Large Language Model Assisted Intent-Based Satellite-Integrated Access and Backhaul FWA for Rural Areas

2026-07-23

AI Summary

This study addresses the highly dynamic spatiotemporal communication demands in rural areas, where sparse populations and concurrent household and field activities lead to inefficient network resource utilization and poor energy efficiency. To tackle this challenge, the work proposes a novel user-intent-aware wireless access framework for rural environments, leveraging large language models (LLMs) to interpret user semantics and translate them into structured network requirements. These requirements drive the joint optimization of fixed broadband and temporary field connectivity through an integrated satellite-based Integrated Access and Backhaul (IAB) system. A two-stage Benders decomposition algorithm is employed to enable intelligent resource scheduling. Simulation results demonstrate that the proposed approach significantly enhances energy efficiency while meeting stringent rate requirements.

Abstract

Rural areas exhibit low population density and highly variable connectivity needs shaped by both household usage and field operations such as planting, harvesting, and mining. These field activities often occur in isolated locations requiring temporary connectivity, whereas rural households depend on fixed broadband. During intensive outdoor activities, household fixed networks may remain underutilized, resulting in inefficient resource use and unnecessary energy consumption. The coexistence of residential and field-based communication demands creates substantial spatial and temporal fluctuations that the current rural network cannot effectively adapt to. Limited visibility into user mobility, activity patterns, and intent makes it difficult for operators to coordinate temporary and fixed networks. To address these underexplored challenges, we propose an AI driven Intent Aware Satellite Integrated Access and Backhaul (IAB) approach to connect rural areas. In our proposal, a large language model (LLM) translates users' intents into explicit network requirements. Guided by these inferred requirements, we develop a dynamic satellite IAB based Fixed Wireless Access (FWA) network approach that jointly optimizes temporary field connectivity and fixed broadband access to maximize energy efficiency while satisfying the data rate requirement. The formulated optimization problem is solved using a two stage Benders decomposition approach. The simulation results show that our approach significantly reduces energy consumption while maximizing energy efficiency.

Problem

• rural connectivity
• spatiotemporal demand fluctuation
• intent-aware networking
• fixed wireless access
• energy efficiency

Innovation

• Large Language Model (LLM)
• Intent-Aware Networking
• Satellite Integrated Access and Backhaul (IAB)
• Fixed Wireless Access (FWA)
• Energy-Efficient Optimization