Context-aware cognitive radio using deep learning

Autor:	Ahmed Selim, Christian Bluemm, Maicon Kist, Justin Tallon, André Puschmann, Luiz A. DaSilva, Francisco Paisana, Pedro Alvarez
Rok vydání:	2017
Předmět:	Computer science business.industry Deep learning 05 social sciences DySPAN 050801 communication & media studies 020206 networking & telecommunications Context (language use) Throughput 02 engineering and technology Software-defined radio 0508 media and communications Cognitive radio Human–computer interaction 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Telecommunications Field-programmable gate array Communication channel
Zdroj:	DySPAN
DOI:	10.1109/dyspan.2017.7920784
Popis:	This paper describes the design, experimental assessmant and Software Defined Radio (SDR) implementation of a Secondary User (SU) link for the IEEE DySPAN Challenge 2017. The objective is to successfully discern the behavior of and coexist with a Primary User (PU), whose channel access patterns vary over time. For that end, we utilize sensing, deep learning and dynamic optimization.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8927c68e77800a7d63484a89efb574b3 https://doi.org/10.1109/dyspan.2017.7920784 Zobrazit plný text záznamu