CNN based Channel Estimation using NOMA for mmWave Massive MIMO System
Anu T S, Tara Raveendran

TL;DR
This paper introduces a CNN-based method for accurate channel estimation in NOMA-enabled mmWave massive MIMO systems, addressing the challenge of precise channel knowledge in complex wireless environments.
Contribution
It proposes a hybrid CNN approach that improves channel estimation accuracy over traditional methods in NOMA mmWave MIMO systems.
Findings
Outperforms LS and MMSE estimates in accuracy.
Achieves estimation close to the Cramer-Rao Bound.
Enhances system performance in NOMA mmWave MIMO scenarios.
Abstract
Non-Orthogonal Multiple Access (NOMA) schemes are being actively explored to address some of the major challenges in 5th Generation (5G) Wireless communications. Channel estimation is exceptionally challenging in scenarios where NOMA schemes are integrated with millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. An accurate estimation of the channel is essential in exploiting the benefits of the pairing of the duo-NOMA and mmWave. This paper proposes a convolutional neural network (CNN) based approach to estimate the channel for NOMA based millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems built on a hybrid architecture. Initially, users are grouped into different clusters based on their channel gains and beamforming technique is performed to maximize the signal in the direction of desired cluster. A coarse estimation of the…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Antenna Design and Optimization · Indoor and Outdoor Localization Technologies
