Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks

Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power alloca...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Chao [verfasserIn] Gao, Zihe [verfasserIn] Xia, Junjuan [verfasserIn] Deng, Dan [verfasserIn] Fan, Liseng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Cache UAV B5G NOMA Physical-layer security Reinforcement learning

Übergeordnetes Werk:	Enthalten in: EURASIP journal on wireless communications and networking - Heidelberg : Springer, 2004, 2020(2020), 1 vom: 06. Jan.
Übergeordnetes Werk:	volume:2020 ; year:2020 ; number:1 ; day:06 ; month:01

Links:	Volltext

DOI / URN:	10.1186/s13638-019-1595-x

Katalog-ID:	SPR032058896

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matchkey_str	article:16871499:2020----::ahealdhsclaescrgmaantmruvsitdt
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publishDate	2020
allfields	10.1186/s13638-019-1595-x doi (DE-627)SPR032058896 (SPR)s13638-019-1595-x-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Li, Chao verfasserin aut Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy. Cache (dpeaa)DE-He213 UAV (dpeaa)DE-He213 B5G (dpeaa)DE-He213 NOMA (dpeaa)DE-He213 Physical-layer security (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Gao, Zihe verfasserin aut Xia, Junjuan verfasserin aut Deng, Dan verfasserin aut Fan, Liseng verfasserin aut Enthalten in EURASIP journal on wireless communications and networking Heidelberg : Springer, 2004 2020(2020), 1 vom: 06. Jan. (DE-627)47265151X (DE-600)2168613-0 1687-1499 nnns volume:2020 year:2020 number:1 day:06 month:01 https://dx.doi.org/10.1186/s13638-019-1595-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.74 ASE 54.32 ASE AR 2020 2020 1 06 01
spelling	10.1186/s13638-019-1595-x doi (DE-627)SPR032058896 (SPR)s13638-019-1595-x-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Li, Chao verfasserin aut Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy. Cache (dpeaa)DE-He213 UAV (dpeaa)DE-He213 B5G (dpeaa)DE-He213 NOMA (dpeaa)DE-He213 Physical-layer security (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Gao, Zihe verfasserin aut Xia, Junjuan verfasserin aut Deng, Dan verfasserin aut Fan, Liseng verfasserin aut Enthalten in EURASIP journal on wireless communications and networking Heidelberg : Springer, 2004 2020(2020), 1 vom: 06. Jan. (DE-627)47265151X (DE-600)2168613-0 1687-1499 nnns volume:2020 year:2020 number:1 day:06 month:01 https://dx.doi.org/10.1186/s13638-019-1595-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.74 ASE 54.32 ASE AR 2020 2020 1 06 01
allfields_unstemmed	10.1186/s13638-019-1595-x doi (DE-627)SPR032058896 (SPR)s13638-019-1595-x-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Li, Chao verfasserin aut Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy. Cache (dpeaa)DE-He213 UAV (dpeaa)DE-He213 B5G (dpeaa)DE-He213 NOMA (dpeaa)DE-He213 Physical-layer security (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Gao, Zihe verfasserin aut Xia, Junjuan verfasserin aut Deng, Dan verfasserin aut Fan, Liseng verfasserin aut Enthalten in EURASIP journal on wireless communications and networking Heidelberg : Springer, 2004 2020(2020), 1 vom: 06. Jan. (DE-627)47265151X (DE-600)2168613-0 1687-1499 nnns volume:2020 year:2020 number:1 day:06 month:01 https://dx.doi.org/10.1186/s13638-019-1595-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.74 ASE 54.32 ASE AR 2020 2020 1 06 01
allfieldsGer	10.1186/s13638-019-1595-x doi (DE-627)SPR032058896 (SPR)s13638-019-1595-x-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Li, Chao verfasserin aut Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy. Cache (dpeaa)DE-He213 UAV (dpeaa)DE-He213 B5G (dpeaa)DE-He213 NOMA (dpeaa)DE-He213 Physical-layer security (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Gao, Zihe verfasserin aut Xia, Junjuan verfasserin aut Deng, Dan verfasserin aut Fan, Liseng verfasserin aut Enthalten in EURASIP journal on wireless communications and networking Heidelberg : Springer, 2004 2020(2020), 1 vom: 06. Jan. (DE-627)47265151X (DE-600)2168613-0 1687-1499 nnns volume:2020 year:2020 number:1 day:06 month:01 https://dx.doi.org/10.1186/s13638-019-1595-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.74 ASE 54.32 ASE AR 2020 2020 1 06 01
allfieldsSound	10.1186/s13638-019-1595-x doi (DE-627)SPR032058896 (SPR)s13638-019-1595-x-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Li, Chao verfasserin aut Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy. Cache (dpeaa)DE-He213 UAV (dpeaa)DE-He213 B5G (dpeaa)DE-He213 NOMA (dpeaa)DE-He213 Physical-layer security (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Gao, Zihe verfasserin aut Xia, Junjuan verfasserin aut Deng, Dan verfasserin aut Fan, Liseng verfasserin aut Enthalten in EURASIP journal on wireless communications and networking Heidelberg : Springer, 2004 2020(2020), 1 vom: 06. Jan. (DE-627)47265151X (DE-600)2168613-0 1687-1499 nnns volume:2020 year:2020 number:1 day:06 month:01 https://dx.doi.org/10.1186/s13638-019-1595-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.74 ASE 54.32 ASE AR 2020 2020 1 06 01
language	English
source	Enthalten in EURASIP journal on wireless communications and networking 2020(2020), 1 vom: 06. Jan. volume:2020 year:2020 number:1 day:06 month:01
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institution	findex.gbv.de
topic_facet	Cache UAV B5G NOMA Physical-layer security Reinforcement learning
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authorswithroles_txt_mv	Li, Chao @@aut@@ Gao, Zihe @@aut@@ Xia, Junjuan @@aut@@ Deng, Dan @@aut@@ Fan, Liseng @@aut@@
publishDateDaySort_date	2020-01-06T00:00:00Z
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author	Li, Chao
spellingShingle	Li, Chao ddc 620 bkl 53.74 bkl 54.32 misc Cache misc UAV misc B5G misc NOMA misc Physical-layer security misc Reinforcement learning Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks
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topic_title	620 004 ASE 53.74 bkl 54.32 bkl Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks Cache (dpeaa)DE-He213 UAV (dpeaa)DE-He213 B5G (dpeaa)DE-He213 NOMA (dpeaa)DE-He213 Physical-layer security (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213
topic	ddc 620 bkl 53.74 bkl 54.32 misc Cache misc UAV misc B5G misc NOMA misc Physical-layer security misc Reinforcement learning
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title	Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks
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title_full	Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks
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title_sort	cache-enabled physical-layer secure game against smart uav-assisted attacks in b5g noma networks
title_auth	Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks
abstract	Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy.
abstractGer	Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy.
abstract_unstemmed	Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy.
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container_issue	1
title_short	Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks
url	https://dx.doi.org/10.1186/s13638-019-1595-x
remote_bool	true
author2	Gao, Zihe Xia, Junjuan Deng, Dan Fan, Liseng
author2Str	Gao, Zihe Xia, Junjuan Deng, Dan Fan, Liseng
ppnlink	47265151X
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isOA_txt	true
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doi_str	10.1186/s13638-019-1595-x
up_date	2024-07-04T02:18:17.584Z
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