Fast Detection Based on Customized Complex Valued Convolutional Neural Network for Generalized Spatial Modulation Systems

Marseet, Akram; Elganimi, Taissir Y.

doi:10.1109/wnyipw.2019.8923057

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…The receiver symbol from the pilot build and one data build is admitted as the input of the pattern of deep learning. To make smaller the gap among the neural network output and the transmitting symbol, the model of deep learning is trained [17][18][19][20][21]. Applications are realized using 64 subcarriers and the CP of lengths 16 and 8 in the purposed OFDM system and QPSK is selected as the modulation technique.…”

Section: Y X Hmentioning

confidence: 99%

Effect of DNN Approximation for Channel Estimation and Signal Detection on OFDM Applications

Çalışır¹

2022

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This paper offers a deep learning approximation to realize channel estimation and signal detection that creates the main communication structure skeleton for the orthogonal frequency-division multiplexing (OFDM) system known as an efficient modulation type on 5G. This letter offers an application of deep learning to handle the wireless OFDM channels' end-to-end conduct. First, channel state information (CSI) is predicted explicitly that differs from existing OFDM receivers, then detected the transmitted symbols utilizing the predicted CSI. In the end, CSI is predicted by the suggested deep learning approximation indirectly and transmitted symbols are directly recovered. The structure of the designed receiver occurs of a layer of DNN and soft decisions, which resolves the issues channel estimation error, time delay, and limitation of decoding between users in classic detection techniques. In the simulation results, it is observed that the receiver has powerful stability on the power distribution of user, not only convenient for the linear channel, but also for nonlinear channel when enhancement the number of users, also detection can be well on the receiver. Generally, the efficiency of the modulation system decreases with the features of the multipath channel utilized for transmission. Channel estimation and detection of symbols utilize to reduce the impacts of the channel, which needs high computation and bandwidth conventionally. This paper is used deep neural networks (DNN) for detecting the signal, in this way much effort in detecting the channel is prevented. The proposed method saves priceless bandwidth via used CP in OFDM with a big increase in SNR.

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Section: Y X Hmentioning

confidence: 99%

Effect of DNN Approximation for Channel Estimation and Signal Detection on OFDM Applications

Çalışır¹

2022

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Channel Estimation Based on Machine Learning Paradigm for Spatial Modulation OFDM

Badi

Elganimi

Alkishriwo

et al. 2021

2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engi

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Fast Detection Based on Customized Complex Valued Convolutional Neural Network for Generalized Spatial Modulation Systems

Cited by 2 publications

References 16 publications

Effect of DNN Approximation for Channel Estimation and Signal Detection on OFDM Applications

Effect of DNN Approximation for Channel Estimation and Signal Detection on OFDM Applications

Channel Estimation Based on Machine Learning Paradigm for Spatial Modulation OFDM

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