通过联合训练编码与解码器，提升低信噪比下联邦边缘学习的无线聚合性能。
Learned Digital Over-the-Air Computing for Federated Edge Learning
- 设计可学习的数字无线聚合框架，融合无源随机接入与压缩感知
- 在相同上行开销下，信噪比适用范围提升约7dB
- 适用于不同模型、设备活跃度和异构数据场景
无线超表面聚合通过利用无线信道的叠加特性，将通信与计算融合，实现联邦边缘学习（FEEL），无需逐个调度和解码设备。模拟式OTA方案传输未编码更新，但对噪声、信道衰落和功率错配敏感，促使更鲁棒的数字方案发展。然而，当前最先进的数字OTA设计结合无源随机接入（URA）与压缩感知，在物联网部署中常见的低信噪比（SNR）环境下表现不佳，符号恢复和活跃设备估计变得不可靠。本文提出一种可学习的数字OTA框架，联合训练一个URA码本与基于展开近似消息传递（AMP）的解码器。所提解码器引入逐层阻尼、残差缩放、温度控制的贝叶斯去噪及轻量卷积神经网络（CNN）精炼模块，码本则通过因子化参数化实现端到端优化。在接近完美聚合精度下，该设计在相同上行开销下，相比最先进基线将可用信噪比范围扩展约7dB，且在不同模型、活跃度水平和异构数据分布下具有良好泛化能力。
原文摘要 · Abstract (English)
Over-the-air (OTA) aggregation enables federated edge learning (FEEL) by exploiting the superposition property of the wireless channel to merge communication with computation, eliminating the need to schedule and decode devices individually. Analog OTA schemes transmit uncoded updates but are sensitive to noise, fading, and power misalignment, motivating more robust digital alternatives. However, state-of-the-art (SoTA) digital OTA designs that combine unsourced random access (URA) with compressed sensing struggle in the low signal-to-noise ratio (SNR) regimes common in Internet of Things (IoT) deployments, where symbol recovery and active-device estimation become unreliable. We propose a learned digital OTA framework that jointly trains a URA codebook with an unrolled approximate message passing (AMP)-based decoder. The learned decoder incorporates per-layer damping, residual scaling, temperature-controlled Bayesian denoising, and a lightweight convolutional neural network (CNN) refinement, while the codebook is optimised end-to-end through a factorised parameterisation. At near-perfect-aggregation accuracy, the proposed design extends the viable SNR range by approximately 7\,dB over the SoTA baseline at the same uplink overhead, and generalises across models, activity levels, and heterogeneous data.
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