1 citations · 1 across the 13 of their papers we have counts for
13 papers
Multi-Access Speculative Inference: Uplink or Downlink?
Chang Cai, Kaibin Huang
Multi-access speculative inference (Multi-SPIN) extends SPIN to multi-device edge networks to accelerate cooperative token generation. It allows on-device small language models (SL…
MORES: Mobile Reasoning-as-a-Service via Distributed LLM Inference-Time Scaling
Guanchen Liu, Hongyang Du, Kaibin Huang
Inference-time scaling has emerged as an effective approach for enhancing the capabilities of Large Language Models (LLMs), addressing the growing demand for stronger reasoning wit…
Multi-SPIN: Multi-Access Speculative Inference for Cooperative Token Generation at the Edge
Haotian Zheng, Zhanwei Wang, Mingyao Cui +3
Speculative inference (SPIN) was originally developed as an efficient architecture to accelerate Large Language Models (LLMs). In this work, we propose its distributed deployment t…
NetMCP: Network-Aware Model Context Protocol Platform for LLM Capability Extension
Enhan Li, Hongyang Du, Kaibin Huang
Large Language Models (LLMs) remain static in functionality after training, and extending their capabilities requires integration with external data, computation, and services. The…
Experience Scaling: Post-Deployment Evolution For Large Language Models
Xingkun Yin, Kaibin Huang, Dong In Kim +1
Scaling model size, training data, and compute power have driven advances in large language models (LLMs), but these approaches are reaching saturation as human-generated text is e…
Ubiquitous Intelligence Via Wireless Network-Driven LLMs Evolution
Xingkun Yin, Feiran You, Hongyang Du +1
We introduce ubiquitous intelligence as a paradigm where Large Language Models (LLMs) evolve within wireless network-driven ecosystems. Unlike static model deployments, this approa…
LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction
Shiyuan Zhang, Tong Li, Zhu Xiao +2
Service-level mobile traffic prediction for individual users is essential for network efficiency and quality of service enhancement. However, current prediction methods are limited…
Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning
Xudong Wang, Hongyang Du, Lei Feng +1
The growing demand for low-latency computing in 6G is driving the use of UAV-based low-altitude mobile edge computing (MEC) systems. However, limited spectrum often leads to severe…
DRESS: Diffusion Reasoning-based Reward Shaping Scheme For Intelligent Networks
Feiran You, Hongyang Du, Xiangwang Hou +2
Network optimization remains fundamental in wireless communications, with Artificial Intelligence (AI)-based solutions gaining widespread adoption. As Sixth-Generation (6G) communi…
Optimal Expert Selection for Distributed Mixture-of-Experts at the Wireless Edge
Shengling Qin, Hai Wu, Hongyang Du +1
The emergence of distributed Mixture-of-Experts (DMoE) systems, which deploy expert models at edge nodes, offers a pathway to achieving connected intelligence in sixth-generation (…
Multi-objective Low-altitude IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning
Wenwen Xie, Geng Sun, Chuang Zhang +3
Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless netwo…
JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services
Feiran You, Hongyang Du, Kaibin Huang +1
Large Language Models (LLMs) are increasingly integrated into mobile services over wireless networks to support complex user requests. This trend has led to longer prompts, which i…
JPPO: Joint Power and Prompt Optimization for Accelerated Large Language Model Services
Feiran You, Hongyang Du, Kaibin Huang +1
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, leading to their increasing deployment in wireless networks for a wide variety of user serv…