Since the start of 5G work in 3GPP in early 2016, tremendous progress has been made in
both standardization and commercial deployments. 3GPP is now entering the second phase …

Reservoir computing (RC) is a novel computational framework derived from recurrent neural
networks (RNN). It can reduce the training complexity of RNN and is suitable for time-series …

Recently, there have been renewed interests in applying machine learning (ML) techniques
to wireless systems. Nevertheless, ML-based approaches often require a large amount of …

Vehicular communications are an important focus of studies for 5G applications and beyond.
However, in a scenario with doubly-selective and highly variable channel characteristics …

In this paper, we derive a theoretical upper bound on the generalization error of reservoir
computing (RC), a special category of recurrent neural networks (RNNs). The specific RC …

Quaternion neural networks (QNNs) form a class of neural networks constructed with
quaternion numbers. They are suitable for processing 3-D features with fewer trainable free …

In this paper, we develop a learning-based symbol detection algorithm for massive MIMO-
OFDM systems. To exploit the structure information inherited in the received signals from …

One major challenge for semantic communication networks is to deploy the network in
practice where channel environments change dynamically. When adjusting the network …

In this paper, we develop a learning-based symbol detection algorithm for massive MIMO
systems. To exploit the structural information inherited in the received signals from massive …

Due to the dependability, spatial multiplexing and increased spatial diversity gain,
Orthogonal frequency division multiplexing (OFDM) and multiple input, multiple output …