84 citations · 150 across the 17 of their papers we have counts for
6 papers · 1 filter
Attention-Aided MMSE with Ridge Denoising: How to Train under Noisy Channel Samples
TaeJun Ha, Hyeji Kim, Jeonghun Park
Deep neural channel estimators are typically trained with clean channel state information (CSI), which is unavailable in practical orthogonal frequency-division multiplexing (OFDM)…
Enhancing K-user Interference Alignment for Discrete Constellations via Learning
Rajesh Mishra, Syed Jafar, Sriram Vishwanath +1
In this paper, we consider a K-user interference channel where interference among the users is neither too strong nor too weak, a scenario that is relatively underexplored in the l…
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator
Mohamed S. Abdelfattah, Łukasz Dudziak, Thomas Chau +3
Neural architecture search (NAS) has been very successful at outperforming human-designed convolutional neural networks (CNN) in accuracy, and when hardware information is present,…
DeepTurbo: Deep Turbo Decoder
Yihan Jiang, Hyeji Kim, Himanshu Asnani +3
Present-day communication systems routinely use codes that approach the channel capacity when coupled with a computationally efficient decoder. However, the decoder is typically de…
MIND: Model Independent Neural Decoder
Yihan Jiang, Hyeji Kim, Himanshu Asnani +1
Standard decoding approaches rely on model-based channel estimation methods to compensate for varying channel effects, which degrade in performance whenever there is a model mismat…
LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks
Yihan Jiang, Hyeji Kim, Himanshu Asnani +3
Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards. However, a sharp characterization of the performance of traditional…