# Deep Reinforcement Learning Based Power Allocation for Minimizing AoI and Energy Consumption in MIMO-NOMA IoT Systems

arXiv: https://arxiv.org/abs/2303.06411 · HTML: https://mlsanity.com/paper/2303.06411/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Joint Status Sampling and Updating for Minimizing Age of Information in the Internet of Things](https://mlsanity.com/paper/1807.04356/) (2018)
- [AoI Minimization in Status Update Control with Energy Harvesting Sensors](https://mlsanity.com/paper/2009.04224/) (2020)
- [Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems](https://mlsanity.com/paper/2104.01527/) (2021)
- [Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2107.14772/) (2021)
- [Time-Dependent Performance Modeling for Platooning Communications at Intersection](https://mlsanity.com/paper/2208.01213/) (2022)
- [Velocity-adaptive Access Scheme for MEC-assisted Platooning Networks: Access Fairness Via Data Freshness](https://mlsanity.com/paper/2108.03778/) (2021)
- [Delay Sensitive Task Offloading in the 802.11p Based Vehicular Fog Computing Systems](https://mlsanity.com/paper/2012.01122/) (2020)
- [AoI-Delay Tradeoff in Mobile Edge Caching with Freshness-Aware Content Refreshing](https://mlsanity.com/paper/2002.05868/) (2020)
- [Zero-Forcing Based Downlink Virtual MIMO-NOMA Communications in IoT Networks](https://mlsanity.com/paper/2209.11382/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X](https://mlsanity.com/paper/2501.12656/) (2025)

## Abstract (arXiv)

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.