In this paper, we introduce a reservoir computing (RC) structure, namely, windowed echo
state network (WESN), for multiple-input-multiple-output orthogonal frequency-division …

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 …

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 …

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 investigate online learning-based MIMO-OFDM symbol detection strategies
focusing on a special recurrent neural network (RNN)-reservoir computing (RC). We first …

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a
fundamental transmission scheme, promises high throughput and robustness against …

This paper proposes a novel learning to learn method, called learning to learn iterative
search algorithm (LISA), for signal detection in a multi-input multi-output (MIMO) system. The …

Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM)
system is a promising technology that provides high capacity and high data rate …

Conventional reservoir computing (RC) is a shallow recurrent neural network (RNN) with
fixed high dimensional hidden dynamics and one trainable output layer. It has the nice …