Paper Info
Title
Competitive Algorithms and Reinforcement Learning for NOMA in IoT Networks
Abstract
This paper studies the problem of massive Internet of things (IoT) access in beyond fifth generation (B5G) networks using non-orthogonal multiple access (NOMA) technique. The problem involves massive IoT devices grouping and power allocation in order to respect the low latency as well as the limited operating energy of the IoT devices. The considered objective function, maximizing the number of successfully received IoT packets, is different from the classical sum-rate-related objective functions. The problem is first divided into multiple NOMA grouping subproblems. Then, using competitive analysis, an efficient online competitive algorithm (CA) is proposed to solve each subproblem. Next, to solve the power allocation problem, we propose a new reinforcement learning (RL) framework in which a RL agent learns to use the CA as a black box and combines the obtained solutions to each subproblem to determine the power allocation for each NOMA group. Our simulations results reveal that the proposed innovative RL framework outperforms deep-Q-learning methods and is close-to-optimal.
Year
DOI
Venue
2021
10.1109/ICC42927.2021.9500285
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)
Keywords
DocType
ISSN
Internet of things, non-orthogonal multiple access, online grouping, online power allocation, online competitive algorithms, reinforcement learning
Conference
1550-3607
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Zoubeir Mlika1133.84
Soumaya Cherkaoui218740.89