2 citations · 5 across the 8 of their papers we have counts for
9 papers
Hierarchical Wireless Foundation Model for Multi-Task Optimization
Yangjing Wang, Ouya Wang, Shenglong Zhou +1
The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications. However, most existing studi…
Decentralized Federated Learning by Partial Message Exchange
Shan Sha, Shenglong Zhou, Xin Wang +2
Decentralized federated learning (DFL) has emerged as a transformative server-free paradigm that enables collaborative learning over large-scale heterogeneous networks. However, it…
Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems
Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li
Future wireless networks are expected to be AI-empowered, making their performance highly dependent on the quality of training datasets. However, physical-layer entities often obse…
Preconditioned Inexact Stochastic ADMM for Deep Model
Shenglong Zhou, Ouya Wang, Ziyan Luo +2
Deep learning models are usually trained with stochastic gradient descent-based algorithms, but these optimizers face inherent limitations, such as slow convergence and stringent a…
Fast Adaptation for Deep Learning-based Wireless Communications
Ouya Wang, Hengtao He, Shenglong Zhou +4
The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges h…
BADM: Batch ADMM for Deep Learning
Ouya Wang, Shenglong Zhou, Geoffrey Ye Li
Stochastic gradient descent-based algorithms are widely used for training deep neural networks but often suffer from slow convergence. To address the challenge, we leverage the fra…
Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks
Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li
Federated Reinforcement Learning (FRL) offers a promising solution to various practical challenges in resource allocation for vehicle-to-everything (V2X) networks. However, the dat…
New Environment Adaptation with Few Shots for OFDM Receiver and mmWave Beamforming
Ouya Wang, Shenglong Zhou, Geoffrey Ye Li
Few-shot learning (FSL) enables adaptation to new tasks with only limited training data. In wireless communications, channel environments can vary drastically; therefore, FSL techn…
Federated Reinforcement Learning for Resource Allocation in V2X Networks
Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li
Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or mac…