scholarly journals Online Computation Offloading in NOMA-Based Multi-Access Edge Computing: A Deep Reinforcement Learning Approach

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 99098-99109 ◽  
Author(s):  
Maurice Nduwayezu ◽  
Quoc-Viet Pham ◽  
Won-Joo Hwang
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Computation Offloading ◽  
Learning Approach ◽  
Online Computation ◽  
Multi Access

scholarly journals Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 134742-134753 ◽  
Author(s):  
Shengli Pan ◽  
Zhiyong Zhang ◽  
Zongwang Zhang ◽  
Deze Zeng
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Computation Offloading ◽  
Learning Approach ◽  
Mobile Edge Computing

scholarly journals Deep Reinforcement Learning for the Computation Offloading in MIMO-based Edge Computing

2021 ◽  
Author(s):  
Abdeladim Sadiki ◽  
Jamal Bentahar ◽  
Rachida Dssouli ◽  
Abdeslam En-Nouaary
Keyword(s):  
Reinforcement Learning ◽  
High Performance ◽  
Multiple Input Multiple Output ◽  
Edge Computing ◽  
Base Stations ◽  
Computation Offloading ◽  
Discrete Action ◽  
Computationally Intensive ◽  
Multi Access ◽  
Policy Optimization

Multi-access Edge Computing (MEC) has recently emerged as a potential technology to serve the needs of mobile devices (MDs) in 5G and 6G cellular networks. By offloading tasks to high-performance servers installed at the edge of the wireless networks, resource-limited MDs can cope with the proliferation of the recent computationally-intensive applications. In this paper, we study the computation offloading problem in a massive multiple-input multiple-output (MIMO)-based MEC system where the base stations are equipped with a large number of antennas. Our objective is to minimize the power consumption and offloading delay at the MDs under the stochastic system environment. To this end, we formulate the problem as a Markov Decision Process (MDP) and propose two Deep Reinforcement Learning (DRL) strategies to learn the optimal offloading policy without any prior knowledge of the environment dynamics. First, a Deep Q-Network (DQN) strategy to solve the curse of the state space explosion is analyzed. Then, a more general Proximal Policy Optimization (PPO) strategy to solve the problem of discrete action space is introduced. Simulation results show that the proposed DRL-based strategies outperform the baseline and state-of-the-art algorithms. Moreover, our PPO algorithm exhibits stable performance and efficient offloading results compared to the benchmark DQN strategy.

Sign in / Sign up

Export Citation Format

Share Document