Collaborative beamforming via diffusion adaptation based on tensor over array networks
Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we here...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xia, Wei [verfasserIn] Xia, Guoqing [verfasserIn] Li, Jinghua [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Digital signal processing - Orlando, Fla. : Academic Press, 1991, 106 |
|---|---|
| Übergeordnetes Werk: |
volume:106 |
| DOI / URN: |
10.1016/j.dsp.2020.102825 |
|---|
| Katalog-ID: |
ELV004735838 |
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| 245 | 1 | 0 | |a Collaborative beamforming via diffusion adaptation based on tensor over array networks |
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| 520 | |a Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency. | ||
| 650 | 4 | |a Distributed beamforming | |
| 650 | 4 | |a Adaptive networks | |
| 650 | 4 | |a Diffusion strategies | |
| 650 | 4 | |a Tensor | |
| 700 | 1 | |a Xia, Guoqing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jinghua |e verfasserin |4 aut | |
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10.1016/j.dsp.2020.102825 doi (DE-627)ELV004735838 (ELSEVIER)S1051-2004(20)30170-6 DE-627 ger DE-627 rda eng 620 DE-600 53.73 bkl Xia, Wei verfasserin aut Collaborative beamforming via diffusion adaptation based on tensor over array networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency. Distributed beamforming Adaptive networks Diffusion strategies Tensor Xia, Guoqing verfasserin aut Li, Jinghua verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 106 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 106 |
| spelling |
10.1016/j.dsp.2020.102825 doi (DE-627)ELV004735838 (ELSEVIER)S1051-2004(20)30170-6 DE-627 ger DE-627 rda eng 620 DE-600 53.73 bkl Xia, Wei verfasserin aut Collaborative beamforming via diffusion adaptation based on tensor over array networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency. Distributed beamforming Adaptive networks Diffusion strategies Tensor Xia, Guoqing verfasserin aut Li, Jinghua verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 106 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 106 |
| allfields_unstemmed |
10.1016/j.dsp.2020.102825 doi (DE-627)ELV004735838 (ELSEVIER)S1051-2004(20)30170-6 DE-627 ger DE-627 rda eng 620 DE-600 53.73 bkl Xia, Wei verfasserin aut Collaborative beamforming via diffusion adaptation based on tensor over array networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency. Distributed beamforming Adaptive networks Diffusion strategies Tensor Xia, Guoqing verfasserin aut Li, Jinghua verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 106 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 106 |
| allfieldsGer |
10.1016/j.dsp.2020.102825 doi (DE-627)ELV004735838 (ELSEVIER)S1051-2004(20)30170-6 DE-627 ger DE-627 rda eng 620 DE-600 53.73 bkl Xia, Wei verfasserin aut Collaborative beamforming via diffusion adaptation based on tensor over array networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency. Distributed beamforming Adaptive networks Diffusion strategies Tensor Xia, Guoqing verfasserin aut Li, Jinghua verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 106 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 106 |
| allfieldsSound |
10.1016/j.dsp.2020.102825 doi (DE-627)ELV004735838 (ELSEVIER)S1051-2004(20)30170-6 DE-627 ger DE-627 rda eng 620 DE-600 53.73 bkl Xia, Wei verfasserin aut Collaborative beamforming via diffusion adaptation based on tensor over array networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency. Distributed beamforming Adaptive networks Diffusion strategies Tensor Xia, Guoqing verfasserin aut Li, Jinghua verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 106 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 106 |
| language |
English |
| source |
Enthalten in Digital signal processing 106 volume:106 |
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Xia, Wei @@aut@@ Xia, Guoqing @@aut@@ Li, Jinghua @@aut@@ |
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Xia, Wei |
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collaborative beamforming via diffusion adaptation based on tensor over array networks |
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Collaborative beamforming via diffusion adaptation based on tensor over array networks |
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Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency. |
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Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency. |
| abstract_unstemmed |
Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV004735838</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524164826.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230503s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.dsp.2020.102825</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV004735838</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1051-2004(20)30170-6</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xia, Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Collaborative beamforming via diffusion adaptation based on tensor over array networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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">Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. 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