In this paper, we propose a neural network-based precoder selection method for multiple
antenna systems that are equipped with maximum likelihood detectors. We train a fully …

Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output
(MIMO) systems, it has become computationally prohibitive in massive MIMO systems …

In this paper, we propose a deep learning based sphere decoding (SD) scheme to reduce
the detection complexity for the multiple-input multiple-output (MIMO) communication …

Sphere decoder (SD) has variable complexity, and the traditional fixed-complexity sphere
decoder (FSD) is not applicable for rank-deficient (NT> NR) multiple input multiple output …

Tree-based demappers for multiple-input multiple-output (MIMO) detection such as the
sphere decoder can achieve near-optimal performance but incur high computational cost …

In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large
multiple-input-multiple-output systems, namely, deep path prediction-based sphere …

We introduce a low complexity iterative soft output sphere decoding algorithm for coded
transmissions over multiple antenna channels. Before the iterative detection and decoding …

This study uses a supervised machine learning-based approach to analyze a low-
complexity precoder design to maximize the minimal Signal-to-Interference plus Noise Ratio …

Sphere Decoding provides a computationally efficient method of solving the maximum-
likelihood detection problem in multiple-input multiple-output communication systems …

In this paper, we propose a deep learning based breadth-first sphere decoding (SD) scheme
to reduce the detection complexity for multiple-input multiple-output (MIMO) communication …