DECENT: Deep learning enabled green computation for edge centric 6G networks

Kashyap, PK, Kumar, S, Jaiswal, A, Kaiwartya, O ORCID: https://orcid.org/0000-0001-9669-8244, Kumar, M, Dohare, U and Gandomi, AH, 2022. DECENT: Deep learning enabled green computation for edge centric 6G networks. IEEE Transactions on Network and Service Management. ISSN 1932-4537

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Abstract

Edge computing has received significant attention from academia and industries and has emerged as a promising solution for enhancing the information processing capability at the edge for next generation 6G networks. The technical design of 6G edge networks in terms of offloading the computationally extensive task is very critical because of the overgrowth in data volume primarily due to the explosion of smart IoT devices, and the ever-reducing size of these energy-constrained devices in IoT systems. Toward harnessing the benefits of deep recurrent neural network based on Long Short Term Memory (LSTM) in the design of next-generation edge networks, this paper presents a framework DECENT-Deep learning Enabled green Computation for Edge centric Next generation 6G neTworks. The data offloading problem is modeled as a Markov decision process considering joint optimization of energy consumption, computation latency, and offloading rate for network utility in 6G environment. The algorithm learns faster from previous long-term offloading experiences and solves the optimization problem with better convergence speed. Simulation results of the proposed framework DECENT shows that it maximizes the network utility by overcoming the challenges as compared to the state-of-the-art techniques.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Network and Service Management
Creators:	Kashyap, P.K., Kumar, S., Jaiswal, A., Kaiwartya, O., Kumar, M., Dohare, U. and Gandomi, A.H.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	21 January 2022
ISSN:	1932-4537
Identifiers:	Number Type 10.1109/tnsm.2022.3145056 DOI 1511950 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	27 Jan 2022 16:06
Last Modified:	27 Jan 2022 16:06
URI:	https://irep.ntu.ac.uk/id/eprint/45434

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