industrial solutions in 6G is discussed. The potential applications of each method are also analyzed. Finally, this article presented the research issues and future directions of optimization technology and research gaps that need to be addressed before the standardization of 6G networks. INTRODUCTIONRecently, innovative solutions of communication systems (CSs) have been developed impressively, as data traffic is increasing exponentially by more than 50% per year. 1 Several factors will contribute for increasing data demand massively, for example, as predicted by CISCO, the video will make up 82% of the all IP traffic by 2022. 2,3 This massive increase in data traffic will cause several new issues that will facing the implementation of future mobile networks. That will include a dramatic increase for wider spectrum need, 4 the necessity for big system capacity, 5 further mobility issues, 6,7 and higher energy consumption, 8 in which cannot be handled efficiently by the current cellular networks.

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“…Hence, the operations in the digital beamformer, the analog precoder, the analog combiner and the digital combiner can be alternatively represented with the help of NNs with trainable parameters, instead of vectormatrix multiplication. In [72], an RL approach was considered for EE maximization in 5G m-MIMO orientations. At the first step of the proposed algorithm, EE was maximized independently for every set of MSs' positions.…”

Section: Massive Mimo Beamforming and Precodingmentioning

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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Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

Cited by 13 publications

References 39 publications

Variational Optimization for Sustainable Massive MIMO Base Station Switching

Variational Optimization for Sustainable Massive MIMO Base Station Switching

Overview of evolutionary algorithms and neural networks for modern mobile communication

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

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