# Deep Reinforcement Learning for Uplink Multi-Carrier Non-Orthogonal Multiple Access Resource Allocation Using Buffer State Information

arXiv: https://arxiv.org/abs/2208.14689 · HTML: https://mlsanity.com/paper/2208.14689/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Parameter Space Noise for Exploration](https://mlsanity.com/paper/1706.01905/) (2017)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [Deep Reinforcement Learning for Scheduling in Cellular Networks](https://mlsanity.com/paper/1905.05914/) (2019)
- [Efficient Entropy for Policy Gradient with Multidimensional Action Space](https://mlsanity.com/paper/1806.00589/) (2018)
- [Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2103.15371/) (2021)
- [NOMA for Next-generation Massive IoT: Performance Potential and Technology Directions](https://mlsanity.com/paper/2104.04911/) (2021)
- [Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information](https://mlsanity.com/paper/2108.12198/) (2021)

## Abstract (arXiv)

For orthogonal multiple access (OMA) systems, the number of served user equipments (UEs) is limited to the number of available orthogonal resources. On the other hand, non-orthogonal multiple access (NOMA) schemes allow multiple UEs to use the same orthogonal resource. This extra degree of freedom introduces new challenges for resource allocation. Buffer state information (BSI), like the size and age of packets waiting for transmission, can be used to improve scheduling in OMA systems. In this paper, we investigate the impact of BSI on the performance of a centralized scheduler in an uplink multi-carrier NOMA scenario with UEs having various data rate and latency requirements. To handle the large combinatorial space of allocating UEs to the resources, we propose a novel scheduler based on actor-critic reinforcement learning incorporating BSI. Training and evaluation are carried out using Nokia's "wireless suite". We propose various novel techniques to both stabilize and speed up training. The proposed scheduler outperforms benchmark schedulers.