Machine learning (ML) can be used in various ways to improve multi-user multiple-input
multiple-output (MU-MIMO) receive processing. Typical approaches either augment a single …

Machine learning (ML) starts to be widely used to enhance the performance of multi-user
multiple-input multiple-output (MU-MIMO) receivers. However, it is still unclear if such …

In orthogonal frequency division multiplexing (OFDM)-based wireless communication
systems, the bit error rate (BER) performance is heavily dependent on the accuracy of …

This paper studies the application of tools from Artificial Intelligence and Machine Learning
(AI/ML) for the adaptive configuration of reference signals (pilots). Specifically, we propose a …

This paper focuses on studying the deep learning (DL) application of neural networks to
solve the reception of single-antenna OFDM signals. Specifically, in multi-antenna …

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 …

In recent years, deep learning (DL) has seen extensive applications in multiple-input
multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In …

To apply a suitable precoder in a downlink (DL) MU-MIMO system, it is imperative to use a
reference signal for estimating the DL channel quality at the UE which is then reported back …

How to reduce the pilot overhead required for channel estimation? How to deal with the
channel dynamic changes and error propagation in channel prediction? To jointly address …

This work proposes symbol and block level adaptive channel estimation schemes, based on
the least mean squares (LMS) and block-LMS (BLMS) approaches, respectively, for …