Year: 2024
Type: article
Abstract: The Consumer Internet of Things (CIoT), a key aspect of the IoT, aims to integrate smart technologies into everyday life. In order to improve the spectral efficiency and provide massive connectivity to IoT networks, non-orthogonal multiple access (NOMA) variants like semi-grant-free (SGF) NOMA are employed. This paper aims to maximize secrecy energy efficiency (EE) for SGF-NOMA enabled CIoT in the presence of untrusted users (eavesdroppers) by utilizing a single-agent multi-agent deep reinforcement learning (SAMA-DRL) algorithm to overcome scalability and expensive learning issues. Given the limited long-distance transmission capabilities of CIoT devices, which typically have low transmit power, relay nodes are used to decode and forward data from grant-free (GF) users to the base station. Moreover, to enhance the coverage for GF users, the K-nearest neighbors (KNN) algorithm is utilized to place the relay nodes at an optimal positions. Furthermore, we design a collaborative contribution reward system to discourage user (agent) laziness. Simulation results show that the proposed SAMA-DRL-based SGF-NOMA algorithm for CIoT networks is more effective than baseline algorithms, achieving a 20% increase in secrecy EE compared to DRL-based SGF-NOMA without KNN. Moreover, the proposed scheme outperforms benchmark schemes in terms of EE across different radii. Additionally, we show that the proposed algorithm with quality of service based successive interference cancelation (SIC) is more power efficient as compared to conventional SIC decoding order.
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4401507914