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 the feedback from the receiver for the sixth generation (6G) communication networks and beyond. 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 0dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications for 6G.

本论文探讨了利用接收器反馈信息实现超可靠短数据包通信的新模式，提出 AttentionCode 这类深度学习反馈码。AttentionCode 是由 AttentionNet、输入数据重构和自适应衰落信道等三个创新技术与大批量、分布式学习、前瞻优化、信噪比错配训练和课程学习等多个训练方法构成的，是一类极具实用价值的机器学习无线通信方法。实验结果验证了 AttentionCode 在各类无线信道中均具有较优表现（误块率可达 $10^{-7}$），可为未来关键应用提供超高可靠短数据包的传输保障。

AttentionCode: 短数据包超可靠反馈编码