Multi-Agent Reinforcement Learning for Distributed Resource Allocation in Cell-Free Massive MIMO-Enabled Mobile Edge Computing Network

Tilahun, Fitsum Debebe; Abebe, Ameha T.; Kang, Chung G.

doi:10.1109/tvt.2023.3290954

Cited by 14 publications

(5 citation statements)

References 40 publications

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“…The methodology is based on the linear relationship between the number of input, and output functions and the number of computing nodes. A distributed approach based on cooperative multi-agent reinforcement learning has been examined in [84] to obtain adaptive and efficient joint communication and computing resource allocation. A distributed learning and inference scheme allows edge devices to train machine learning models devoid of the exchange of raw data.…”

Section: A Related Workmentioning

confidence: 99%

6G: Technology Evolution in Future Wireless Networks

Shafi,

Jha,

Jain

2024

IEEE Access

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The Sixth Generation (6G) Wireless Communication Network (WCN) is the successive provision to ameliorate the gain with ultra-low latency, and e xtremely high energy efficiency. The 6G WCN enables the specifications of artificial intelligence to optimize the services and capabilities. The vision of the 6G era is expected to address a seamless fusion of communication between the human, physical world, and digital world. The latest 6G WCN standard is a fundamental foundation and requires immense attention in the field of research. This paper presents the framework of 6G WCN with an illustration of its key technologies. The different technologies involved in 6G are well explained with the demonstration of the communication scenario such that the key performance indicators are improved with major differences. The primary contribution of this paper is the explanation of the 6G with the technologies that have a drastic impact on the characteristic aspects of a wireless communication network such as data rate, spectrum efficiency, energy efficiency, connection density, and reliability. All these technologies have the capability to revolutionize the subsequent WCN.

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Section: A Related Workmentioning

confidence: 99%

6G: Technology Evolution in Future Wireless Networks

Shafi,

Jha,

Jain

2024

IEEE Access

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“…[28] provides an overview of practical distributed edge learning techniques and their interplay with advanced communication optimization designs Refs. [29,30] combined cell-free networks with edge computing. User tasks were offloaded to edge servers to solve the joint communication and computational resource allocation problem and reduce CPU computational pressure.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

Joint AP Selection and Task Offloading Based on Deep Reinforcement Learning for Urban-Micro Cell-Free UAV Network

Pan,

Wang,

Yue

et al. 2023

Electronics

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The flexible mobility feature of unmanned aerial vehicles (UAVs) leads to frequent handovers and serious inter-cell interference problems in UAV-assisted cellular networks. Establishing a cell-free UAV (CF-UAV) network without cell boundaries effectively alleviates frequent handovers and interference problems and has been an important topic of 6G research. However, in existing CF-UAV networks, a large amount of backhaul data increases the computational pressure on the central processing unit (CPU), which also increases system delay. Meanwhile, the mobility of UAVs also leads to time-varying channel conditions. Therefore, designing dynamic resource allocation schemes with the help of edge computing can effectively alleviate this problem. Thus, aiming at partial network breakdown in an urban-micro (UMi) environment, an urban-micro CF-UAV (UMCF-UAV) network architecture is proposed in this paper. A delay minimization problem and a dynamic task offloading (DTO) strategy that jointly optimizes access point (AP) selection and task offloading is proposed to reduce system delay in this paper. Considering the coupling of various resources and the non-convex feature of the proposed problem, a dynamic resource cooperative allocation (DRCA) algorithm based on deep reinforcement learning (DRL) to flexibly deploy AP selection and task offloading of UAVs between the edge and locally is proposed to solve the problem. Simulation results show fast convergence behavior of the proposed algorithm compared with classical reinforcement learning. Decreased system delay is obtained by the proposed algorithm compared with other baseline resource allocation schemes, with the maximize improvement being 53%.

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“…The authors in [208] introduced a MIMO-enabled mobile edge network designed to meet the stringent requirements of advanced services. They created a joint communication and computing resource allocation (JCCRA) problem to minimize energy consumption while meeting the delay constraints [71].…”

Section: Application Scenariosmentioning

confidence: 99%

“…As XL-MIMO systems increase in antenna numbers and complexity, energy consumption becomes a significant issue. Ensuring sustainability and widespread adoption of XL-MIMO technology requires the development of energy-efficient processing algorithms, power allocation schemes, and antenna selection strategies [80], [208]. More specifically, the application of distributed signal processing algorithms can offload complex computations to more energy-efficient Baseband Units (BBUs) in the cloud rather than conducting them at the base station.…”

Section: B Energy Efficiency and Green Communicationmentioning

confidence: 99%

“…This can provide invaluable feedback for refining XL-MIMO technology's design, implementation, and optimization. For example, the work in [208] demonstrates the effectiveness of a distributed approach for minimizing energy consumption in CF mMIMO-enabled mobile edge networks, which could serve as a basis for further experimental evaluation. Similarly, the DAP architecture proposed in [80] could benefit from experimental validation to confirm its effectiveness in exploiting the near-field effects for capacity improvement in 6G XL-MIMO networks.…”

Section: E Testbeds and Experimental Evaluationmentioning

confidence: 99%

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Very large scale integration architectures of discrete transforms for digital signal processing applications

Wang¹

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Luo Jianwen, Associate Professor Jong Ching Chuen, and Associate Professor Chang Chip Hong, for their friendship, help, discussions, suggestions and encouragement.I do also appreciate the support and help from my colleagues in the FTD Solutions Pte Ltd and in the Centre for Signal Processing, NTD. Especially, I would like to thank Qian Yue, Lu Dawei, Yang Fan, and Dr. Yu Zhuliang for their valuable comments, suggestions and criticisms.I have enjoyed the company and friendship of all of my friends in Singapore. I would like to thank them for making the past five years an unforgettable and invaluable experience in my life.Last, I wish to express my deepest gratitude to my dear parents, my dear brother, and my lovely four year old nephew. It is indeed their deep love and consistent encouragement that has kept me focused, determined, cheered up, and never to say give up.

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Multi-Agent Reinforcement Learning for Distributed Resource Allocation in Cell-Free Massive MIMO-Enabled Mobile Edge Computing Network

Cited by 14 publications

References 40 publications

6G: Technology Evolution in Future Wireless Networks

6G: Technology Evolution in Future Wireless Networks

Joint AP Selection and Task Offloading Based on Deep Reinforcement Learning for Urban-Micro Cell-Free UAV Network

Very large scale integration architectures of discrete transforms for digital signal processing applications

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