In this paper, we introduce a structure-based neural network architecture, namely RC-Struct,
for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO …

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 …

The echo state network (ESN) is a recently developed machine-learning paradigm whose
processing capabilities rely on the dynamical behavior of recurrent neural networks. Its …

In this paper, we consider a real-time deep learning-based symbol detection approach for
MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the …

The limited over-the-air (OTA) pilot symbols in multiple-input-multiple-output orthogonal-
frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for …

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 …

The increased flexibility and density of spectrum access in 5G New Radio (NR) has made
jamming detection and classification a critical research area. To detect coexisting jamming …

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 …

This paper introduces a structure-based neural network architecture, namely RC-Struct, for
MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO …

In modern communication systems, it's important to detect and use available radio
frequencies effectively. However, current methods face challenges with complexity and …