Low-delay analog joint source-channel coding with deep learning
Z Xuan, K Narayanan - IEEE Transactions on Communications, 2022 - ieeexplore.ieee.org
We consider the design of low-delay joint source-channel coding (JSCC) schemes for the
transmission of discrete-time analog sources over noisy channels based on deep neural …
transmission of discrete-time analog sources over noisy channels based on deep neural …
Deep channel prediction: A DNN framework for receiver design in time-varying fading channels
SR Mattu, LN Theagarajan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In time-varying fading channels, channel coefficients are estimated using pilot symbols that
are transmitted every coherence interval. For channels with high Doppler spread, the rapid …
are transmitted every coherence interval. For channels with high Doppler spread, the rapid …
A review of applications of supervised learning to future networks
S Bilson, A Thompson - 2022 - eprintspublications.npl.co.uk
NPL REPORT MS40 Page 1 NPL REPORT MS 40 A REVIEW OF APPLICATIONS OF
SUPERVISED LEARNING TO FUTURE NETWORKS BILSON, S AND THOMPSON, A …
SUPERVISED LEARNING TO FUTURE NETWORKS BILSON, S AND THOMPSON, A …
Interpreting deep-learned error-correcting codes
Deep learning has been used recently to learn error-correcting encoders and decoders
which may improve upon previously known codes in certain regimes. The encoders and …
which may improve upon previously known codes in certain regimes. The encoders and …
Neural belief propagation auto-encoder for linear block code design
G Larue, LA Dufrene, Q Lampin… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The growing number of Internet of Thing (IoT) and Ultra-Reliable Low Latency
Communications (URLCC) use cases in next generation communication networks calls for …
Communications (URLCC) use cases in next generation communication networks calls for …
TinyTurbo: Efficient turbo decoders on edge
In this paper, we introduce a neural-augmented decoder for Turbo codes called
TINYTURBO. TINYTURBO has complexity comparable to the classical max-log-MAP …
TINYTURBO. TINYTURBO has complexity comparable to the classical max-log-MAP …
Inventing codes for channels with active feedback via deep learning
Designing reliable codes for channels with feedback, which has significant theoretical and
practical importance, is one of the long-standing open problems in coding theory. While …
practical importance, is one of the long-standing open problems in coding theory. While …
List autoencoder: Towards deep learning based reliable transmission over noisy channels
In this paper, we present list autoencoder (listAE) to mimic list decoding used in classical
coding theory. With listAE, the decoder network outputs a list of decoded message word …
coding theory. With listAE, the decoder network outputs a list of decoded message word …
Decoding quadratic residue codes using deep neural networks
In this paper, a low-complexity decoder based on a neural network is proposed to decode
binary quadratic residue (QR) codes. The proposed decoder is based on the neural min …
binary quadratic residue (QR) codes. The proposed decoder is based on the neural min …
Deep learning based near-orthogonal superposition code for short message transmission
Massive machine type communication (mMTC) has attracted new coding schemes
optimized for reliable short message transmission. In this paper, a novel deep learning …
optimized for reliable short message transmission. In this paper, a novel deep learning …