arXiv：cs.LG· Seyed Mohammad Azimi-Abarghouyi·· 5 小时前AI 评分30
AirCPU：从无线叠加中提取结构化函数的 out-of-air computation
Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superposition
AI 导读
论文提出 out-of-air computation（AirCPU），一种基于联合信源信道编码的提取式 AirComp 新范式，从结构化无线叠加中直接提取目标函数，无需单独的量化阶段。
正文
Abstract:Over-the-air computation (AirComp) broadly exploits the superposition property of wireless multiple-access channels (MACs) to compute functions of distributed data. Within this broad class, dominant conventional designs are embedding-oriented: they pre-shape transmitted signals or mitigate channel effects so that the received superposition directly realizes the prescribed computation, often requiring the MAC to approximate an ideal computational medium. This paper introduces out-of-air computation (AirCPU) and establishes an extraction-oriented paradigm for AirComp. Built on joint source-channel coding, AirCPU creates a structured wireless superposition from which the receiver extracts the target function. AirCPU operates directly on continuous-valued device data, avoiding the need for a separate source quantization stage, and employs a multi-layer nested lattice architecture that enables progressive resolution by decomposing each input into hierarchically scaled components, all transmitted over a common bounded digital constellation under a fixed power constraint. We formalize the notion of decoupled resolution, showing that in operating regimes where the decoding error probability is sufficiently small, the impact of channel noise and finite constellation constraints on distortion becomes negligible, and the resulting computation error is primarily determined by the target resolution set by the finest lattice. For fading MACs, we further introduce collective and successive computation mechanisms, in addition to the proposed direct computation, which exploit multiple decoded integer-coefficient functions and side-information functions as structural representations of the wireless superposition to significantly expand the reliable operating regime.
| Subjects: | Information Theory (cs.IT); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.04312 [cs.IT] |
| (or arXiv:2604.04312v2 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2604.04312 arXiv-issued DOI via DataCite |
Submission history
From: Seyed Mohammad Azimi-Abarghouyi [view email]
[v1]
Sun, 5 Apr 2026 23:12:16 UTC (158 KB)
[v2]
Wed, 7 Oct 2026 00:17:33 UTC (163 KB)
来源：arXiv：cs.LG · arxiv.org