Abstract
Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for short-packet internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging due to the absence of closed-loop power control. We design a prototype of transmit power pool (PP) to provide open-loop power control. IoT users acquire their transmit power in advance from this prototype PP solely according to their communication distances. Firstly, a multi-agent deep Q-network (DQN) aided GF-NOMA algorithm is proposed to determine the optimal transmit power levels for the prototype PP. More specifically, each IoT user acts as an agent and learns a policy by interacting with the wireless environment that guides them to select optimal actions. Secondly, to prevent the Q-learning model overestimation problem, double DQN (DDQN) based GF-NOMA algorithm is proposed. Numerical results confirm that the DDQN based algorithm finds out the optimal transmit power levels that form the PP. Comparing with the conventional online learning approach, the proposed algorithm with the prototype PP converges faster under changing environments due to limiting the action space based on previous learning. The considered GF-NOMA system outperforms the networks with fixed transmission power, namely all the users have the same transmit power and the traditional GF with orthogonal multiple access techniques, in terms of throughput.
Highlights
One of the main challenges to the generation cellular networks is the provision of massive connectivity to explosively increased Internet-of-things (IoT) users, especially for uplink transmission
We propose a multi-agent deep Q network (DQN) and double deep Q-network (DQN) (DDQN) based GF-non-orthogonal multiple access (NOMA) algorithm for prototype power pool design, where the BS broadcasts this pool to all IoT users so as to avoid acquiring channel state information (CSI)
We show the advantages of multi-agent DDQN over traditional multiagent DQN for GF-NOMA IoT networks
Summary
One of the main challenges to the generation cellular networks is the provision of massive connectivity to explosively increased Internet-of-things (IoT) users, especially for uplink transmission. In current cellular networks, enabling multiple access with limited resources is an inherent problem. Non-orthogonal multiple access (NOMA) with a new degree of freedom, namely the power domain, has been established as a promising technique for the solution of this problem [2]. Some latest work investigating NOMA from different aspects can be found in [3] [4] [5]. Grant-based (GB) has been widely studied, it fails to provide sufficient access to IoT users with short packets, since multiple. Part of this work was submitted in IEEE International Conference on Communications (ICC), June, Canada, 2021 [1]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.