Green NOMA based MU-MIMO transmission for MEC in 6G Networks

Dursun, Yunus; Goktas, Muhammed Burak; Ding, Zhengtao

doi:10.1016/j.comnet.2023.109749

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Let 𝑐 𝑚 represent the number of CPU cycles required for the 𝑚-th user to perform one sample of data and 𝐷 𝑚 represent the total data samples in local model training. Hence, the computation time dedicated to a local iteration from [24] and [27] is given as…”

Section: System Modelmentioning

confidence: 99%

Hybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networks

Kavitha,

Kavitha

2023

RADIOENGINEERING

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Wireless Federated Learning (WFL) is an innovative machine learning paradigm enabling distributed devices to collaboratively learn without sharing raw data. WFL is particularly useful for mobile devices that generate massive amounts of data but have limited resources for training complex models. This paper highlights the significance of reducing delay for efficient WFL implementation through advanced multiple access protocols and joint optimization of communication and computing resources. We propose optimizing the WFL Compute-then-Transmit (CT) protocol using hybrid Non-Orthogonal Multiple Access (H-NOMA).To minimize and optimize latency for the transmission of local training data, we use the Successive Convex Optimization (SCA) method, which efficiently reduces the complexity of non-convex algorithms. Finally, the numerical results verify the effectiveness of H-NOMA in terms of delay reduction, compared to the benchmark that is based on Non-Orthogonal Multiple Acces (NOMA).

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Section: System Modelmentioning

confidence: 99%

Hybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networks

Kavitha,

Kavitha

2023

RADIOENGINEERING

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Wireless powered NOMA-based cognitive radio for 6G networks

Dursun,

Al Basit,

Ding

2024

Computer Networks

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Comparative Analysis of NOMA and OMA Schemes: GSVD-based NOMA Systems and the Role of Mobile Edge Computing

Dursun

2023

JTIT

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This paper presents a comprehensive study that examines the fundamental concept of the non-orthogonal multiple access (NOMA) scheme and provides its detailed comparison with the orthogonal multiple access (OMA) technique. Furthermore, the paper explores the application of the generalized singular value decomposition (GSVD) method in conjunction with NOMA, accompanied by a detailed review of GSVD-based NOMA systems. This study also introduces the concept of mobile edge computing (MEC) and extensively discusses its key parameters. Furthermore, a comprehensive analysis of NOMA MEC is presented, shedding light on its potential advantages and challenges. The aims of this study are to provide a comprehensive understanding of the aforementioned topics and contribute to the advancement of MIMO-NOMA systems.

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Green NOMA based MU-MIMO transmission for MEC in 6G Networks

Cited by 3 publications

References 16 publications

Hybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networks

Hybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networks

Wireless powered NOMA-based cognitive radio for 6G networks

Comparative Analysis of NOMA and OMA Schemes: GSVD-based NOMA Systems and the Role of Mobile Edge Computing

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