Cross-Link Channel Prediction for Massive IoT Networks

TL;DR

This paper introduces Cross-Link Channel Prediction (CLCP), a novel method that leverages multi-view learning to predict CSI of nearby links in massive IoT networks, significantly reducing overhead and improving throughput.

Contribution

The paper proposes CLCP, a practical multi-view learning approach for cross-link CSI prediction that outperforms existing algorithms and reduces estimation overhead in large-scale IoT networks.

Findings

CLCP achieves 2x throughput gain over baseline 802.11ax.

CLCP outperforms existing cross-band prediction algorithms by 30%.

Evaluation conducted in large-scale indoor scenarios with up to 144 users.

Abstract

Tomorrow's massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP's design is highly practical, exploiting channel estimates obtained from existing transmissions instead of dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users. Moreover, we measure its performance with four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2x throughput gain over baseline 802.11ax and a 30 percent throughput gain over existing cross-band prediction algorithms.