Digital receivers are required to recover the transmitted symbols from their observed
channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple …

This letter proposes a deep neural network Transformer-based sliding symbol detection for
integrated sensing and communications (ISAC) in Single-Input-Multiple-Output Orthogonal …

In this paper, we present a learning scheme for Joint Source-Channel Coding (JSCC) over
analog independent additive noise channels. We formulate the learning problem by …

We present a data-driven framework to symbol detection design that combines machine
learning (ML) and model-based algorithms. The resulting data-driven receivers are most …

The need to recover high-dimensional signals from their noisy low-resolution quantized
measurements is widely encountered in communications and sensing. In this paper, we …

Many important schemes in signal processing and communications, ranging from the BCJR
algorithm to the Kalman filter, are instances of factor graph methods. This family of …

In this work, we consider the problem of one-bit deep compressive sensing from both a
system design and a signal recovery perspective. In particular, we develop hybrid model …

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 …

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 …

Wireless communications is often subject to channel fading. Various statistical models have
been proposed to capture the inherent randomness in fading, and conventional model …