Tootfinder

Large Language Model-Empowered Channel Prediction and Predictive Beamforming for LEO Satellite Communications
Zhixiong Chen, Hyundong Shin, Arumugam Nallanathan, Jonathon Chambers
https://arxiv.org/abs/2510.10561

Large Language Model-Empowered Channel Prediction and Predictive Beamforming for LEO Satellite Communications
Accurate channel prediction and effective beamforming are essential for low Earth orbit (LEO) satellite communications to enhance system capacity and enable high-speed connectivity. Most existing channel prediction and predictive beamforming methods are limited by model generalization capabilities and struggle to adapt to time-varying wireless propagation environments. Inspired by the remarkable generalization and reasoning capabilities of large language models (LLMs), this work proposes an LLM…

Efficient LLM Inference over Heterogeneous Edge Networks with Speculative Decoding
Bingjie Zhu, Zhixiong Chen, Liqiang Zhao, Hyundong Shin, Arumugam Nallanathan
https://arxiv.org/abs/2510.11331

Efficient LLM Inference over Heterogeneous Edge Networks with Speculative Decoding
Large language model (LLM) inference at the network edge is a promising serving paradigm that leverages distributed edge resources to run inference near users and enhance privacy. Existing edge-based LLM inference systems typically adopt autoregressive decoding (AD), which only generates one token per forward pass. This iterative process, compounded by the limited computational resources of edge nodes, results in high serving latency and constrains the system's ability to support multiple users…