"Cross-Link Channel Prediction for Massive IoT Networks," presented at the ACM MobiHoc '23 conference in Washington, D.C., has won the Best Paper Award.  The lead author Kun Woo Cho accepted the award at the opening of the conference on October 25, 2023.

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.

Cross-Link Channel Prediction (CLCP) is 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.