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.

翻译：学习任何到任何(A2A)路径丢失图(A2A),目标是重建地图中任何两个指定点之间的路径丢失,这种方法可能是许多依赖设备到装置(D2D)通信的应用程序的关键促进因素,这些应用程序包括机器型通信(MTC)或车辆到车辆(V2V)通信。目前学习A2A地图的方法或是以模型为基础的方法,或纯粹的数据驱动方法。基于模型的方法的优点是,它们能够产生可靠的估计,而计算复杂性较低,但它们不能利用数据产生的信息。纯粹的数据驱动方法可以取得良好的业绩,而不必假定任何物理模型,但它们的复杂性和缺乏可靠性是许多应用程序所不能接受的。在本文中,我们建议采用新的混合模型和数据驱动方法,将从数据集和模型获得的信息以在线方式结合。为此,我们利用随机学习框架来处理样品的顺序顺序,并提出一种在线算法,可以将原非convelx问题按顺序进行最小化。在最初的非convex问题上,纯粹的数据可以实现良好的性,但对于许多应用程序来说,它们的复杂性和缺乏强健性的数据的合并证据。