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Abstract
Learning any-to-any (A2A) path loss maps might be a key enabler for many applications that rely on a device-to-device (D2D) communication, such as vehicle-to-vehicle (V2V) communications. Current approaches for learning A2A maps have a number of important limitations, including i) a high complexity that increases rapidly with the number of samples, making the problems quickly intractable, and ii) the inability of coping with a time-varying environment, among others. In this letter, we propose a novel approach that reconstruct A2A path loss maps in an online fashion. To that end, we leverage on the framework of stochastic learning to deal with the sequential arrival of samples, and propose an online algorithm based on the forward-backward splitting method. Preliminary simulation results show a significant decrease in complexity, while its performance is comparable to that of a batch approach.
Highlights
M ANY applications in wireless networks can benefit from information related to the spatial distribution of path loss
We propose an algorithm based on the descent version of an alternating minimization where we take one step of the forward-backward splitting method to update the spatial loss field (SLF), followed by another step to update the model
We have addressed the online learning of path loss maps
Summary
M ANY applications in wireless networks can benefit from information related to the spatial distribution of path loss. A problem with elastic net regularization and multi-kernels is formulated, and an algorithm based on the ADMM is used to obtain a solution Both algorithms in [7], [8] have a key limitation since they batch algorithms, i.e. upon arrival of new measurements, the algorithms are carried out again including all the historical data, which increases the complexity dramatically. We extend the work in [8] and pose an optimization problem that, upon arrival of new measurements, aims at obtaining new estimates of both the SLF and the window matrix We do this by minimizing the least squared error regularized by elastic nets [9]. We propose an algorithm based on the descent version of an alternating minimization where we take one step of the forward-backward splitting method to update the SLF, followed by another step to update the model
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