198 citations · 311 across the 15 of their papers we have counts for
16 papers
Towards CSI-Native Foundation Models: A Channel-Adaptive Roadmap for 6G
Chenyu Zhang, Xinchen Lyu, Chenshan Ren +2
Wireless foundation models offer a path toward reusable channel state information (CSI) intelligence for sixth-generation (6G) systems. However, existing generic-backbone adaptatio…
Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models
Chenyu Zhang, Xinchen Lyu, Chenshan Ren +4
Wireless foundation models (WFMs) have emerged as a promising paradigm for unified channel state information (CSI) acquisition across diverse tasks in sixth-generation (6G) network…
HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models
Chenyu Zhang, Xinchen Lyu, Chenshan Ren +3
Wireless foundation models promise transformative capabilities for channel state information (CSI) processing across diverse 6G network applications, yet face fundamental challenge…
Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows
Yunhao Hu, Xinchen Lyu, Chenshan Ren +3
The escalating complexity of sixth-generation (6G) networks demands unprecedented levels of autonomy beyond the capabilities of traditional optimization-based and current AI-based…
FourierCompress: Layer-Aware Spectral Activation Compression for Efficient and Accurate Collaborative LLM Inference
Jian Ma, Xinchen Lyu, Jun Jiang +4
Collaborative large language model (LLM) inference enables real-time, privacy-preserving AI services on resource-constrained edge devices by partitioning computational workloads be…
SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices
Jian Ma, Xinchen Lyu, Jun Jiang +3
Fine-tuning large language models (LLMs) on private, on-device data can empower tailored personalized AI agents. However, fine-tuning LLMs on resource-constrained edge devices face…
A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond
Yipeng Li, Xinchen Lyu, Zhenyu Liu
We aim to provide a unified convergence analysis for permutation-based Stochastic Gradient Descent (SGD), where data examples are permuted before each epoch. By examining the relat…
Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities
Qimei Cui, Xiaohu You, Ni Wei +22
With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks ha…
Sharp Bounds for Sequential Federated Learning on Heterogeneous Data
Yipeng Li, Xinchen Lyu
There are two paradigms in Federated Learning (FL): parallel FL (PFL), where models are trained in a parallel manner across clients, and sequential FL (SFL), where models are train…
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size
Junlin Liu, Xinchen Lyu
Adversarial examples are one critical security threat to various visual applications, where injected human-imperceptible perturbations can confuse the output.Generating transferabl…
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
Yipeng Li, Xinchen Lyu
There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: (i) parallel FL (PFL), where clients train models in a parallel manner; a…
Boosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications
Zeju Li, Xinghan Liu, Guoshun Nan +4
End-to-end semantic communication (ESC) system is able to improve communication efficiency by only transmitting the semantics of the input rather than raw bits. Although promising,…
Convergence Analysis of Sequential Split Learning on Heterogeneous Data
Yipeng Li, Xinchen Lyu
Federated Learning (FL) and Split Learning (SL) are two popular paradigms of distributed machine learning. By offloading the computation-intensive portions to the server, SL is pro…
Similarity-based Label Inference Attack against Training and Inference of Split Learning
Junlin Liu, Xinchen Lyu, Qimei Cui +1
Split learning is a promising paradigm for privacy-preserving distributed learning. The learning model can be cut into multiple portions to be collaboratively trained at the partic…
Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge
Meihui Hua, Hui Tian, Xinchen Lyu +2
By exploiting the superiority of non-orthogonal multiple access (NOMA), NOMA-aided mobile edge computing (MEC) can provide scalable and low-latency computing services for the Inter…
Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things
Hao Wu, Xinchen Lyu, Hui Tian
A spurt of progress in wireless power transfer (WPT) and mobile edge computing (MEC) provides a promising approach for Industrial Internet of Things (IIoT) to enhance the quality a…