Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory

As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models ofte...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ke Zang [verfasserIn] Zhenguo Ma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Automatic modulation classification (AMC) recurrent neural networks (RNNs) hierarchical recurrent structure long-term memory

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 213052-213061
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:213052-213061

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3039543

Katalog-ID:	DOAJ062562576

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ062562576
003	DE-627
005	20230309021822.0
007	cr uuu---uuuuu
008	230228s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/ACCESS.2020.3039543 \|2 doi
035			\|a (DE-627)DOAJ062562576
035			\|a (DE-599)DOAJ8aa308d30133449686f3cda521a9c18d
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TK1-9971
100	0		\|a Ke Zang \|e verfasserin \|4 aut
245	1	0	\|a Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory
264		1	\|c 2020
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals.
650		4	\|a Automatic modulation classification (AMC)
650		4	\|a recurrent neural networks (RNNs)
650		4	\|a hierarchical recurrent structure
650		4	\|a long-term memory
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Zhenguo Ma \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Access \|d IEEE, 2014 \|g 8(2020), Seite 213052-213061 \|w (DE-627)728440385 \|w (DE-600)2687964-5 \|x 21693536 \|7 nnns
773	1	8	\|g volume:8 \|g year:2020 \|g pages:213052-213061
856	4	0	\|u https://doi.org/10.1109/ACCESS.2020.3039543 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/8aa308d30133449686f3cda521a9c18d \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/9265251/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2169-3536 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 8 \|j 2020 \|h 213052-213061

Indexfelder

author_variant	k z kz z m zm
matchkey_str	article:21693536:2020----::uoaimdltocasfctobsdnirrhcleurnnuantok
hierarchy_sort_str	2020
callnumber-subject-code	TK
publishDate	2020
allfields	10.1109/ACCESS.2020.3039543 doi (DE-627)DOAJ062562576 (DE-599)DOAJ8aa308d30133449686f3cda521a9c18d DE-627 ger DE-627 rakwb eng TK1-9971 Ke Zang verfasserin aut Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals. Automatic modulation classification (AMC) recurrent neural networks (RNNs) hierarchical recurrent structure long-term memory Electrical engineering. Electronics. Nuclear engineering Zhenguo Ma verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 213052-213061 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:213052-213061 https://doi.org/10.1109/ACCESS.2020.3039543 kostenfrei https://doaj.org/article/8aa308d30133449686f3cda521a9c18d kostenfrei https://ieeexplore.ieee.org/document/9265251/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 213052-213061
spelling	10.1109/ACCESS.2020.3039543 doi (DE-627)DOAJ062562576 (DE-599)DOAJ8aa308d30133449686f3cda521a9c18d DE-627 ger DE-627 rakwb eng TK1-9971 Ke Zang verfasserin aut Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals. Automatic modulation classification (AMC) recurrent neural networks (RNNs) hierarchical recurrent structure long-term memory Electrical engineering. Electronics. Nuclear engineering Zhenguo Ma verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 213052-213061 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:213052-213061 https://doi.org/10.1109/ACCESS.2020.3039543 kostenfrei https://doaj.org/article/8aa308d30133449686f3cda521a9c18d kostenfrei https://ieeexplore.ieee.org/document/9265251/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 213052-213061
allfields_unstemmed	10.1109/ACCESS.2020.3039543 doi (DE-627)DOAJ062562576 (DE-599)DOAJ8aa308d30133449686f3cda521a9c18d DE-627 ger DE-627 rakwb eng TK1-9971 Ke Zang verfasserin aut Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals. Automatic modulation classification (AMC) recurrent neural networks (RNNs) hierarchical recurrent structure long-term memory Electrical engineering. Electronics. Nuclear engineering Zhenguo Ma verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 213052-213061 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:213052-213061 https://doi.org/10.1109/ACCESS.2020.3039543 kostenfrei https://doaj.org/article/8aa308d30133449686f3cda521a9c18d kostenfrei https://ieeexplore.ieee.org/document/9265251/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 213052-213061
allfieldsGer	10.1109/ACCESS.2020.3039543 doi (DE-627)DOAJ062562576 (DE-599)DOAJ8aa308d30133449686f3cda521a9c18d DE-627 ger DE-627 rakwb eng TK1-9971 Ke Zang verfasserin aut Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals. Automatic modulation classification (AMC) recurrent neural networks (RNNs) hierarchical recurrent structure long-term memory Electrical engineering. Electronics. Nuclear engineering Zhenguo Ma verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 213052-213061 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:213052-213061 https://doi.org/10.1109/ACCESS.2020.3039543 kostenfrei https://doaj.org/article/8aa308d30133449686f3cda521a9c18d kostenfrei https://ieeexplore.ieee.org/document/9265251/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 213052-213061
allfieldsSound	10.1109/ACCESS.2020.3039543 doi (DE-627)DOAJ062562576 (DE-599)DOAJ8aa308d30133449686f3cda521a9c18d DE-627 ger DE-627 rakwb eng TK1-9971 Ke Zang verfasserin aut Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals. Automatic modulation classification (AMC) recurrent neural networks (RNNs) hierarchical recurrent structure long-term memory Electrical engineering. Electronics. Nuclear engineering Zhenguo Ma verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 213052-213061 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:213052-213061 https://doi.org/10.1109/ACCESS.2020.3039543 kostenfrei https://doaj.org/article/8aa308d30133449686f3cda521a9c18d kostenfrei https://ieeexplore.ieee.org/document/9265251/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 213052-213061
language	English
source	In IEEE Access 8(2020), Seite 213052-213061 volume:8 year:2020 pages:213052-213061
sourceStr	In IEEE Access 8(2020), Seite 213052-213061 volume:8 year:2020 pages:213052-213061
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Automatic modulation classification (AMC) recurrent neural networks (RNNs) hierarchical recurrent structure long-term memory Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	IEEE Access
authorswithroles_txt_mv	Ke Zang @@aut@@ Zhenguo Ma @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	728440385
id	DOAJ062562576
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ062562576</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309021822.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2020.3039543</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ062562576</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ8aa308d30133449686f3cda521a9c18d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ke Zang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Automatic modulation classification (AMC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">recurrent neural networks (RNNs)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hierarchical recurrent structure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">long-term memory</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhenguo Ma</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">8(2020), Seite 213052-213061</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2020</subfield><subfield code="g">pages:213052-213061</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2020.3039543</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/8aa308d30133449686f3cda521a9c18d</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9265251/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2020</subfield><subfield code="h">213052-213061</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Ke Zang
spellingShingle	Ke Zang misc TK1-9971 misc Automatic modulation classification (AMC) misc recurrent neural networks (RNNs) misc hierarchical recurrent structure misc long-term memory misc Electrical engineering. Electronics. Nuclear engineering Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory
authorStr	Ke Zang
ppnlink_with_tag_str_mv	@@773@@(DE-627)728440385
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	TK1-9971
illustrated	Not Illustrated
issn	21693536
topic_title	TK1-9971 Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory Automatic modulation classification (AMC) recurrent neural networks (RNNs) hierarchical recurrent structure long-term memory
topic	misc TK1-9971 misc Automatic modulation classification (AMC) misc recurrent neural networks (RNNs) misc hierarchical recurrent structure misc long-term memory misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Automatic modulation classification (AMC) misc recurrent neural networks (RNNs) misc hierarchical recurrent structure misc long-term memory misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Automatic modulation classification (AMC) misc recurrent neural networks (RNNs) misc hierarchical recurrent structure misc long-term memory misc Electrical engineering. Electronics. Nuclear engineering
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Access
hierarchy_parent_id	728440385
hierarchy_top_title	IEEE Access
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)728440385 (DE-600)2687964-5
title	Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory
ctrlnum	(DE-627)DOAJ062562576 (DE-599)DOAJ8aa308d30133449686f3cda521a9c18d
title_full	Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory
author_sort	Ke Zang
journal	IEEE Access
journalStr	IEEE Access
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2020
contenttype_str_mv	txt
container_start_page	213052
author_browse	Ke Zang Zhenguo Ma
container_volume	8
class	TK1-9971
format_se	Elektronische Aufsätze
author-letter	Ke Zang
doi_str_mv	10.1109/ACCESS.2020.3039543
author2-role	verfasserin
title_sort	automatic modulation classification based on hierarchical recurrent neural networks with grouped auxiliary memory
callnumber	TK1-9971
title_auth	Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory
abstract	As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals.
abstractGer	As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals.
abstract_unstemmed	As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory
url	https://doi.org/10.1109/ACCESS.2020.3039543 https://doaj.org/article/8aa308d30133449686f3cda521a9c18d https://ieeexplore.ieee.org/document/9265251/ https://doaj.org/toc/2169-3536
remote_bool	true
author2	Zhenguo Ma
author2Str	Zhenguo Ma
ppnlink	728440385
callnumber-subject	TK - Electrical and Nuclear Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/ACCESS.2020.3039543
callnumber-a	TK1-9971
up_date	2024-07-04T02:07:13.754Z
_version_	1803612411665580032
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ062562576</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309021822.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2020.3039543</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ062562576</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ8aa308d30133449686f3cda521a9c18d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ke Zang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a valuable topic in wireless communication systems, automatic modulation classification has been studied for many years. In recent years, recurrent neural networks (RNNs), such as long short-term memory (LSTM), have been used in this area and have achieved good results. However, these models often suffer from the vanishing gradient problem when the temporal depth and spatial depth increases, which diminishes the ability to latch long-term memories. In this paper, we propose a new hierarchical RNN architecture with grouped auxiliary memory to better capture long-term dependencies. The proposed model is compared with LSTM and gated recurrent unit (GRU) on the RadioML 2016.10a dataset, which is widely used as a benchmark in modulation classification. The results show that the proposed network yields a higher average classification accuracy under varying signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, even with much fewer parameters. The performance superiority is also confirmed using a dataset with variable lengths of signals.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Automatic modulation classification (AMC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">recurrent neural networks (RNNs)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hierarchical recurrent structure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">long-term memory</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhenguo Ma</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">8(2020), Seite 213052-213061</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2020</subfield><subfield code="g">pages:213052-213061</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2020.3039543</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/8aa308d30133449686f3cda521a9c18d</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9265251/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2020</subfield><subfield code="h">213052-213061</subfield></datafield></record></collection>
score	7.400527

Nicht das Richtige dabei?

Schreiben Sie uns!

Automatic Modulation Classification Based on Hierarchical Recurrent Neural Networks With Grouped Auxiliary Memory

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?