  In this paper, we address the problem of reconstructing coverage maps from
path-loss measurements in cellular networks. We propose and evaluate two
kernel-based adaptive online algorithms as an alternative to typical offline
methods. The proposed algorithms are application-tailored extensions of
powerful iterative methods such as the adaptive projected subgradient method
and a state-of-the-art adaptive multikernel method. Assuming that the moving
trajectories of users are available, it is shown how side information can be
incorporated in the algorithms to improve their convergence performance and the
quality of the estimation. The complexity is significantly reduced by imposing
sparsity-awareness in the sense that the algorithms exploit the compressibility
of the measurement data to reduce the amount of data which is saved and
processed. Finally, we present extensive simulations based on realistic data to
show that our algorithms provide fast, robust estimates of coverage maps in
real-world scenarios. Envisioned applications include path-loss prediction
along trajectories of mobile users as a building block for anticipatory
buffering or traffic offloading.
