计算信道：调制如何编程电波
The Computing Channel: How Modulation Programs the Airwaves
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中文总结 AI 辅助
本文提出数字面向函数通信，通过联合设计符号表示与接收决策，利用多址叠加直接计算聚合函数，以解决空中计算与数字系统的失配问题，并展示其在联邦边缘学习中的应用。
中文摘要 AI 辅助
分布式计算和分布式人工智能需要频繁交换中间结果，尽管许多应用只需要聚合结果而非来自单个设备的消息。传统系统在计算聚合之前先恢复每条消息，而空中计算（OAC）利用同时传输直接获得聚合结果。然而，主流的OAC实现依赖于模拟信号，这与有限精度数据和数字通信流程不匹配。本文提出数字面向函数通信，其中有限字母符号表示和接收机决策被联合设计，使得多址叠加编码所需函数而无需恢复单个输入。我们介绍了其计算星座原理、主要设计方法、扩展和实现挑战。联邦边缘学习展示了该框架如何直接对量化模型更新进行操作，同时减少用户相关的数据承载资源。
英文摘要
Distributed computing and distributed artificial intelligence require frequent exchanges of intermediate results, although many applications need only an aggregate rather than messages from individual devices. Conventional systems recover each message before computing the aggregate, whereas over-the-air computation (OAC) exploits simultaneous transmission to obtain it directly. However, dominant OAC implementations rely on analog signaling, creating a mismatch with finite-precision data and digital communication procedures. This article presents digital function-oriented communication, in which finite-alphabet symbol representations and receiver decisions are jointly designed so that multiple-access superposition encodes the desired function without recovering individual inputs. We introduce its computational-constellation principle, main design approaches, extensions, and implementation challenges. Federated edge learning illustrates how the framework can reduce user-dependent data-bearing resources while operating directly on quantized model updates.
发表机构
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology（电气工程学院和计算机学院，皇家理工学院）
- Digital Futures of KTH（KTH数字未来中心）
机构由 AI 辅助整理，请以论文原文为准。
