�ݺ�ߣshows by User: aelnashar

�ݺ�ߣshows by User: aelnashar / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: aelnashar / Sun, 30 Jan 2022 10:51:21 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: aelnashar White paper du_huawei_for_5_g_core_network_evolution /slideshow/white-paper-duhuaweifor5gcorenetworkevolution/251079668 whitepaperduhuaweifor5gcorenetworkevolution-220130105122
5G Core Network ]]>
5G Core Network ]]> Sun, 30 Jan 2022 10:51:21 GMT /slideshow/white-paper-duhuaweifor5gcorenetworkevolution/251079668 aelnashar@slideshare.net(aelnashar) White paper du_huawei_for_5_g_core_network_evolution aelnashar 5G Core Network <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whitepaperduhuaweifor5gcorenetworkevolution-220130105122-thumbnail.jpg?width=120&height=120&fit=bounds" /> 5G Core Network

White paper du_huawei_for_5_g_core_network_evolution from Dr. Ayman Elnashar, PhD

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0 Iot Evolution /slideshow/iot-evolution-251079666/251079666 iotevoluation-220130105038
Iot Evolution]]>
Iot Evolution]]> Sun, 30 Jan 2022 10:50:38 GMT /slideshow/iot-evolution-251079666/251079666 aelnashar@slideshare.net(aelnashar) Iot Evolution aelnashar Iot Evolution <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iotevoluation-220130105038-thumbnail.jpg?width=120&height=120&fit=bounds" /> Iot Evolution

Iot Evolution from Dr. Ayman Elnashar, PhD

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0 Defining telco cloud at du /slideshow/defining-telco-cloud-at-du/251079664 definingtelcocloudatduwhitepaperfinal-220130105005
Defining telco cloud at du]]>
Defining telco cloud at du]]> Sun, 30 Jan 2022 10:50:04 GMT /slideshow/defining-telco-cloud-at-du/251079664 aelnashar@slideshare.net(aelnashar) Defining telco cloud at du aelnashar Defining telco cloud at du <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/definingtelcocloudatduwhitepaperfinal-220130105005-thumbnail.jpg?width=120&height=120&fit=bounds" /> Defining telco cloud at du

Defining telco cloud at du from Dr. Ayman Elnashar, PhD

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0 5G design concepts /slideshow/5g-design-concepts/251079660 5gdesignconceptsv14-220130104931
5G design concepts]]>
5G design concepts]]> Sun, 30 Jan 2022 10:49:31 GMT /slideshow/5g-design-concepts/251079660 aelnashar@slideshare.net(aelnashar) 5G design concepts aelnashar 5G design concepts <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5gdesignconceptsv14-220130104931-thumbnail.jpg?width=120&height=120&fit=bounds" /> 5G design concepts

5G design concepts from Dr. Ayman Elnashar, PhD

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0 IoT /aelnashar/iot-251079659 iotwf-whitepaper-transformation-next-generation-iot-sp-220130104852
IoT]]>
IoT]]> Sun, 30 Jan 2022 10:48:51 GMT /aelnashar/iot-251079659 aelnashar@slideshare.net(aelnashar) IoT aelnashar IoT <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iotwf-whitepaper-transformation-next-generation-iot-sp-220130104852-thumbnail.jpg?width=120&height=120&fit=bounds" /> IoT

IoT from Dr. Ayman Elnashar, PhD

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0 Eitc Infrastructure outlook 2021 /slideshow/eitc-infrastructure-outlook-2021/251079656 eitcinfrastructureoutlook2021wpenfinal-220130104812
Eitc infrastructure outlook 2021]]>
Eitc infrastructure outlook 2021]]> Sun, 30 Jan 2022 10:48:12 GMT /slideshow/eitc-infrastructure-outlook-2021/251079656 aelnashar@slideshare.net(aelnashar) Eitc Infrastructure outlook 2021 aelnashar Eitc infrastructure outlook 2021 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eitcinfrastructureoutlook2021wpenfinal-220130104812-thumbnail.jpg?width=120&height=120&fit=bounds" /> Eitc infrastructure outlook 2021

Eitc Infrastructure outlook 2021 from Dr. Ayman Elnashar, PhD

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0 Practical aspects of lte design and deployment /slideshow/practical-aspects-of-lte-design-and-deployment-251079654/251079654 practicalaspectsofltedesignanddeployment-220130104724
Practical aspects of LTE design and deployment]]>
Practical aspects of LTE design and deployment]]> Sun, 30 Jan 2022 10:47:24 GMT /slideshow/practical-aspects-of-lte-design-and-deployment-251079654/251079654 aelnashar@slideshare.net(aelnashar) Practical aspects of lte design and deployment aelnashar Practical aspects of LTE design and deployment <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/practicalaspectsofltedesignanddeployment-220130104724-thumbnail.jpg?width=120&height=120&fit=bounds" /> Practical aspects of LTE design and deployment

Practical aspects of lte design and deployment from Dr. Ayman Elnashar, PhD

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0 VoLTE Performance /slideshow/volte-performance/251079651 volteperformancev23-220130104613
VoLTE Performance]]>
VoLTE Performance]]> Sun, 30 Jan 2022 10:46:13 GMT /slideshow/volte-performance/251079651 aelnashar@slideshare.net(aelnashar) VoLTE Performance aelnashar VoLTE Performance <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/volteperformancev23-220130104613-thumbnail.jpg?width=120&height=120&fit=bounds" /> VoLTE Performance

VoLTE Performance from Dr. Ayman Elnashar, PhD

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0 Sample-by-sample and block-adaptive robust constant modulus-based algorithms /slideshow/samplebysample-and-blockadaptive-robust-constant-modulusbased-algorithms/63908648 06410957-160711121943
In this study, a robust sample-by-sample linearly constrained constant modulus algorithm (LCCMA) and a robust adaptive block-Shanno constant modulus algorithm (BSCMA) are developed. The well-established quadratic inequality constraint approach is exploited to add robustness to the developed algorithms. The LCCMA algorithm is implemented using a fast steepest descent adaptive algorithm, whereas the BSCMA algorithm is realised using a modified Newton’s algorithm without the inverse of Hessian matrix estimation. The developed algorithms are exercised to cancel the multiple access interference in a loaded direct sequence code division multiple access (DS/CDMA) system. Simulations are presented in a rich multipath environment with a severe near-far effect to evaluate the robustness of the proposed DS/CDMA detectors. Finally, a comprehensive comparative analysis between the sample-by-sample and block-adaptive constant modulus-based detectors is presented. It has been demonstrated that the developed robust BSCMA detector offers rapid convergence speed and very low computational complexity, whereas the developed robust LCCMA detector engenders about 5 dB improvement in the output signal-to-interference-plus-noise ratio over the BSCMA detector.]]>
In this study, a robust sample-by-sample linearly constrained constant modulus algorithm (LCCMA) and a robust adaptive block-Shanno constant modulus algorithm (BSCMA) are developed. The well-established quadratic inequality constraint approach is exploited to add robustness to the developed algorithms. The LCCMA algorithm is implemented using a fast steepest descent adaptive algorithm, whereas the BSCMA algorithm is realised using a modified Newton’s algorithm without the inverse of Hessian matrix estimation. The developed algorithms are exercised to cancel the multiple access interference in a loaded direct sequence code division multiple access (DS/CDMA) system. Simulations are presented in a rich multipath environment with a severe near-far effect to evaluate the robustness of the proposed DS/CDMA detectors. Finally, a comprehensive comparative analysis between the sample-by-sample and block-adaptive constant modulus-based detectors is presented. It has been demonstrated that the developed robust BSCMA detector offers rapid convergence speed and very low computational complexity, whereas the developed robust LCCMA detector engenders about 5 dB improvement in the output signal-to-interference-plus-noise ratio over the BSCMA detector.]]> Mon, 11 Jul 2016 12:19:43 GMT /slideshow/samplebysample-and-blockadaptive-robust-constant-modulusbased-algorithms/63908648 aelnashar@slideshare.net(aelnashar) Sample-by-sample and block-adaptive robust constant modulus-based algorithms aelnashar In this study, a robust sample-by-sample linearly constrained constant modulus algorithm (LCCMA) and a robust adaptive block-Shanno constant modulus algorithm (BSCMA) are developed. The well-established quadratic inequality constraint approach is exploited to add robustness to the developed algorithms. The LCCMA algorithm is implemented using a fast steepest descent adaptive algorithm, whereas the BSCMA algorithm is realised using a modified Newton’s algorithm without the inverse of Hessian matrix estimation. The developed algorithms are exercised to cancel the multiple access interference in a loaded direct sequence code division multiple access (DS/CDMA) system. Simulations are presented in a rich multipath environment with a severe near-far effect to evaluate the robustness of the proposed DS/CDMA detectors. Finally, a comprehensive comparative analysis between the sample-by-sample and block-adaptive constant modulus-based detectors is presented. It has been demonstrated that the developed robust BSCMA detector offers rapid convergence speed and very low computational complexity, whereas the developed robust LCCMA detector engenders about 5 dB improvement in the output signal-to-interference-plus-noise ratio over the BSCMA detector. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/06410957-160711121943-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this study, a robust sample-by-sample linearly constrained constant modulus algorithm (LCCMA) and a robust adaptive block-Shanno constant modulus algorithm (BSCMA) are developed. The well-established quadratic inequality constraint approach is exploited to add robustness to the developed algorithms. The LCCMA algorithm is implemented using a fast steepest descent adaptive algorithm, whereas the BSCMA algorithm is realised using a modified Newton’s algorithm without the inverse of Hessian matrix estimation. The developed algorithms are exercised to cancel the multiple access interference in a loaded direct sequence code division multiple access (DS/CDMA) system. Simulations are presented in a rich multipath environment with a severe near-far effect to evaluate the robustness of the proposed DS/CDMA detectors. Finally, a comprehensive comparative analysis between the sample-by-sample and block-adaptive constant modulus-based detectors is presented. It has been demonstrated that the developed robust BSCMA detector offers rapid convergence speed and very low computational complexity, whereas the developed robust LCCMA detector engenders about 5 dB improvement in the output signal-to-interference-plus-noise ratio over the BSCMA detector.

Sample-by-sample and block-adaptive robust constant modulus-based algorithms from Dr. Ayman Elnashar, PhD

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0 Low-complexity robust adaptive generalized sidelobe canceller detector for DS/CDMA systems /slideshow/lowcomplexity-robust-adaptive-generalized-sidelobe-canceller-detector-for-dscdma-systems/63908497 acs1088-160711121405
A novel low computational complexity robust adaptive blind multiuser detector, based on the minimum output energy (MOE) detector with multiple constraints and a quadratic inequality (QI) constraint is developed in this paper. Quadratic constraint has been a widespread approach to improve robustness against mismatch errors, uncertainties in estimating the data covariance matrix, and random perturbations in detector parameters. A diagonal loading technique is compulsory to achieve the quadratic constraint where the diagonal loading level is adjusted to satisfy the constrained value. Integrating the quadratic constraint into recursive algorithms seems to be a moot point since there is no closed-form solution for the diagonal loading term. In this paper, the MOE detector of DS/CDMA system is implemented using a fast recursive steepest descent adaptive algorithm anchored in the generalized sidelobe canceller (GSC) structure with multiple constraints and a QI constraint on the adaptive portion of the GSC structure. The Lagrange multiplier method is exploited to solve the QI constraint. An optimal variable loading technique, which is capable of providing robustness against uncertainties and mismatch errors with low computational complexity is adopted. Simulations for several mismatch and random perturbations scenarios are conducted in a rich multipath environment with near–far effect to explore the robustness of the proposed detector.]]>
A novel low computational complexity robust adaptive blind multiuser detector, based on the minimum output energy (MOE) detector with multiple constraints and a quadratic inequality (QI) constraint is developed in this paper. Quadratic constraint has been a widespread approach to improve robustness against mismatch errors, uncertainties in estimating the data covariance matrix, and random perturbations in detector parameters. A diagonal loading technique is compulsory to achieve the quadratic constraint where the diagonal loading level is adjusted to satisfy the constrained value. Integrating the quadratic constraint into recursive algorithms seems to be a moot point since there is no closed-form solution for the diagonal loading term. In this paper, the MOE detector of DS/CDMA system is implemented using a fast recursive steepest descent adaptive algorithm anchored in the generalized sidelobe canceller (GSC) structure with multiple constraints and a QI constraint on the adaptive portion of the GSC structure. The Lagrange multiplier method is exploited to solve the QI constraint. An optimal variable loading technique, which is capable of providing robustness against uncertainties and mismatch errors with low computational complexity is adopted. Simulations for several mismatch and random perturbations scenarios are conducted in a rich multipath environment with near–far effect to explore the robustness of the proposed detector.]]> Mon, 11 Jul 2016 12:14:05 GMT /slideshow/lowcomplexity-robust-adaptive-generalized-sidelobe-canceller-detector-for-dscdma-systems/63908497 aelnashar@slideshare.net(aelnashar) Low-complexity robust adaptive generalized sidelobe canceller detector for DS/CDMA systems aelnashar A novel low computational complexity robust adaptive blind multiuser detector, based on the minimum output energy (MOE) detector with multiple constraints and a quadratic inequality (QI) constraint is developed in this paper. Quadratic constraint has been a widespread approach to improve robustness against mismatch errors, uncertainties in estimating the data covariance matrix, and random perturbations in detector parameters. A diagonal loading technique is compulsory to achieve the quadratic constraint where the diagonal loading level is adjusted to satisfy the constrained value. Integrating the quadratic constraint into recursive algorithms seems to be a moot point since there is no closed-form solution for the diagonal loading term. In this paper, the MOE detector of DS/CDMA system is implemented using a fast recursive steepest descent adaptive algorithm anchored in the generalized sidelobe canceller (GSC) structure with multiple constraints and a QI constraint on the adaptive portion of the GSC structure. The Lagrange multiplier method is exploited to solve the QI constraint. An optimal variable loading technique, which is capable of providing robustness against uncertainties and mismatch errors with low computational complexity is adopted. Simulations for several mismatch and random perturbations scenarios are conducted in a rich multipath environment with near–far effect to explore the robustness of the proposed detector. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/acs1088-160711121405-thumbnail.jpg?width=120&height=120&fit=bounds" /> A novel low computational complexity robust adaptive blind multiuser detector, based on the minimum output energy (MOE) detector with multiple constraints and a quadratic inequality (QI) constraint is developed in this paper. Quadratic constraint has been a widespread approach to improve robustness against mismatch errors, uncertainties in estimating the data covariance matrix, and random perturbations in detector parameters. A diagonal loading technique is compulsory to achieve the quadratic constraint where the diagonal loading level is adjusted to satisfy the constrained value. Integrating the quadratic constraint into recursive algorithms seems to be a moot point since there is no closed-form solution for the diagonal loading term. In this paper, the MOE detector of DS/CDMA system is implemented using a fast recursive steepest descent adaptive algorithm anchored in the generalized sidelobe canceller (GSC) structure with multiple constraints and a QI constraint on the adaptive portion of the GSC structure. The Lagrange multiplier method is exploited to solve the QI constraint. An optimal variable loading technique, which is capable of providing robustness against uncertainties and mismatch errors with low computational complexity is adopted. Simulations for several mismatch and random perturbations scenarios are conducted in a rich multipath environment with near–far effect to explore the robustness of the proposed detector.

Low-complexity robust adaptive generalized sidelobe canceller detector for DS/CDMA systems from Dr. Ayman Elnashar, PhD

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0 Automatic analysis and classification of surface electromyography /slideshow/automatic-analysis-and-classification-of-surface-electromyography/55493491 p13s-151125062607-lva1-app6892
My 1st published paper on Signal Processing for EMG Signal using Neural Networks.]]>
My 1st published paper on Signal Processing for EMG Signal using Neural Networks.]]> Wed, 25 Nov 2015 06:26:07 GMT /slideshow/automatic-analysis-and-classification-of-surface-electromyography/55493491 aelnashar@slideshare.net(aelnashar) Automatic analysis and classification of surface electromyography aelnashar My 1st published paper on Signal Processing for EMG Signal using Neural Networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/p13s-151125062607-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> My 1st published paper on Signal Processing for EMG Signal using Neural Networks.

Automatic analysis and classification of surface electromyography from Dr. Ayman Elnashar, PhD

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0 Lte World Summit 2012 Ver 2 /slideshow/lte-world-summit-2012-ver-2/14561445 lteworldsummit2012ver2-13492050455902-phpapp02-121002141251-phpapp02
LTE Presentation at LTE World Summit 2012]]>
LTE Presentation at LTE World Summit 2012]]> Tue, 02 Oct 2012 14:11:29 GMT /slideshow/lte-world-summit-2012-ver-2/14561445 aelnashar@slideshare.net(aelnashar) Lte World Summit 2012 Ver 2 aelnashar LTE Presentation at LTE World Summit 2012 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lteworldsummit2012ver2-13492050455902-phpapp02-121002141251-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> LTE Presentation at LTE World Summit 2012

Lte World Summit 2012 Ver 2 from Dr. Ayman Elnashar, PhD

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0 Wideband AMR HD Voice /slideshow/wideband-amr-hd-voice/14560858 widebandamrhdvoice-13492023932825-phpapp02-121002132831-phpapp02
Testing and Validation of WB-AMR over 3G Network]]>
Testing and Validation of WB-AMR over 3G Network]]> Tue, 02 Oct 2012 13:27:24 GMT /slideshow/wideband-amr-hd-voice/14560858 aelnashar@slideshare.net(aelnashar) Wideband AMR HD Voice aelnashar Testing and Validation of WB-AMR over 3G Network <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/widebandamrhdvoice-13492023932825-phpapp02-121002132831-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Testing and Validation of WB-AMR over 3G Network

Wideband AMR HD Voice from Dr. Ayman Elnashar, PhD

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0 LTE Evolution /slideshow/lte-evolution/10422374 aymanelnashartelecoworldsummit2011-13227749709594-phpapp01-111201153139-phpapp01
Speech at Middle East Telco World Summit 29-30 Nov. 2011]]>
Speech at Middle East Telco World Summit 29-30 Nov. 2011]]> Thu, 01 Dec 2011 15:31:26 GMT /slideshow/lte-evolution/10422374 aelnashar@slideshare.net(aelnashar) LTE Evolution aelnashar Speech at Middle East Telco World Summit 29-30 Nov. 2011 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aymanelnashartelecoworldsummit2011-13227749709594-phpapp01-111201153139-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Speech at Middle East Telco World Summit 29-30 Nov. 2011

LTE Evolution from Dr. Ayman Elnashar, PhD

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0 Phd Presentation /slideshow/phd-presentation-9935627/9935627 phdpresentation-13198801469983-phpapp01-111029042616-phpapp01
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]]> Sat, 29 Oct 2011 04:23:00 GMT /slideshow/phd-presentation-9935627/9935627 aelnashar@slideshare.net(aelnashar) Phd Presentation aelnashar <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/phdpresentation-13198801469983-phpapp01-111029042616-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Phd Presentation from Dr. Ayman Elnashar, PhD

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0 Wireless Broadband Evolution /slideshow/ayman-el-nashar/8372740 aymanelnashar-13086429099346-phpapp01-110621025604-phpapp01
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]]> Tue, 21 Jun 2011 02:55:27 GMT /slideshow/ayman-el-nashar/8372740 aelnashar@slideshare.net(aelnashar) Wireless Broadband Evolution aelnashar <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aymanelnashar-13086429099346-phpapp01-110621025604-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Wireless Broadband Evolution from Dr. Ayman Elnashar, PhD

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0 Beamforming for Antenna Array /slideshow/04693974/8372680 04693974-1308642475031-phpapp02-110621030501-phpapp02
On efficient implementation of robust adaptive beamforming based on worst-case performance optimization]]>
On efficient implementation of robust adaptive beamforming based on worst-case performance optimization]]> Tue, 21 Jun 2011 02:48:19 GMT /slideshow/04693974/8372680 aelnashar@slideshare.net(aelnashar) Beamforming for Antenna Array aelnashar On efficient implementation of robust adaptive beamforming based on worst-case performance optimization <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/04693974-1308642475031-phpapp02-110621030501-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> On efficient implementation of robust adaptive beamforming based on worst-case performance optimization

Beamforming for Antenna Array from Dr. Ayman Elnashar, PhD

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0 Lte World Summit 2010 /slideshow/lte-world-summit-2010-ver-3pptx/8372674 lteworldsummit2010ver3pptx-13086424090931-phpapp02-110621024747-phpapp02
]]>
]]> Tue, 21 Jun 2011 02:47:04 GMT /slideshow/lte-world-summit-2010-ver-3pptx/8372674 aelnashar@slideshare.net(aelnashar) Lte World Summit 2010 aelnashar <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lteworldsummit2010ver3pptx-13086424090931-phpapp02-110621024747-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Lte World Summit 2010 from Dr. Ayman Elnashar, PhD

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0 Wi Max Mena Dubai Conference /slideshow/wi-max-mena-dubai-ver-3pptx/8372544 wimaxmenadubaiver3pptx-13086410579102-phpapp01-110621060005-phpapp01
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Wi Max Mena Dubai Conference from Dr. Ayman Elnashar, PhD

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0 Robust Adaptive Beamforming for Antenna Array /slideshow/04020392/865172 04020392-122995789399-phpapp02
Further study on Robust Adaptive Beamforming with optimum diagonal loading]]>
Further study on Robust Adaptive Beamforming with optimum diagonal loading]]> Mon, 22 Dec 2008 06:59:34 GMT /slideshow/04020392/865172 aelnashar@slideshare.net(aelnashar) Robust Adaptive Beamforming for Antenna Array aelnashar Further study on Robust Adaptive Beamforming with optimum diagonal loading <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/04020392-122995789399-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Further study on Robust Adaptive Beamforming with optimum diagonal loading

Robust Adaptive Beamforming for Antenna Array from Dr. Ayman Elnashar, PhD

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0 https://cdn.slidesharecdn.com/profile-photo-aelnashar-48x48.jpg?cb=1643539311 Hold B.Sc., M.Sc., and PhD degrees in Communications engineering. Innovative telecom professional with more than 18 years of experience and leadership positions in Telecom/IT industry with highly esteemed organizations. Proven track record and ability to manage start-up networks including RFP, vendor management, network strategy and design, rollout, optimization, and operation (outsource/insource models). Have been part of three major start-up operators in MENA region (Orange/Egypt, Mobily/KSA, and du/UAE) and held key leadership positions. Managed end-to-end delivery of several large-scale networks and mega projects with more than 1.5 billion USD budgets from major vendors including Eric... www.du.ae https://cdn.slidesharecdn.com/ss_thumbnails/whitepaperduhuaweifor5gcorenetworkevolution-220130105122-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/white-paper-duhuaweifor5gcorenetworkevolution/251079668 White paper du_huawei_... https://cdn.slidesharecdn.com/ss_thumbnails/iotevoluation-220130105038-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/iot-evolution-251079666/251079666 Iot Evolution https://cdn.slidesharecdn.com/ss_thumbnails/definingtelcocloudatduwhitepaperfinal-220130105005-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/defining-telco-cloud-at-du/251079664 Defining telco cloud a...