Semi-Centralized Optimization for Energy Efficiency in IoT Networks With NOMA

We propose a novel semi-centralized framework for Internet-of-Things (IoT) networks with non-orthogonal multiple access to maximize the energy efficiency (EE) of two types of clients, namely grant-based (GB) and grant-free (GF). We use a proximal policy optimization algorithm to maximize the EE of G...

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Bibliographic Details
Published in:IEEE wireless communications letters 2023-02, Vol.12 (2), p.366-370
Main Authors: Alajmi, Abdullah, Fayaz, Muhammad, Ahsan, Waleed, Nallanathan, Arumugam
Format: Article
Language:English
Subjects:
Algorithms
Clients
Complexity theory
Computer networks
deep reinforcement learning
Energy efficiency
Internet of Things
Logic gates
Machine learning
Mathematical models
Multiagent systems
NOMA
Non-orthogonal multiple access
Nonorthogonal multiple access
Optimization
Quality of service
Resource allocation
Resource management
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Summary:We propose a novel semi-centralized framework for Internet-of-Things (IoT) networks with non-orthogonal multiple access to maximize the energy efficiency (EE) of two types of clients, namely grant-based (GB) and grant-free (GF). We use a proximal policy optimization algorithm to maximize the EE of GB clients and a multi-agent deep Q-network to optimize resource allocation for GF clients aided by a gateway node. The proposed algorithm combines the advantages of fully centralized and fully distributed frameworks to compensate for their shortcomings (complexity and long learning time). The numerical results show that the proposed algorithm enhances the EE of GB clients by 6% and 11.5%, respectively, compared with the fixed power allocation and random power allocation strategies. Moreover, the results demonstrate a 47.4% increase in the EE of GF clients over the benchmark scheme. Additionally, we show that the increase in the number of GB clients has a significant impact on the EE of GB and GF clients.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2022.3227135