Deep Reinforcement Learning-Based Power Allocation for Minimizing Age of Information and Energy Consumption in Multi-Input Multi-Output and Non-Orthogonal Multiple Access Internet of Things Systems

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 24; p. 9687
• Main Authors: Wu, Qiong, Zhang, Zheng, Zhu, Hongbiao, Fan, Pingyi, Fan, Qiang, Zhu, Huiling, Wang, Jiangzhou
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.12.2023; MDPI
• Subjects: age of information; Algorithms; Deep learning; deep reinforcement learning; Energy consumption; Energy use; Internet of Things; MIMO-NOMA; Optimization; Spectrum allocation; Supply and demand; Wireless telecommunications equipment; deep reinforcement learning; Internet of Things; MIMO-NOMA; age of information
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23249687

Multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) Internet-of-Things (IoT) systems can improve channel capacity and spectrum efficiency distinctly to support real-time applications. Age of information (AoI) plays a crucial role in real-time applications as it determines the timeliness of the extracted information. In MIMO-NOMA IoT systems, the base station (BS) determines the sample collection commands and allocates the transmit power for each IoT device. Each device determines whether to sample data according to the sample collection commands and adopts the allocated power to transmit the sampled data to the BS over the MIMO-NOMA channel. Afterwards, the BS employs the successive interference cancellation (SIC) technique to decode the signal of the data transmitted by each device. The sample collection commands and power allocation may affect the AoI and energy consumption of the system. Optimizing the sample collection commands and power allocation is essential for minimizing both AoI and energy consumption in MIMO-NOMA IoT systems. In this paper, we propose the optimal power allocation to achieve it based on deep reinforcement learning (DRL). Simulations have demonstrated that the optimal power allocation effectively achieves lower AoI and energy consumption compared to other algorithms. Overall, the reward is reduced by 6.44% and 11.78% compared the to GA algorithm and random algorithm, respectively.