Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the
potential of challenging large-scale problems in conventional massive multiple-input …

In the forthcoming 6G wireless networks, the explosive growth in the number of human-type
devices and machine-type devices will result in the network densification including both end …

In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency
division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi …

In this paper, we propose an encoder-decoder neural architecture (called Channelformer) to
achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) …

Two of the main principles underlying the life cycle of an artificial intelligence (AI) module in
communication networks are adaptation and monitoring. Adaptation refers to the need to …

Bayesian modelling of the channel distribution is a crucial step before channel recovery
specially in the underdetermined scenario in multiple input multiple output (MIMO) antenna …

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 …

Channel estimation in hybrid massive multiple input multiple output (MIMO) orthogonal
frequency division multiplexing (OFDM) systems is challenging as only low-dimensional …

In this paper we introduce StructNet-CE, a novel real-time online learning framework for
MIMO-OFDM channel estimation, which only utilizes over-the-air (OTA) pilot symbols for …

Over-the-Air aggregation (OTA) is a promising technology for Internet-of-Things (IoT)
applications, but it can be vulnerable to burst interference from co-channel non-cooperative …