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10.1016/J.PHYCOM.2019.100771

In this paper, by exploiting the highly correlated spatial structure of the obtained array response vectors and sparsity of the multipath signal components of the massive MIMO-OFDM channels, a modified spatial basis expansion model (modified-SBEM) is introduced.

Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels

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10.1109/ACCESS.2019.2893619

First, the mmWave MIMO-OFDM channel is categorized and represented in four domains: the array-frequency, array-time, angle-frequency, as well as angle-time.

Channel Prediction for Millimeter Wave MIMO-OFDM Communications in Rapidly Time-Varying Frequency-Selective Fading Channels

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10.1109/LWC.2018.2881978

The main advantages of the proposed approach are: 1) it learns the characteristics of the MIMO-OFDM channel properly and predicts the suitable modulation and coding scheme and 2) it does not need complex features selection.

Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems

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10.1109/TWC.2019.2917905

By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing-based channel estimators under the STCS framework.

Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

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10.1109/GLOBECOM38437.2019.9013443

In this paper we analyze the number of degrees of freedom needed to accurately capture and model wideband MIMO-OFDM channels.

Degrees of Freedom of Ray-Based Models for mm-Wave Wideband MIMO-OFDM

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10.1109/LWC.2018.2873339

Here, we proposed to use the joint sparsity of mMIMO-OFDM channels using stage-wise forward backward pursuit (StFBP) algorithm.

Massive MIMO-OFDM Channel Estimation via Distributed Compressed Sensing

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10.1186/s13634-019-0627-3

As our solution, based on the improved OFDM frame structure, a novel massive MIMO-OFDM channel estimation method under CFO environment is proposed.

Sparse massive MIMO-OFDM channel estimation based on compressed sensing over frequency offset environment

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Mimo Ofdm Channel - In this paper, by exploiting the highly correlated spatial structure of the obtained array response vectors and sparsity of the multipath signal components of the massive MIMO-OFDM channels, a modified spatial basis expansion model (modified-SBEM) is introduced. [1] First, the mmWave MIMO-OFDM channel is categorized and represented in four domains: the array-frequency, array-time, angle-frequency, as well as angle-time. [2] The main advantages of the proposed approach are: 1) it learns the characteristics of the MIMO-OFDM channel properly and predicts the suitable modulation and coding scheme and 2) it does not need complex features selection. [3] By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing-based channel estimators under the STCS framework. [4] In this paper we analyze the number of degrees of freedom needed to accurately capture and model wideband MIMO-OFDM channels. [5] Here, we proposed to use the joint sparsity of mMIMO-OFDM channels using stage-wise forward backward pursuit (StFBP) algorithm. [6] As our solution, based on the improved OFDM frame structure, a novel massive MIMO-OFDM channel estimation method under CFO environment is proposed. [7]