  Learning any-to-any (A2A) path loss maps, where the objective is the
reconstruction of path loss between any two given points in a map, might be a
key enabler for many applications that rely on device-to-device (D2D)
communication. Such applications include machine-type communications (MTC) or
vehicle-to-vehicle (V2V) communications. Current approaches for learning A2A
maps are either model-based methods, or pure data-driven methods. Model-based
methods have the advantage that they can generate reliable estimations with low
computational complexity, but they cannot exploit information coming from data.
Pure data-driven methods can achieve good performance without assuming any
physical model, but their complexity and their lack of robustness is not
acceptable for many applications. In this paper, we propose a novel hybrid
model and data-driven approach that fuses information obtained from datasets
and models in an online fashion. To that end, we leverage the framework of
stochastic learning to deal with the sequential arrival of samples and propose
an online algorithm that alternatively and sequentially minimizes the original
non-convex problem. A proof of convergence is presented, along with experiments
based firstly on synthetic data, and secondly on a more realistic dataset for
V2X, with both experiments showing promising results.
