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Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal freque...Show More

Abstract:

This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Our target is to maximize the throughput and fulfill a packet error rate constraint. We consider practical impairments of MIMO-OFDM receiver, such as imperfect timing synchronization, carrier frequency offset correction, and channel estimation. We treat the estimated channel state information and the noise standard deviation as input features to the DCNN. 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.
Published in: IEEE Wireless Communications Letters ( Volume: 8, Issue: 3, June 2019)
Page(s): 665 - 668
Date of Publication: 18 November 2018

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