activity
20182026
most citedFederated Linear Contextual Bandits

15 citations · 52 across the 19 of their papers we have counts for

collaborators
Showing cs.ITShow all

7 papers · 1 filter

cs.IT2025

Decision Feedback In-Context Learning for Wireless Symbol Detection

Li Fan, Wei Shen, Jing Yang +1

Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts without model update. Transforme…

cs.IT2025

Average Reward Reinforcement Learning for Wireless Radio Resource Management

Kun Yang, Jing Yang, Cong Shen

In this paper, we address a crucial but often overlooked issue in applying reinforcement learning (RL) to radio resource management (RRM) in wireless communications: the mismatch b…

cs.IT2024

Decision Feedback In-Context Symbol Detection over Block-Fading Channels

Li Fan, Jing Yang, Cong Shen

Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts \textit{without model update}. T…

cs.IT2023

Advancing RAN Slicing with Offline Reinforcement Learning

Kun Yang, Shu-ping Yeh, Menglei Zhang +3

Dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing. This technology, cr…

cs.IT2023

Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets

Kun Yang, Cong Shen, Jing Yang +2

The recent development of reinforcement learning (RL) has boosted the adoption of online RL for wireless radio resource management (RRM). However, online RL algorithms require dire…

cs.IT2022

Random Orthogonalization for Federated Learning in Massive MIMO Systems

Xizixiang Wei, Cong Shen, Jing Yang +1

We propose a novel communication design, termed random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The ke…