Wireless Power Transfer and Intent-Driven Network Optimization in AAVs-assisted IoT for 6G Sustainable Connectivity

TL;DR

This work tackles the challenge of reliable intent interpretation and low-latency control in AAV-assisted IoT over 6G by introducing an implicit intent model and two novel components. The Hyperdimensional Transformer provides efficient, high-fidelity prediction of user intents from historical actions using hyperdimensional encoding and Hamming-distance attention, while DA-MAPPO coordinates multi-AAV actions with two decoupled policy networks to preserve dependencies in high-dimensional action spaces. The approach is validated on real IoT data with measured wireless channels, showing sub-100 ms intent translation and milliwatt-edge budgets, and consistently outperforming strong baselines in prediction and control tasks. Overall, the proposed framework advances scalable, intent-driven network optimization for sustainable 6G IoT connectivity.

Abstract

Autonomous Aerial Vehicle (AAV)-assisted Internet of Things (IoT) represents a collaborative architecture in which AAV allocate resources over 6G links to jointly enhance user-intent interpretation and overall network performance. Owing to this mutual dependence, improvements in intent inference and policy decisions on one component reinforce the efficiency of others, making highly reliable intent prediction and low-latency action execution essential. Although numerous approaches can model intent relationships, they encounter severe obstacles when scaling to high-dimensional action sequences and managing intensive on-board computation. We propose an Intent-Driven Framework for Autonomous Network Optimization comprising prediction and decision modules. First, implicit intent modeling is adopted to mitigate inaccuracies arising from ambiguous user expressions. For prediction, we introduce Hyperdimensional Transformer (HDT), which embeds data into a Hyperdimensional space via Hyperdimensional vector encoding and replaces standard matrix and attention operations with symbolic Hyperdimensional computations. For decision-making, where AAV must respond to user intent while planning trajectories, we design Double Actions based Multi-Agent Proximal Policy Optimization (DA-MAPPO). Building upon MAPPO, it samples actions through two independently parameterized networks and cascades the user-intent network into the trajectory network to maintain action dependencies. We evaluate our framework on a real IoT action dataset with authentic wireless data. Experimental results demonstrate that HDT and DA-MAPPO achieve superior performance across diverse scenarios.

Figures (7)

• Figure 1: The framework of Intent-based Networking.
• Figure 2: The framework of Hyperdimensional Transformer.
• Figure 3: The framework of Double Actions based Multi Agent Proximal Policy Optimization.
• Figure 4: The training trends of intent prediction algorithms.
• Figure 5: The training trends of network optimization algorithms.