Subsampling of received wireless signals is important for relaxing hardware requirements
as well as the computational cost of signal processing algorithms that rely on the output …

We propose a deep-learning approach for the joint MIMO detection and channel decoding
problem. Conventional MIMO receivers adopt a model-based approach for MIMO detection …

Optimizing precoding with deep learning enables its real-time implementation. In addition to
the learning perfor-mance such as sum rate, training complexity is also important since …

In this paper, a novel framework is proposed to enable air-to-ground channel modeling over
millimeter wave (mmWave) frequencies in an unmanned aerial vehicle (UAV) wireless …

Constant envelope (CE) precoding design is of great interest for massive multiuser multi-
input multi-output systems because it can significantly reduce hardware cost and power …

Learning-based precoding has been shown able to be implemented in real-time, jointly
optimized with channel acquisition, and robust to imperfect channels. Yet previous works …

Effective and adaptive interference management is required in next generation wireless
communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA) …

Channel state information (CSI) plays a vital role in adaptive transmission systems, which
adapt their transmission parameters to instantaneous channel conditions. However, the CSI …

Deep learning can be used to classify waveform characteristics (eg, modulation) with
accuracy levels that are hardly attainable with traditional techniques. Recent research has …

Deep learning has achieved remarkable success in many machine learning tasks such as
image classification, speech recognition, and game playing. However, these breakthroughs …