With the vision to transform the current wireless network into a cyber-physical intelligent
platform capable of supporting bandwidth-hungry and latency-constrained applications, both …

This paper develops novel deep learning-based architectures and design methodologies for
an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one …

In this letter, deep learning is applied to estimate the uplink channels for mixed analog-to-
digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a …

This paper presents a low-complexity near-maximum-likelihood-detection (near-MLD)
algorithm called one-bit sphere decoding for an uplink massive multiple-input multiple …

This letter considers uplink massive MIMO systems with 1-bit analog-to-digital converters
(ADCs) and develops a deep-learning based channel estimation framework. In this …

Federated learning is a privacy-preserving approach to train a global model at a central
server by collaborating with wireless devices, each with its own local training data set. In this …

This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly
detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) …

Non-orthogonal communications are expected to play a key role in future wireless systems.
In downlink transmissions, the data symbols are broadcast from a base station to different …

Antenna selection is a promising technology to achieve a good balance between high
transmission capacity and low hardware complexity for massive multiple-input multiple …

The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for
reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) …