Waveform Design for Over-the-Air Computing

2024-05-31

IEEE Transactions on Wireless Communications

AI Summary

To address performance degradation in over-the-air computation (AirComp) under massive device access—caused by transmitter synchronization errors and inter-symbol interference (ISI)—this paper proposes a robust waveform co-design framework. First, we establish the first theoretical mean-square error (MSE) model for AirComp transmission that explicitly incorporates both synchronization errors and ISI. Second, we formulate a customized loss function that jointly respects energy and bandwidth constraints while enforcing waveform symmetry. Third, we adopt an alternating optimization strategy that jointly designs power allocation and deep neural network (DNN)-driven waveforms, replacing end-to-end DNN retraining with deployable fitted parameters. Simulation results demonstrate that the proposed waveform achieves significantly lower MSE than benchmark waveforms—including Raised Cosine and BTRC—while enabling plug-and-play, rapid deployment in practical systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

In response to the increasing number of devices expected in next-generation networks, a shift to over-the-air (OTA) computing has been proposed. By leveraging the superposition of multiple access channels, OTA computing enables efficient resource management by supporting simultaneous uncoded transmission in the time and frequency domains. To advance the integration of OTA computing, our study presents a theoretical analysis that addresses practical issues encountered in current digital communication transceivers, such as transmitter synchronization (sync) errors and intersymbol interference (ISI). To this end, we investigate the theoretical mean squared error (MSE) for OTA transmission under sync errors and ISI, while also exploring methods for minimizing the MSE in OTA transmission. Using alternating optimization, we also derive optimal power policies for both the devices and the base station. In addition, we propose a novel deep neural network (DNN)-based approach to design waveforms that improve OTA transmission performance under sync errors and ISI. To ensure a fair comparison with existing waveforms such as raised cosine (RC) and better-than-raised-cosine (BTRC), we incorporate a custom loss function that integrates energy and bandwidth constraints along with practical design considerations such as waveform symmetry. Simulation results validate our theoretical analysis and demonstrate performance gains of the designed pulse over RC and BTRC waveforms. To facilitate testing of our results without the need to rebuild the DNN structure, we also provide curve-fitting parameters for the selected DNN-based waveforms.

Problem

• Addresses transmitter synchronization errors in over-the-air computing systems
• Mitigates intersymbol interference issues in digital communication transceivers
• Optimizes waveform design to minimize transmission mean squared error

Innovation

• DNN-based waveform design for OTA computing
• Alternating optimization for optimal power policies
• Custom loss function with energy and bandwidth constraints