This paper investigates how end-to-end (E2E) channel autoencoders (AEs) can achieve energy-efficient wideband communications by leveraging Walsh-Hadamard (WH) interleaved converters. WH interleaving enables high sampling rate analog-digital conversion with reduced power consumption using an analog WH transformation. We demonstrate that E2E-trained neural coded modulation can transparently adapt to the WH-transceiver hardware without requiring algorithmic redesign. Focusing on the short block length regime, we train WH-domain AEs and benchmark them against standard neural and conventional baselines, including 5G Polar codes. We quantify the system-level energy tradeoffs among baseband compute, channel signal-to-noise ratio (SNR), and analog converter power. Our analysis shows that the proposed WH-AE system can approach conventional Polar code SNR performance within 0.14dB while consuming comparable or lower system power. Compared to the best neural baseline, WH-AE achieves, on average, 29% higher energy efficiency (in bit/J) for the same reliability. These findings establish WH-domain learning as a viable path to energy-efficient, high-throughput wideband communications by explicitly balancing compute complexity, SNR, and analog power consumption.

翻译：本文研究了端到端信道自编码器如何通过利用沃尔什-哈达玛交织转换器实现高能效的宽带通信。沃尔什-哈达玛交织技术通过模拟沃尔什-哈达玛变换，能够以较低功耗实现高速率的模数转换。我们证明了经过端到端训练的神经编码调制能够透明地适配沃尔什-哈达玛收发器硬件，而无需重新设计算法。聚焦于短码长场景，我们训练了沃尔什-哈达玛域自编码器，并将其与标准神经基线和传统基线（包括5G Polar码）进行了性能对比。我们量化了基带计算复杂度、信道信噪比与模拟转换器功耗之间的系统级能量权衡。分析表明，所提出的沃尔什-哈达玛自编码器系统能够在0.14dB范围内逼近传统Polar码的信噪比性能，同时消耗相当或更低的系统功耗。与最佳神经基线相比，在相同可靠性条件下，沃尔什-哈达玛自编码器平均可实现29%的能效提升。这些发现确立了沃尔什-哈达玛域学习作为实现高能效、高吞吐量宽带通信的可行路径，其通过显式平衡计算复杂度、信噪比与模拟功耗来实现这一目标。