AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications
Yulin Shao, Emre Ozfatura, Alberto Perotti, Branislav Popovic, Deniz, Gunduz

TL;DR
AttentionCode introduces a novel deep learning-based feedback coding scheme that significantly enhances ultra-reliable short-packet communication performance in wireless networks, achieving error rates below 10^-7 in challenging channel conditions.
Contribution
The paper proposes AttentionCode, a new feedback coding framework utilizing deep learning with architectural innovations and training techniques, setting a new state-of-the-art in ultra-reliable short-packet communication.
Findings
Achieves BLER of 10^-7 at 0 dB SNR in AWGN channels
Outperforms existing DL-based feedback codes in reliability
Demonstrates effectiveness in fading channels
Abstract
Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep learning (DL) technologies. The underpinnings of AttentionCode are three architectural innovations: AttentionNet, input restructuring, and adaptation to fading channels, accompanied by several training methods, including large-batch training, distributed learning, look-ahead optimizer, training-test signal-to-noise ratio (SNR) mismatch, and curriculum learning. The training methods can potentially be generalized to other wireless communication applications with machine learning. Numerical experiments verify that AttentionCode…
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Taxonomy
TopicsWireless Signal Modulation Classification · Wireless Communication Security Techniques · Distributed Sensor Networks and Detection Algorithms
