标题： 基于卷积神经网络的超越奈奎斯特均衡技术 显示英文标题

标题： Faster-Than-Nyquist Equalization with Convolutional Neural Networks

摘要： 超奈奎斯特（FTN）信号传输旨在通过突破奈奎斯特-香农采样定理的限制来提高无线通信系统的频谱效率。首次引入科学文献50年后，无线通信已经发生了显著变化，但频谱效率仍然是一个关键挑战。为了采用FTN信号传输，接收端需要均衡符号间干扰（ISI）模式。受带跳跃连接的卷积神经网络模式识别能力的启发，我们提出了这种深度学习架构，用于FTN接收器中的ISI均衡和符号解调。我们研究了所提出模型的性能，考虑了正交相移键控调制和低密度奇偶校验编码，并将其与一组基准进行了比较，包括基于频率域均衡、基于二次规划的接收器以及基于深度神经网络的均衡方案。结果显示，我们的接收器优于所有基准，在FTN信号传输的更高频谱效率下实现了与加性白高斯噪声信道相当的误码率和更高的有效吞吐量。在压缩因子为60%且码率为3/4的情况下，所提出的模型在比特能量与噪声功率谱密度比仅为10 dB时达到了峰值有效吞吐量2.5 Mbps，而其他接收器由于强符号间干扰受到误码平台的限制。为了促进无线通信领域深度学习的可重复性，我们的代码已开源，可在参考文献提供的存储库中获取。 显示英文摘要

摘要： Faster-than-Nyquist (FTN) signaling aims at improving the spectral efficiency of wireless communication systems by exceeding the boundaries set by the Nyquist-Shannon sampling theorem. 50 years after its first introduction in the scientific literature, wireless communications have significantly changed, but spectral efficiency remains one of the key challenges. To adopt FTN signaling, inter-symbol interference (ISI) patterns need to be equalized at the receiver. Motivated by the pattern recognition capabilities of convolutional neural networks with skip connections, we propose such deep learning architecture for ISI equalization and symbol demodulation in FTN receivers. We investigate the performance of the proposed model considering quadrature phase shift keying modulation and low density parity check coding, and compare it to a set of benchmarks, including frequency-domain equalization, a quadratic-programming-based receiver, and an equalization scheme based on a deep neural network. We show that our receiver outperforms any benchmark, achieving error rates comparable to those in additive white Gaussian noise channel, and higher effective throughput, thanks to the increased spectral efficiency of FTN signaling. With a compression factor of 60% and code rate 3/4, the proposed model achieves a peak effective throughput of 2.5 Mbps at just 10dB of energy per bit over noise power spectral density ratio, with other receivers being limited by error floors due to the strong inter-symbol interference. To promote reproducibility in deep learning for wireless communications, our code is open source at the repository provided in the references.