2 citations · 5 across the 8 of their papers we have counts for
8 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…
Large Language Models for Wireless Communications: From Adaptation to Autonomy
Le Liang, Hao Ye, Yucheng Sheng +4
The emergence of large language models (LLMs) has revolutionized artificial intelligence, offering unprecedented capabilities in reasoning, generalization, and zero-shot learning.…
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…
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…
Learn to Adapt to New Environment from Past Experience and Few Pilot
Ouya Wang, Jiabao Gao, Geoffrey Ye Li
In recent years, deep learning has been widely applied in communications and achieved remarkable performance improvement. Most of the existing works are based on data-driven deep l…
Accretionary Learning with Deep Neural Networks
Xinyu Wei, Biing-Hwang Fred Juang, Ouya Wang +2
One of the fundamental limitations of Deep Neural Networks (DNN) is its inability to acquire and accumulate new cognitive capabilities. When some new data appears, such as new obje…