Authors
Swaroop Gopalam, Randall Berry, Dongning Guo, and Michael Honig (Northwestern University)
Abstract
Spectrum sharing with a primary incumbent imposes constraints on entrant transmitters to limit the associated interference. Existing designs use conservative models of the propagation environment and worst-case entrant power levels for ensuring protection, leading to spectral inefficiency. We present two approaches driven by spatially distributed beacons or sensors, where measurements are used for estimating interference levels at particular incumbent locations. We propose a computationally efficient estimation algorithms for both. In the beacons approach, entrants estimate their interference from beacon measurements accounting for their transmit power levels. We propose a computationally efficient estimation algorithm using a maximum likelihood objective for log-normal shadowing, by exploiting channel reciprocity between beacons and the incumbent. In the sensors approach, distributed sensors estimate the entrant power levels from real-time spatial measurements, using a two stage maximum likelihood algorithm with log-normal fading and exponential noise. This knowledge is vital information that can then be used to infer aggregate interference at an incumbent receiver location, or for other purposes such as spectrum monitoring and enforcement.