We consider a dynamic multichannel access problem, where multiple correlated channels
follow an unknown joint Markov model and users select the channel to transmit data. The …

We consider the problem of distributed online learning with multiple players in multiarmed
bandit (MAB) models. Each player can pick among multiple arms. When a player picks an …

There have been tremendous improvements in deep learning and reinforcement learning
techniques. Automating learning and intelligence to the full extent remains a challenge. The …

We consider the problem of transmitting at the optimal rate over a rapidly-varying wireless
channel with unknown statistics when the feedback about channel quality is very limited …

A fundamental theoretical problem in opportunistic spectrum access is the following: a single
secondary user must choose a channel to sense and access at each time, with the …

We study defending strategies against adversarial attacks on Combinatorial Multi-Armed
Bandits (CMAB) algorithms. CMAB is an effective sequence decision making tool that has …

(Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University
of Posts and Telecommunications, Chongqing 400065, China) Abstract: In the opportunistic …

In this paper, we study the quality-of-service (QoS) support for realtime traffic in cognitive
radio (CR) networks when spectrum availability and quality is not known a priori. A resource …

We study the file transfer problem in opportunistic spectrum access (OSA) model, which has
been widely studied in throughput-oriented applications for max-throughput strategies and …

We consider the following learning problem motivated by opportunistic spectrum access in
cognitive radio networks. There are N independent Gilbert-Elliott channels with possibly non …