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End-to-end wireless path deployment with intelligent surfaces using interpretable neural networks

Liaskos Christos, Nie Shuai, Tsioliaridou Ageliki, Pitsillides Andreas, Ioannidis Sotirios, Akyildiz, Ian Fuat

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/2FD46CFF-F40D-4752-BE65-08352C034554
Έτος 2020
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
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Βιβλιογραφική Αναφορά C. Liaskos, S. Nie, A. Tsioliaridou, A. Pitsillides, S. Ioannidis and I. Akyildiz, "End-to-end wireless path deployment with intelligent surfaces using interpretable neural networks," IEEE Trans. Commun., vol. 68, no. 11, pp. 6792-6806, Nov. 2020. doi: 10.1109/TCOMM.2020.3012577 https://doi.org/10.1109/TCOMM.2020.3012577
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Περίληψη

Intelligent surfaces exert deterministic control over the wireless propagation phenomenon, enabling novel capabilities in performance, security and wireless power transfer. Such surfaces come in the form of rectangular tiles that cascade to cover large surfaces such as walls, ceilings or building facades. Each tile is addressable and can receive software commands from a controller, manipulating an impinging electromagnetic wave upon it by customizing its reflection direction, focus, polarization and phase. A new problem arises concerning the orchestration of a set of tiles towards serving end-to-end communication objectives. Towards that end, we propose a novel intelligent surface networking algorithm based on interpretable neural networks. Tiles are mapped to neural network nodes and any tile line-of-sight connectivity is expressed as a neural network link. Tile wave manipulation functionalities are captured via geometric reflection with virtually rotatable tile surface norm, thus being able to tunable distribute power impinging upon a tile over the corresponding neural network links, with the corresponding power parts acting as the link weights. A feedforward/backpropagate process optimizes these weights to match ideal propagation outcomes (normalized network power outputs) to wireless user emissions (normalized network power inputs). An interpretation process translates these weights to the corresponding tile wave manipulation functionalities.

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