Re-thinking compliance enforcement: Investigating random spectrum sampling techniques for temporal occupancy characterization

Abstract

The estimation of temporal occupancy statistics is a common monitoring output for spectrum management. In emerging dynamic spectrum access (DSA) networks as well as for recent interference limit policies such as harm claim threshold mechanisms, this is even more crucial for compliance enforcement since operational parameters additionally can include temporal constraints. In this paper, random temporal sampling is explored, for probabilistic characterization of temporal channel occupancy. The precision and bias performance due to various random temporal measurement plan designs are examined, both analytically as well as through experiments implemented using Software Defined Radio technology. A framework for performance analysis of random temporal sampling for compliance enforcement is presented, and a lower bound on sensing performance in terms of estimator precision is derived for spectrum occupancy modeled as an alternating renewal process. Using random sampling, estimator variance is seen to approach the theoretical lower bound using larger sample sizes, or through sparser sampling. Results further suggest that the detection error impacts the performance of random temporal sampling for average temporal occupancy estimation. The work further motivates the use of probabilistic characterization of spectrum occupancy for compliance enforcement, given the non-deterministic behavior of dynamic spectrum access mechanisms in emerging wireless network deployment scenarios.