Deep reinforcement learning empowered energy efficient task-offloading in cloud-radio access networks. (7th February 2023)

• Record Type: Journal Article
• Authors: Kumar, Naveen; Ahmad, Anwar
• Is Part Of: International journal of communication networks and distributed systems. Volume 29:Number 3(2023)
• Page Start: 341
• Page End: 358
• Publication Date: 2023-02-07
• Subjects: C-RAN -- deep reinforcement learning -- DRL -- mobile edge computing -- Q-learning -- resource allocation -- task offloading; Computer networks -- Periodicals; Telecommunication systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; 004.6
• Languages: English
• ISSNs: 1754-3916

Abstract

Mobile applications often demand computationally heavy resources to attain high quality, on the other hand, running all programs on a single mobile device still consumes a lot of energy and generates a lot of delay. By breaking a single operation into distinct components, partial computational offloading may be intelligently investigated in mobile edge computing (MEC) to minimise energy consumption and service latency of mobile device. Some of the components run on the mobile device itself, while the rest are sent to a mobile edge server. Current task offloading systems, primarily focus on average-based performance indicators, failing to satisfy the deadline constraints. This paper offers a deep reinforcement learning (DRL) empowered energy efficient task offloading method, to optimise the reward under task deadline constraints. Simulation results show that the proposed technique can efficiently transfer traffic from cloud-radio access networks to next-generation node B, while also saving energy by turning off underutilised baseband processing units.