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

arXiv: https://arxiv.org/abs/2511.18368 · HTML: https://mlsanity.com/paper/2511.18368/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 23 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [Efficient Planning in a Compact Latent Action Space](https://mlsanity.com/paper/2208.10291/) (2022)
- [Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case](https://mlsanity.com/paper/1910.07421/) (2019)
- [The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey](https://mlsanity.com/paper/2401.07872/) (2024)
- [Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks](https://mlsanity.com/paper/1807.09995/) (2018)
- [Graph Intention Network for Click-through Rate Prediction in Sponsored Search](https://mlsanity.com/paper/2103.16164/) (2021)
- [UAV Assisted Data Collection for Internet of Things: A Survey](https://mlsanity.com/paper/2211.09555/) (2022)
- [Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge](https://mlsanity.com/paper/2208.06089/) (2022)
- [A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction](https://mlsanity.com/paper/2008.08931/) (2020)
- [Network Topology Optimization via Deep Reinforcement Learning](https://mlsanity.com/paper/2204.14133/) (2022)
- [Charting an Intent Driven Network](https://mlsanity.com/paper/1604.05925/) (2016)

## Abstract (arXiv)

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.