  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.
