AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications
AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications
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DOI:
10.1109/tcomm.2023.3280563
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发表时间:
2022-05
影响因子:
8.3
通讯作者:
Yulin Shao;Emre Ozfatura;A. Perotti;B. Popović;Deniz Gündüz
中科院分区:
文献类型:
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作者:
Yulin Shao;Emre Ozfatura;A. Perotti;B. Popović;Deniz Gündüz
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 establishes a new state of the art among all DL-based feedback codes in both additive white Gaussian noise (AWGN) channels and fading channels. In AWGN channels with noiseless feedback, for example, AttentionCode achieves a block error rate (BLER) of 10−7 when the forward channel SNR is 0 dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications.