TY - GEN
T1 - Scalable Multi-Modal Learning for Cross-Link Channel Prediction in Massive IoT Networks
AU - Cho, Kun Woo
AU - Cominelli, Marco
AU - Gringoli, Francesco
AU - Widmer, Joerg
AU - Jamieson, Kyle
N1 - Publisher Copyright:
© 2023 Owner/Author(s).
PY - 2023/10/23
Y1 - 2023/10/23
N2 - 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 existing transmissions rather than 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 and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.
AB - 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 existing transmissions rather than 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 and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.
KW - channel prediction
KW - massive-IoT networks
KW - multi-modal learning
UR - http://www.scopus.com/inward/record.url?scp=85176153510&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85176153510&partnerID=8YFLogxK
U2 - 10.1145/3565287.3610280
DO - 10.1145/3565287.3610280
M3 - Conference contribution
AN - SCOPUS:85176153510
T3 - Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
SP - 221
EP - 229
BT - MobiHoc 2023 - Proceedings of the 2023 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
PB - Association for Computing Machinery
T2 - 2023 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2023
Y2 - 23 October 2023 through 26 October 2023
ER -