Recent research has shown that in massive multiple-input multiple-output (MIMO) detection,
the model-driven machine learning (ML) detection algorithm with online training method can …

Model-driven machine learning for signal detection in the physical layer of mobile
communication systems combines well-known detector structures with learned parameters …

Low-complexity detectors play an essential role in massive multiple-input multiple-output
(MIMO) transmissions. In this work, we discuss the perspectives of utilizing approximate …

Recently, massive multiple-input multiple-output (MIMO) detection schemes based on
message passing detection (MPD) have attracted extensive attention due to their good …

In this paper, we present a deep learning based trainable approximate message passing
algorithm (TAMP) for signal detection in massive multiple-input multiple-output (MIMO) …

Among expectation propagation (EP)-based massive multiple-input–multiple-output (MIMO)
detection algorithms, EP with weighted Neumann-series approximation (EPA-wNSA) has …

In massive multiple‐input multiple‐output (MIMO) systems, most of the existing detection
work mainly assumes that the channel is the additive white Gaussian noise (AWGN) …

Massive multiple-input multiple-output (MIMO) is a crucial technology in fifth-generation (5G)
and beyond 5G (B5G). However, the huge number of antennas used in massive MIMO …

In this letter, an efficient massive multiple-input multiple-output (MIMO) detector is proposed
by employing a deep neural network (DNN). Specifically, we first unfold an existing iterative …

The use of massive multiple-input multiple-output (mMIMO) technology is essential for the
fifth-generation (5G) and sixth-generation (6G) networks. However, the computational …