Multi-input multi-out and non-orthogonal multiple access (MIMO-NOMA) internet-of-things (IoT) systems can improve channel capacity and spectrum efficiency distinctly to support the real-time applications. Age of information (AoI) is an important metric for real-time application, but there is no literature have minimized AoI of the MIMO-NOMA IoT system, which motivates us to conduct this work. In MIMO-NOMA IoT system, the base station (BS) determines the sample collection requirements and allocates the transmission power for each IoT device. Each device determines whether to sample data according to the sample collection requirements and adopts the allocated power to transmit the sampled data to the BS over MIMO-NOMA channel. Afterwards, the BS employs successive interference cancelation (SIC) technique to decode the signal of the data transmitted by each device. The sample collection requirements and power allocation would affect AoI and energy consumption of the system. It is critical to determine the optimal policy including sample collection requirements and power allocation to minimize the AoI and energy consumption of MIMO-NOMA IoT system, where the transmission rate is not a constant in the SIC process and the noise is stochastic in the MIMO-NOMA channel. In this paper, we propose the optimal power allocation to minimize the AoI and energy consumption of MIMO- NOMA IoT system based on deep reinforcement learning (DRL). Extensive simulations are carried out to demonstrate the superiority of the optimal power allocation.

翻译：多输入多输出非正交多址接入物联网系统（MIMO-NOMA IoT）可显著提升信道容量和频谱效率，从而支持实时应用。信息年龄（AoI）是衡量实时应用性能的重要指标，但目前尚无文献针对MIMO-NOMA IoT系统实现AoI最小化，这构成了本研究的动机。在MIMO-NOMA IoT系统中，基站（BS）确定采样需求并为每个物联网设备分配传输功率。各设备根据采样需求决定是否采集数据，并采用分配功率通过MIMO-NOMA信道将采样数据传输至BS。随后，BS采用连续干扰消除（SIC）技术解码各设备发送的数据信号。采样需求与功率分配策略会影响系统的AoI和能耗。在SIC过程中传输速率非恒定且MIMO-NOMA信道中存在随机噪声的条件下，确定包含采样需求与功率分配的最优策略以最小化系统AoI和能耗具有关键意义。本文提出基于深度强化学习（DRL）的最优功率分配方案，以实现MIMO-NOMA IoT系统的AoI与能耗最小化。通过大量仿真验证了该最优功率分配方案的优越性。