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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.<\/jats:p>","DOI":"10.3390\/s23249687","type":"journal-article","created":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T10:45:33Z","timestamp":1701945933000},"page":"9687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["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"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4899-1718","authenticated-orcid":false,"given":"Qiong","family":"Wu","sequence":"first","affiliation":[{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China"},{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China"},{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China"},{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0658-6079","authenticated-orcid":false,"given":"Pingyi","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4940-7453","authenticated-orcid":false,"given":"Qiang","family":"Fan","sequence":"additional","affiliation":[{"name":"Qualcomm, San Jose, CA 95110, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3021-5013","authenticated-orcid":false,"given":"Huiling","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Kent, Canterbury CT2 7NT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0881-3594","authenticated-orcid":false,"given":"Jiangzhou","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Kent, Canterbury CT2 7NT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.23919\/JCC.2023.03.001","article-title":"High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks","volume":"20","author":"Wu","year":"2023","journal-title":"China Commun."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, Q., Wang, S., Ge, H., Fan, P., Fan, Q., and Letaief, K.B. 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