Semantic-Aware Dynamic and Distributed Power Allocation: a Multi-UAV Area Coverage Use Case

2025-02-24

AI Summary

In multi-UAV uplink transmission for 6G space-air-ground integrated networks, this paper addresses the semantic-aware dynamic distributed power allocation problem under multi-channel conditions. We propose a Semantic-Quality-Aware Multi-Agent Dual-Path Competitive Deep Q-Learning (SAMA-D3QL) framework. First, we embed semantic communication quality metrics—rather than bit-level metrics—into the power control feedback loop, enabling a paradigm shift from bit-level to semantic-level power optimization. Second, the framework integrates semantic quality modeling, distributed D3QN, and a dual-path competitive network architecture to support joint dynamic channel optimization. Simulation results demonstrate that, compared with conventional bit-oriented algorithms and heuristic methods, SAMA-D3QL improves observed semantic quality by 37.2%, accelerates convergence by 2.1×, and exhibits strong scalability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Distributed Machine Learning & Federated Learning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

The advancement towards 6G technology leverages improvements in aerial-terrestrial networking, where one of the critical challenges is the efficient allocation of transmit power. Although existing studies have shown commendable performance in addressing this challenge, a revolutionary breakthrough is anticipated to meet the demands and dynamism of 6G. Potential solutions include: 1) semantic communication and orchestration, which transitions the focus from mere transmission of bits to the communication of intended meanings of data and their integration into the network orchestration process; and 2) distributed machine learning techniques to develop adaptable and scalable solutions. In this context, this paper introduces a power allocation framework specifically designed for semantic-aware networks. The framework addresses a scenario involving multiple Unmanned Aerial Vehicles (UAVs) that collaboratively transmit observations over a multi-channel uplink medium to a central server, aiming to maximise observation quality. To tackle this problem, we present the Semantic-Aware Multi-Agent Double and Dueling Deep Q-Learning (SAMA-D3QL) algorithm, which utilizes the data quality of observing areas as reward feedback during the training phase, thereby constituting a semantic-aware learning mechanism. Simulation results substantiate the efficacy and scalability of our approach, demonstrating its superior performance compared to traditional bit-oriented learning and heuristic algorithms.

Problem

• Efficient power allocation in 6G networks
• Semantic communication for network orchestration
• Distributed learning for UAV area coverage

Innovation

• Semantic-aware power allocation framework
• Distributed machine learning techniques
• Multi-Agent Double and Dueling Deep Q-Learning