AI 中文总结
针对农村地区通信需求时空波动及资源利用低效问题，提出基于大语言模型辅助的意图感知卫星集成接入与回传方法，将用户意图转化为网络需求，开发动态卫星IAB的FWA网络方法，最大化能源效率，仿真验证方法有效。
AI 中文摘要
农村地区人口密度低，家庭使用和田间作业（如种植、收割和采矿）对连接需求差异大。这些田间活动常需临时连接，而农村家庭依赖固定宽带。户外活动时，家庭固定网络可能未充分利用，导致资源利用低效和不必要能耗。当前农村网络难以适应住宅和田间通信需求的时空波动。为应对这些挑战，我们提出一种人工智能驱动的意图感知卫星集成接入与回传（IAB）方法来连接农村地区。大语言模型将用户意图转化为明确的网络需求，在此指导下，开发动态卫星IAB的固定无线接入（FWA）网络方法，联合优化临时田间连接和固定宽带接入，以在满足数据速率要求的同时最大化能源效率。通过两阶段Benders分解方法解决优化问题。仿真结果表明，我们的方法显著降低能耗并最大化能源效率。
英文摘要
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.
