Low-Complexity OTFS-Based Over-the-Air Computation Design for Time-Varying Channels

2024-05-11

IEEE Transactions on Wireless Communications

AI Summary

To address the increased data estimation error in AirComp caused by time-varying multipath channels under high-mobility scenarios, this paper proposes an integrated OTFS-AirComp design framework. We first formulate three coordinated schemes: S1 performs direct channel estimation, achieving minimal overhead and optimal performance at low SNR; S2 incorporates zero-padded auxiliary signals to jointly suppress MSE and maintain computational efficiency; S3 introduces a matrix-form iterative estimation algorithm that significantly reduces MSE in highly dispersive channels. Collectively, these schemes establish novel trade-offs between estimation accuracy and computational complexity. Extensive evaluations demonstrate that all three schemes consistently outperform conventional OFDM-AirComp and other baseline approaches across diverse mobility and channel conditions, with S3 delivering the lowest MSE in severe Doppler-rich environments. The framework thus advances robust over-the-air aggregation for next-generation mobile edge computing systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper investigates over-the-air computation (AirComp) over multiple-access time-varying channels, where devices with high mobility transmit their sensing data to a fusion center (FC) for averaging. To combat the Doppler shift induced by time-varying channels, each device adopts orthogonal time frequency space (OTFS) modulation. Our objective is minimizing the mean squared error (MSE) for the target function estimation. Due to the multipath time-varying channels, the OTFS-based AirComp not only suffers from noise but also interference. Specifically, we propose three schemes, namely S1, S2, and S3, for the target function estimation. S1 directly estimates the target function under the impacts of noise and interference. S2 mitigates the interference by introducing a zero padding-assisted OTFS. In S3, we propose an iterative algorithm to estimate the function in a matrix form. In the numerical results, we evaluate the performance of S1, S2, and S3 from the perspectives of MSE and computational complexity, and compare them with benchmarks. Specifically, compared to benchmarks, S3 outperforms them with a significantly lower MSE but incurs a higher computational complexity. In contrast, S2 demonstrates a reduction in both MSE and computational complexity. Lastly, S1 shows superior error performance at small SNR and reduced computational complexity.

Problem

• OTFS Technology
• Channel Estimation
• Aloha Computing

Innovation

• OTFS Technology
• Data Estimation Schemes
• Efficient Signal Processing