  In the evolving landscape of vertical heterogeneous networks, the practice of
cell switching particularly for small base stations faces a significant
challenge due to the lack of accurate data on the traffic load of sleeping
SBSs. This information gap is crucial as it hinders the feasibility and
applicability of existing power consumption optimization methods; however, the
studies in the literature predominantly assume perfect knowledge about the
traffic load of sleeping SBSs. Addressing this critical issue, our study
introduces innovative methodologies for estimating the traffic load of sleeping
SBSs in a vHetNet including the integration of a high altitude platform as a
super macro base station into the terrestrial network. We propose three
distinct spatial interpolation-based estimation schemes: clustering-based,
distance based, and random neighboring selection. Employing a real data set for
empirical validations, we compare the estimation performance of the developed
traffic load estimation schemes and assess the impact of estimation errors. Our
findings demonstrate that accurate estimation of sleeping SBSs' traffic loads
is essential for making network power consumption optimization methods both
feasible and applicable in vHetNets.
