TY - JOUR
T1 - Semi-Centralized Optimization for Energy Efficiency in IoT Networks With NOMA
AU - Alajmi, Abdullah
AU - Fayaz, Muhammad
AU - Ahsan, Waleed
AU - Nallanathan, Arumugam
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - 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.
AB - 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.
KW - deep reinforcement learning
KW - Internet-of-Things
KW - Non-orthogonal multiple access
KW - resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85144787986&partnerID=8YFLogxK
U2 - 10.1109/LWC.2022.3227135
DO - 10.1109/LWC.2022.3227135
M3 - Article
AN - SCOPUS:85144787986
SN - 2162-2337
VL - 12
SP - 366
EP - 370
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 2
ER -
