Generative AI for Advanced UAV Networking

TL;DR

The paper argues that Generative AI offers a powerful alternative to traditional AI for UAV communications by learning and generating distributions that enable robust optimization under mobility and limited resources. It proposes a unified GAI framework for UAV networking, combining diffusion-model-based spectrum estimation with interactive LLM-driven problem formulation, and validates it through case studies on spectrum mapping and rate optimization. Key contributions include SEMG design, diffusion-model-based spectrum inference, and multi-level applications across physical, network, and security layers, showing improved accuracy and efficiency over baselines. The work provides practical implications for energy-aware, secure, and multimodal UAV intelligence in future space-air-ground-sea networks.

Abstract

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions.

Figures (4)

• Figure 1: The roles of UAVs in communications and networking and the comparison of DAI and GAI in solving UAV optimization problem. Due to its maneuverability and computing power, the UAV can act as the aerial base station, relay and edge computing device to solve the communications and networking problems in various scenarios. Moreover, DAI and GAI are widely used to solve optimization problems in the aforementioned scenarios, where GAI stands out due to its powerful generation and learning capabilities.
• Figure 2: The framework of the proposed SEMG. In part A, the prompt optimizer generates professional prompts based on the task of users (in our case study for constructing the objective function, the network structure and loss function of the diffusion model), and subsequently the RAG is employed to output the results. In part B, the diffusion model is used to generate the solution to the optimization problem. Specifically, in Step 1, the current state is obtained. In Step 2, the diffusion model generates the solution based on the state, i.e., the SNR estimation. In Step 3, the objective function value is computed based on the observed state and the generated solution. In Step 4, the loss of the diffusion model network is calculated based on the objective function value and the network is updated.
• Figure 3: The experiment result of spectrum estimation. Part A shows the interaction between the user and GAI agent for the purpose of constructing the objective function, the network structure and loss function of diffusion model. Part B demonstrates the generation performance of diffusion model. (a) the true SNR map. (b) the process of generating SNR estimation map by our proposed SEMG. (c) the process of generating SNR estimation map by LSTM. (d) the difference between the measurements and estimations.
• Figure 4: The impact of percentage of estimation energy on spectrum estimation difference and transmission rate.