# Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2103.15371 · HTML: https://mlsanity.com/paper/2103.15371/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 29 Mar 2021.

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

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges](https://mlsanity.com/paper/1609.06261/) (2016)
- [Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach](https://mlsanity.com/paper/1906.08812/) (2019)
- [Sub-channel Assignment, Power Allocation and User Scheduling for Non-Orthogonal Multiple Access Networks](https://mlsanity.com/paper/1608.08313/) (2016)
- [Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/1909.11074/) (2019)
- [Learning-Based Multi-Channel Access in 5G and Beyond Networks with Fast Time-Varying Channels](https://mlsanity.com/paper/2003.14403/) (2020)

## Built on by (most cited first, 5 of 5)

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Structure-Enhanced DRL for Optimal Transmission Scheduling](https://mlsanity.com/paper/2212.12704/) (2022)
- [Deep Reinforcement Learning for Uplink Multi-Carrier Non-Orthogonal Multiple Access Resource Allocation Using Buffer State Information](https://mlsanity.com/paper/2208.14689/) (2022)
- [Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2304.12184/) (2023)
- [Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems](https://mlsanity.com/paper/2501.06554/) (2025)

## Abstract (arXiv)

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.