A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks
In this paper, we study the usage of Convolutional Neural Network (CNN) estimators for the task of Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Channel Estimation (CE). Specifically, the CNN estimators interpolate the channel values of reference signals for estimating the channel of the full OFDM resource element (RE) matrix. We have designed a 2D CNN architecture based on U-net, and a 3D CNN architecture for handling spatial correlation. We investigate the performance of various CNN architectures fora diverse data set generated according to the 5G NR standard and in particular, we investigate the influence of spatial correlation, Doppler, and reference signal resource allocation. The CE CNN estimators are then integrated with MIMO detection algorithms for testing their influence on the system level Bit Error Rate(BER) performance.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems
Channel estimation is of crucial importance in massive multiple-input multiple-output (m-MIMO) visible light communication (VLC) systems. In order to tackle this problem, a fast and flexible denoising convolutional neura…
DenoisingImage DenoisingPruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems
With the large number of antennas and subcarriers the overhead due to pilot transmission for channel estimation can be prohibitive in wideband massive multiple-input multiple-output (MIMO) systems. This can degrade the o…
Blind Channel Estimation for Massive MIMO: A Deep Learning Assisted Approach
Large scale multiple-input multiple-output (MIMO) or Massive MIMO is one of the pivotal technologies for future wireless networks. However, the performance of massive MIMO systems heavily relies on accurate channel estim…
DenoisingDeep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches
Future wireless multiple-input multiple-output (MIMO) systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands to meet the growing demands for high data rates. MIMO link establishment typically …
Deep LearningCNN based Channel Estimation using NOMA for mmWave Massive MIMO System
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 scenari…