Deep Reinforcement Learning-based Joint Optimization of Delay and Privacy in Multiple-User MEC Systems

2022 ◽  
pp. 1-1
Author(s):  
Ping Zhao ◽  
Jiawei Tao ◽  
Lui Kangjie ◽  
Guanglin Zhang ◽  
Fei Gao
Keyword(s):  
Reinforcement Learning ◽  
Joint Optimization ◽  
Multiple User

scholarly journals Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 146264-146272 ◽  
Author(s):  
Han Qie ◽  
Dianxi Shi ◽  
Tianlong Shen ◽  
Xinhai Xu ◽  
Yuan Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Joint Optimization ◽  
Target Assignment ◽  
Multi Agent ◽  
Multi Uav

scholarly journals A reinforcement learning-based computing offloading and resource allocation scheme in F-RAN

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Fan Jiang ◽  
Rongxin Ma ◽  
Youjun Gao ◽  
Zesheng Gu
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Mixed Integer ◽  
Computational Resource ◽  
Multiple User ◽  
Radio Access ◽  
Wireless Environment ◽  
Resource Allocation Scheme ◽  
Two Phases ◽  
Performance Gains

AbstractThis paper investigates a computing offloading policy and the allocation of computational resource for multiple user equipments (UEs) in device-to-device (D2D)-aided fog radio access networks (F-RANs). Concerning the dynamically changing wireless environment where the channel state information (CSI) is difficult to predict and know exactly, we formulate the problem of task offloading and resource optimization as a mixed-integer nonlinear programming problem to maximize the total utility of all UEs. Concerning the non-convex property of the formulated problem, we decouple the original problem into two phases to solve. Firstly, a centralized deep reinforcement learning (DRL) algorithm called dueling deep Q-network (DDQN) is utilized to obtain the most suitable offloading mode for each UE. Particularly, to reduce the complexity of the proposed offloading scheme-based DDQN algorithm, a pre-processing procedure is adopted. Then, a distributed deep Q-network (DQN) algorithm based on the training result of the DDQN algorithm is further proposed to allocate the appropriate computational resource for each UE. Combining these two phases, the optimal offloading policy and resource allocation for each UE are finally achieved. Simulation results demonstrate the performance gains of the proposed scheme compared with other existing baseline schemes.

scholarly journals A Deep Reinforcement Learning Approach to IRS-assisted MU-MIMO Communication Systems

2022 ◽  
Author(s):  
Dariel Pereira-Ruisánchez ◽  
Óscar Fresnedo ◽  
Darian Pérez-Adán ◽  
Luis Castedo
Keyword(s):  
Reinforcement Learning ◽  
Phase Shift ◽  
Communication Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Joint Optimization ◽  
Mimo Communication ◽  
Continuous State ◽  
Policy Gradient ◽  
Sum Rate

<div>The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) framework is proposed to solve the joint optimization of the IRS phase-shift matrix and the precoding matrix in an IRS-assisted multi-stream multi-user MIMO communication.<br></div><div><br></div><div>The combination of multiple-input multiple-output(MIMO) communications and intelligent reflecting surfaces(IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6Gsystems. In this work, we develop an innovative deep reinforcement learning (DRL)-based approach to the joint optimization of the MIMO precoders and the IRS phase-shift matrices that is proved to be efficient in high dimensional systems. The proposed approach is termed deep deterministic policy gradient (DDPG)and maximizes the sum rate of an IRS-assisted multi-stream(MS) multi-user MIMO (MU-MIMO) system by learning the best matrix configuration through online trial-and-error interactions. The proposed approach is formulated in terms of continuous state and action spaces, and a sum-rate-based reward function. The computational complexity is reduced by using artificial neural networks (ANNs) for function approximations and it is shown that the proposed solution scales better than other state-of-the-art methods, while reaching a competitive performance.<br></div>

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