This paper presents a dynamic partially connected (DPC) structure-based convolutional
neural network (CNN) hybrid precoding with multi-user optimization algorithm. In the …

This letter presents a two-stage (Ts) hybrid precoding algorithm based on convolutional
neural network (CNN) with minimum hybrid precoding residual, named as Ts-CNN …

To reduce the computational complexity and improve the achievable rate of hybrid
precoding, we propose a deep constrained and synchronous training framework, which …

Hybrid precoding is an extremely attractive technology in mmWave massive multiple-input
multiple-output (MIMO) systems. Especially the partially-connected structure has attracted …

Dual-layer iterative algorithms are generally required when solving resource allocation
problems in wireless communication systems. Specifically, the spectrum efficiency …

The massive multiple-input multiple-output (MIMO) is an emerging solution for advanced
wireless communication due to the accomplishment of high data rate and system throughput …

Large antenna array systems are favored in next-generation wireless communications, as it
can offer multiplexing and array gains that enhance the system sum-rate. However, the large …

This letter presents a joint convolutional neural network (JCNN)-based double dimensional
(DD) hybrid precoding algorithm for adaptive fully-connected subarray structure with two …

We propose a deep learning-based method that uses spatial and temporal information
extracted from the sub-6GHz band to predict/track beams in the millimeter-wave (mmWave) …

In the millimeter wave massive multiple-input multiple-output (MIMO) system, hybrid
precoding is regarded as an effective solution to reduce hardware complexity and energy …