Abstract Machine learning models always make a prediction, even when it is likely to be
inaccurate. This behavior should be avoided in many decision support applications, where …

When used in complex engineered systems, such as communication networks, artificial
intelligence (AI) models should be not only as accurate as possible, but also well calibrated …

Deep neural networks (DNNs) have made significant strides in tackling challenging tasks in
wireless systems, especially when an accurate wireless model is not available. However …

Artificial intelligence (AI) algorithms based on neural networks have been designed for
decades with the goal of maximising some measure of accuracy. This has led to two …

In the design of wireless receivers, deep neural networks (DNNs) can be combined with
traditional model-based receiver algorithms to realize modular hybrid model-based/data …

Deep learning is envisioned to facilitate the operation of wireless receivers, with emerging
architectures integrating deep neural networks (DNNs) with traditional modular receiver …

Optimal resource allocation in modern communication networks calls for the optimization of
objective functions that are only accessible via costly separate evaluations for each …

Due to the openness of wireless channels, wireless communication is vulnerable to be
eavesdropped, which results in confidential information leakage. Physical-layer security …

Deep learning (DL) based channel estimation (CE) and multiple input and multiple output
detection (MIMODet), as two separate research topics, have provided convinced evidence to …

Active learning provides guidance for the design and modeling of systems with highly
expensive sampling costs. However, existing active learning approaches suffer from cold …