# A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications

arXiv: https://arxiv.org/abs/2401.06308 · HTML: https://mlsanity.com/paper/2401.06308/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [Task-Oriented Multi-User Semantic Communications](https://mlsanity.com/paper/2112.10255/) (2021)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic Communication](https://mlsanity.com/paper/2303.11543/) (2023)
- [Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees](https://mlsanity.com/paper/2105.04077/) (2021)
- [Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications](https://mlsanity.com/paper/2309.10180/) (2023)
- [QoS-Aware Service Prediction and Orchestration in Cloud-Network Integrated Beyond 5G](https://mlsanity.com/paper/2309.10185/) (2023)
- [Multi-Agent Deep Stochastic Policy Gradient for Event Based Dynamic Spectrum Access](https://mlsanity.com/paper/2004.02656/) (2020)
- [Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications](https://mlsanity.com/paper/2309.10177/) (2023)

## Built on by (most cited first, 6 of 6)

- [Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions](https://mlsanity.com/paper/2407.00081/) (2024)
- [An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning](https://mlsanity.com/paper/2502.16198/) (2025)
- [ORIENT: A Priority-Aware Energy-Efficient Approach for Latency-Sensitive Applications in 6G](https://mlsanity.com/paper/2402.06931/) (2024)
- [Fairness-Utilization Trade-off in Wireless Networks with Explainable Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2411.01924/) (2024)
- [Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook](https://mlsanity.com/paper/2508.15277/) (2025)
- [Exploiting Overlapping Fields of View for Redundancy-Aware Uplink Transmission in Vehicular 6G](https://mlsanity.com/paper/2606.31715/) (2026)

## Abstract (arXiv)

The emergence of the semantic-aware paradigm presents opportunities for innovative services, especially in the context of 6G-based applications. Although significant progress has been made in semantic extraction techniques, the incorporation of semantic information into resource allocation decision-making is still in its early stages, lacking consideration of the requirements and characteristics of future systems. In response, this paper introduces a novel formulation for the problem of multiple access to the wireless spectrum. It aims to optimize the utilization-fairness trade-off, using the α-fairness metric, while accounting for user data correlation by introducing the concepts of self- and assisted throughputs. Initially, the problem is analyzed to identify its optimal solution. Subsequently, a Semantic-Aware Multi-Agent Double and Dueling Deep Q-Learning (SAMA-D3QL) technique is proposed. This method is grounded in Model-free Multi-Agent Deep Reinforcement Learning (MADRL), enabling the user equipment to autonomously make decisions regarding wireless spectrum access based solely on their local individual observations. The efficiency of the proposed technique is evaluated through two scenarios: single-channel and multi-channel. The findings illustrate that, across a spectrum of α values, association matrices, and channels, SAMA-D3QL consistently outperforms alternative approaches. This establishes it as a promising candidate for facilitating the realization of future federated, dynamically evolving applications.