A Parameter-Specific Retrieval and Knowledge-Guided Reasoning Framework for LLM-Based GPSR Optimization in FANETs

2026-08-06

AI Summary

This work addresses the challenge that existing GPSR-based routing protocols for flying ad hoc networks (FANETs) struggle to adaptively tune critical parameters—such as hello interval, number of multipath routes, and greedy forwarding weights—in highly dynamic environments. To overcome this limitation, the paper proposes the PMKR-GPSR framework, which innovatively integrates large language models with a knowledge-guided mechanism. Specifically, it employs parameter-specific multi-index retrieval to acquire relevant optimization experiences from historical data and constructs a constrained knowledge graph to ensure that parameter adjustments adhere to protocol semantics and feasibility constraints. Experimental results demonstrate that the proposed approach significantly improves packet delivery ratio and reduces end-to-end delay in high-mobility FANET simulations compared to baseline methods.

Abstract

Existing Greedy Perimeter Stateless Routing (GPSR)-based protocols for Flying Ad-Hoc Networks (FANETs) struggle to adapt routing parameters, such as hello interval, multi-path number, and greedy forwarding weights, under highly dynamic environments. As an emerging artificial intelligence technology, large language models (LLMs) show potential for intelligent decision-making, providing new opportunities for adaptive adjustment of GPSR parameters to improve network performance. However, applying LLMs to GPSR remains challenging due to irrelevant experience retrieval and the absence of protocol constraints. To address these issues, we propose a Parameter-Specific Multi-Index Retrieval and Knowledge-Guided Reasoning framework for adaptive GPSR optimization (PMKR-GPSR), an LLM-based framework that enables protocol-consistent routing parameter adaptation. We design a parameter-specific multi-index retrieval mechanism to provide LLMs with parameter-relevant experiences while reducing interference from irrelevant information. We further construct a knowledge-guided constraint graph to enforce that the routing parameters satisfy dependency rules and optimization constraints. Simulation results demonstrate that PMKR-GPSR achieves higher packet delivery ratio and lower end-to-end delay under high-mobility FANETs.

Problem

• GPSR
• FANETs
• LLM
• routing parameter adaptation
• dynamic environment

Innovation

• Parameter-Specific Retrieval
• Knowledge-Guided Reasoning
• LLM-Based Optimization
• GPSR
• FANETs