Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

In modern electronic warfare, it is becoming very important to develop intelligent and
adaptive radar anti-jamming methods since jammers can now launch increasingly complex …

In this paper, reinforcement learning (RL) for network slicing is considered in NextG radio
access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the …

In this paper, reinforcement learning (RL) for network slicing is considered in next
generation (NextG) radio access networks, where the base station (gNodeB) allocates …

Semantic communications (SEMCOM) is a novel communication model that exploits neural
networks or deep learning techniques to convey the semantics of the data and contextual …

We investigate the problem of dynamic spectrum anti-jamming access against intelligent
jammer using game theory and opponent modeling. Previous work has formulated the …

Deep reinforcement learning (DRL) has recently been used to perform efficient resource
allocation in wireless communications. In this paper, the vulnerabilities of such DRL agents …

The increasingly intelligence of electronic warfare jamming techniques and the threat of
main-lobe jamming have significantly deteriorated radar detection capabilities. As a result, it …

Dynamic Spectrum Access (DSA) is a technology proposed to address issues such as
spectrum scarcity, inflexible spectrum management, and spectrum waste in wireless …

In this article, we propose a model-based reinforcement learning (RL) algorithm for wireless
channel access. The model-based RL is a relatively new RL paradigm that integrates the …