| Paper ID | SS-3.2 |
| Paper Title |
Blind Carbon Copy on Dirty Paper: Seamless Spectrum Underlay via Canonical Correlation Analysis |
| Authors |
Mohamed Salah Ibrahim, Nicholas D. Sidiropoulos, University of Virginia, United States |
| Session | SS-3: Machine Learning in Wireless Networks |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Machine Learning in Wireless Networks |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The spectrum underlay concept promises enhanced spectrum utilization without disturbing legacy / licensed or scientific primary users, so long as their interference constraints can be met. Existing underlay schemes assume that both the primary signal to secondary interference plus noise ratio, and the secondary signal to primary interference plus noise ratio can be high enough at the primary and secondary receiver, respectively. Even if the cross-network channel state information is available at the secondary users, these two conflicting requirements are hard to achieve simultaneously in practice. This work proposes a practical data-driven approach that allows a pair of secondary users to reliably communicate in underlay mode while keeping the interference at the primary receiver close to its noise floor. The secondary transmitter merely has to transmit its signal twice, at very low power - above the noise floor, but well below the primary's interference. It is shown here that reliable detection of the secondary signal is possible via canonical correlation analysis (CCA). Theoretical and experimental results reveal the remarkable detection performance of the proposed CCA-based approach, which does not require any cross-network coordination, or even channel state information. |
