… deep learning in communications systems is a DNN-based codec ie, encoder and decoder.
… Representatively, the processing time of each DNN layer is less than 1 OFDM symbol …

… applying deep learning to wireless communications. Deep learning based channel decoder
… , +1} [46] - so that every symbol has exactly the same power and the information is conveyed …

… In this letter, we introduce a deep learning approach to channel estimation and symbol
detection … Burshtein, “Learning to decode linear codes using deep learning,” in Proc. 54th Annu. …

… To estimate the transmitted symbol vector ( x ˆ ), we are … To estimate transmitted symbol vector
x , we formulate a … function for any MIMO decoder with respect to transmitted symbols x is 2 …

… application of Deep Learning (DL) in the field of telecommunications is discussed,
focusing specifically on symbol detection at the receiver. The performance of Deep Learning-based …

… By unfolding the iterative decoding progress between checking nodes (CNs) and variable
… deep learning methods and conventional normalized min-sum (CNMS) LDPC decoding …

… reduce the complexity of the decoder via deep learning [19]–[21]. Learning a code that is
as … Our interpretation of this result is that TurboAE-MOD tends to learn symbols that induce …

… Designing codes for this channel via deep learning approaches is the central focus of this …
correctly decoding the received symbols and recover b. Both the encoder and the decoder are …

… (AWGN) disrupts the received symbols and the transmit symbols are equally likely transmitted…
Moreover, the first symbol can be detected at the very end of symbol transmission since the …

… of deep learning on nonbinary codes. In this paper, we apply deep learning to the decoding
… (t = (n−k)/2), and the code is capable of successfully decoding t or fewer symbol errors. The …