Online Learning of Any-to-Any Path Loss Maps
| Autor: | Slawomir Stanczak, Martin Kasparick, M. A. Gutierrez-Estevez |
|---|---|
| Přispěvatelé: | Publica |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Stochastic process
Computer science business.industry 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Computer Science Applications Kernel (linear algebra) Modeling and Simulation Enabling 0202 electrical engineering electronic engineering information engineering Key (cryptography) Leverage (statistics) Path loss Artificial intelligence Electrical and Electronic Engineering Online algorithm Shadow mapping business computer |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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