论文标题
用于远程联合边缘学习的基于CHIRP的空中计算
Chirp-Based Over-the-Air Computation for Long-Range Federated Edge Learning
论文作者
论文摘要
在这项研究中，我们提出了基于循环迁移的CHIRP（CSC）多数票（MV）（CSC-MV），这是一种强大的空中计算（OAC）方案，以实现远程联合边缘学习（FEEL）。提出的方法将从边缘设备（ED）的投票（即局部梯度的符号）映射到使用离散的傅立叶变换式正交频施加频施换（DFT-S-OFDM）发射机构建的线性CSC。在边缘服务器（ES），MV使用能量检测器计算。我们将提议的方案与一位宽带数字聚合（OBDA）进行比较，并表明具有相邻通道渗透比率（ACLR）CSC-MV的发射器的输出功率退回（OBO）要求低于OBDA。例如，CSC-MV的ACLR约束为-22 dB，OBO的要求比具有OBDA的OBO少6-7 dB。当考虑功率放大器（PA）非线性时，我们证明CSC-MV在同质和异构数据分布的测试准确性方面都优于OBDA，而无需在ES和EDS上使用信道状态信息（CSI）。
In this study, we propose circularly-shifted chirp (CSC)-based majority vote (MV) (CSC-MV), a power-efficient over-the-air computation (OAC) scheme, to achieve long-range federated edge learning (FEEL). The proposed approach maps the votes (i.e., the sign of the local gradients) from the edge devices (EDs) to the linear CSCs constructed with a discrete Fourier transform-spread orthogonal frequency division multiplexing (DFT-s-OFDM) transmitter. At the edge server (ES), the MV is calculated with an energy detector. We compare our proposed scheme with one-bit broadband digital aggregation (OBDA) and show that the output-power back-off (OBO) requirement of the transmitters with an adjacent-channel-leakage ratio (ACLR) constraint for CSC-MV is lower than the one with OBDA. For example, with an ACLR constraint of -22 dB, CSC-MV can have an OBO requirement of 6-7 dB less than the one with OBDA. When the power amplifier (PA) non-linearity is considered, we demonstrate that CSC-MV outperforms OBDA in terms of test accuracy for both homogeneous and heterogeneous data distributions, without using channel state information (CSI) at the ES and EDs.
