This paper proposes a deep learning-based channel estimation method for multi-cell
interference-limited massive MIMO systems, in which base stations equipped with a large …

In this letter, we propose a deep learning (DL)-based channel estimation scheme for the
massive multiple-input multiple-output (MIMO) system. Unlike existing studies, we develop …

This letter considers uplink massive MIMO systems with 1-bit analog-to-digital converters
(ADCs) and develops a deep-learning based channel estimation framework. In this …

Massive multiple-input multiple-output (mMIMO) is a critical component in upcoming 5G
wireless deployment as an enabler for high data rate communications. mMIMO is effective …

This paper considers the joint impact of non-linear hardware impairments at the base station
(BS) and user equipments (UEs) on the uplink performance of single-cell massive MIMO …

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge
training overhead caused by downlink transmission, like channel estimation, downlink …

Channel estimation is one of the key issues in practical massive multiple-input multiple-
output (MIMO) systems. Compared with conventional estimation algorithms, deep learning …

Pilot contamination posts a fundamental limit on the performance of massive multiple-input-
multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To …

Channel estimation is a challenging task, especially in a massive multiple-input multiple-
output (MIMO) system with one-bit analog-to-digital converters (ADC). Traditional deep …

In this letter, deep learning is applied to estimate the uplink channels for mixed analog-to-
digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a …