Quadrature Over-the-Air-Computing for Multimodal Dual-Stream Signal Processing

We propose a novel quadrature over-the-air computing (Q-OTAC) framework that enables the simultaneously computation of two independent functions and/or data streams within a single transmission. In contrast to conventional OTAC schemes, where a single function is computed by treating each complex signal as a single component, the proposed Q-OTAC exploits both in-phase and quadrature (IQ) components of a complex signal, encoding two distinct functions and/or data streams at the edge devices (EDs) and employing a novel low-complexity IQ-decoupled combiner at the access point (AP) to independently recover each stream, which effectively doubles the computation rate. A key strength of this framework lies in its simplicity and broad compatibility: the extension into the quadrature domain is conceptually straightforward, yet remakably powerful, allowing seamless integration into existing OTAC techniques. Simulation results validate the effectiveness of this approach, including the first demonstration of dual-function aggregation (e.g., parallel summation and product), highlighting the potential of Q-OTAC for enabling multi-modal and high-efficiency beyond fifth generation (B5G) applications.

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Publication Details

Published
2026-04-21
DOI
https://doi.org/10.1109/icassp55912.2026.11465041
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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article

Quadrature Over-the-Air-Computing for Multimodal Dual-Stream Signal Processing

Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, David González G., Kengo Ando
Neural Networks and Reservoir Computing
article

Quadrature Over-the-Air-Computing for Multimodal Dual-Stream Signal Processing

Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, David González G., Kengo Ando
article en

Abstract

We propose a novel quadrature over-the-air computing (Q-OTAC) framework that enables the simultaneously computation of two independent functions and/or data streams within a single transmission. In contrast to conventional OTAC schemes, where a single function is computed by treating each complex signal as a single component, the proposed Q-OTAC exploits both in-phase and quadrature (IQ) components of a complex signal, encoding two distinct functions and/or data streams at the edge devices (EDs) and employing a novel low-complexity IQ-decoupled combiner at the access point (AP) to independently recover each stream, which effectively doubles the computation rate. A key strength of this framework lies in its simplicity and broad compatibility: the extension into the quadrature domain is conceptually straightforward, yet remakably powerful, allowing seamless integration into existing OTAC techniques. Simulation results validate the effectiveness of this approach, including the first demonstration of dual-function aggregation (e.g., parallel summation and product), highlighting the potential of Q-OTAC for enabling multi-modal and high-efficiency beyond fifth generation (B5G) applications.

Constructor University (DE), Cameco (Germany) (DE)
Openalex Percentile: Top 99%
Neural Networks and Reservoir Computing
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