# A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks

arXiv: https://arxiv.org/abs/2309.01426 · HTML: https://mlsanity.com/paper/2309.01426/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2023.

## Built on by (most cited first, 12 of 12)

- [Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments](https://mlsanity.com/paper/2404.14140/) (2024)
- [A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication](https://mlsanity.com/paper/2310.17705/) (2023)
- [Lightweight Diffusion Models for Resource-Constrained Semantic Communication](https://mlsanity.com/paper/2410.02491/) (2024)
- [Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks](https://mlsanity.com/paper/2405.02972/) (2024)
- [Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness](https://mlsanity.com/paper/2405.05930/) (2024)
- [Is FISHER All You Need in The Multi-AUV Underwater Target Tracking Task?](https://mlsanity.com/paper/2412.03959/) (2024)
- [A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0](https://mlsanity.com/paper/2311.13731/) (2023)
- [Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning](https://mlsanity.com/paper/2408.02047/) (2024)
- [Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization](https://mlsanity.com/paper/2512.23493/) (2025)
- [The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles](https://mlsanity.com/paper/2409.15750/) (2024)
- [Content Accuracy and Quality Aware Resource Allocation Based on LP-Guided DRL for ISAC-Driven AIGC Networks](https://mlsanity.com/paper/2508.12079/) (2025)
- [AdaptFly: Prompt-Guided Adaptation of Foundation Models for Low-Altitude UAV Networks](https://mlsanity.com/paper/2511.11720/) (2025)

## Abstract (arXiv)

With the significant advancements in artificial intelligence (AI) technologies and powerful computational capabilities, generative AI (GAI) has become a pivotal digital content generation technique for offering superior digital services. However, directing GAI towards desired outputs still suffer the inherent instability of the AI model. In this paper, we design a novel framework that utilizes wireless perception to guide GAI (WiPe-GAI) for providing digital content generation service, i.e., AI-generated content (AIGC), in resource-constrained mobile edge networks. Specifically, we first propose a new sequential multi-scale perception (SMSP) algorithm to predict user skeleton based on the channel state information (CSI) extracted from wireless signals. This prediction then guides GAI to provide users with AIGC, such as virtual character generation. To ensure the efficient operation of the proposed framework in resource constrained networks, we further design a pricing-based incentive mechanism and introduce a diffusion model based approach to generate an optimal pricing strategy for the service provisioning. The strategy maximizes the user's utility while enhancing the participation of the virtual service provider (VSP) in AIGC provision. The experimental results demonstrate the effectiveness of the designed framework in terms of skeleton prediction and optimal pricing strategy generation comparing with other existing solutions.