The Computing Channel: How Modulation Programs the Airwaves

2026-09-10

AI Summary

本文提出了一种数字函数导向通信方法，通过多址叠加编码直接计算所需聚合结果，无需恢复单个输入，解决了分布式计算中数据交换效率问题。

Abstract

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.

Problem

• distributed computing
• over-the-air computation
• analog signaling
• finite-precision data
• digital communication

Innovation

• digital function-oriented communication
• computational-constellation principle
• over-the-air computation (OAC)
• finite-alphabet symbol representations