Deep unfolding based hyper‐parameter optimisation for self‐interference cancellation in LTE‐A/5G‐transceivers

Autor:	Thomas Paireder, Mario Huemer, Christian Motz
Rok vydání:	2021
Předmět:	Hyperparameter LTE Advanced Computer science Electronic engineering Electrical engineering. Electronics. Nuclear engineering Electrical and Electronic Engineering Transceiver Self interference 5G TK1-9971
Zdroj:	Electronics Letters, Vol 57, Iss 18, Pp 711-713 (2021)
ISSN:	1350-911X 0013-5194
DOI:	10.1049/ell2.12230
Popis:	Deep unfolding is a very promising concept that allows to combine the advantages of traditional estimation techniques, such as adaptive filters, and machine learning approaches, like artificial neural networks. Focusing on a challenging self‐interference problem occurring in frequency‐division duplex radio frequency transceivers, namely modulated spurs, it is shown that deep unfolding enables remarkable performance gains. Based on the hyper‐parameter optimisation of several least‐mean squares (LMS) variants and the recursive‐least squares algorithm, the importance of a well‐chosen loss function are highlighted. Especially the variable step‐size LMS and the transform‐domain LMS vastly benefit without increased runtime complexity.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b09fce6f23669109f23024649ac4dcef https://doi.org/10.1049/ell2.12230 Zobrazit plný text záznamu Plný text