Over-the-Air Fronthaul Signaling for Uplink Cell-Free Massive MIMO Systems

2025-05-31

AI Summary

To address the high computational load and excessive control signaling overhead induced by digital fronthaul in cell-free massive MIMO systems, this paper pioneers the integration of over-the-air computation (OTA) into the fronthaul architecture. We propose a resource-efficient distributed analog aggregation framework wherein access points (APs) collaboratively compute sufficient statistics directly over the wireless channel—bypassing per-AP backhaul transmission of raw signals. This approach fundamentally alleviates the scalability bottleneck with respect to AP count, significantly reducing fronthaul bandwidth and signaling overhead. We derive closed-form mean-squared error (MSE) expressions for both the sufficient-statistic estimator and the data detector, and corroborate via symbol/ bit error rate (SER/BER) analysis that the proposed scheme achieves performance approaching that of ideal wired fronthaul. Our work establishes a novel paradigm for low-overhead, highly scalable fronthaul design in cell-free networks.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

We propose a novel resource-efficient over-the-air(OTA) computation framework to address the huge fronthaul computational and control overhead requirements in cell-free massive multiple-input multiple-output (MIMO) networks. We show that the global sufficient statistics to decode the data symbols can be computed OTA using the locally available information at the access points (APs). We provide the essential signal processing aspects at the APs and the central processing unit (CPU) to facilitate the OTA computation of sufficient statistics. The proposed framework scales effectively with an increase in the number of APs. We also make a comprehensive study of the benefits of an OTA framework compared to a conventional digital fronthaul in terms of the overhead associated in transferring the sufficient statistics from the APs to the CPU. To evaluate the performance of the OTA framework, we give closed-form expressions for the mean-square error (MSE)of the estimators of sufficient statistics and the overall data estimator. Furthermore, we assess the symbol error rate (SER)and bit error rate (BER) of the user equipment (UEs) data to demonstrate the efficacy of our method, and benchmark them against the state-of-the-art wired fronthaul networks.

Problem

• Reducing fronthaul overhead in cell-free massive MIMO
• Enabling over-the-air computation of sufficient statistics
• Comparing OTA framework performance with wired fronthaul

Innovation

• OTA computation framework reduces fronthaul overhead
• Global statistics computed locally at APs
• Scalable solution for increasing AP numbers