Sista Venkata Surya Prasad
Sista Venkata Surya Prasad
*This is an algorithmically generated author record based on publicly available information.
Sista Venkata Surya Prasad has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Ganesh Miriyala
Department of ECE, MLR Institute of Technology, Hyderabad 500043, Telangana, India
1 shared publication - Sivaramakrishna Yechuri
Department of ECE, MLR Institute of Technology, Hyderabad 500043, Telangana, India
1 shared publication - Sudeep Sharma
ECE Department Indian Institute of Information Technology Surat India
1 shared publication - T. S. Arulananth
MLR Institute of Technology, Hyderabad, India
1 shared publication - Vijaya Kumar Velpula
Department of ECE, MLR Institute of Technology, Hyderabad 500043, Telangana, India
1 shared publication
Latest Publications
- Open Access
- 17 March 2026
- Published by MDPI AG in Engineering Proceedings
- Vol. 124 (1) , 101
High-Gain Artificial Magnetic Conductor-Integrated Antenna for 5G Communication Systems
This article presents a meta-surface-based antenna configuration aimed at enhancing the gain performance for millimeter-wave wireless communication systems. The proposed structure consists of a rectangular meta-surface with circular cut-outs placed beneath a rectangular ring to improve the electromagnetic characteristics of the antenna. A rectangular monopole antenna is designed to operate at dual frequency bands around 38 GHz and 43 GHz. To further enhance radiation performance, Artificial Magnetic Conductor (AMC) structures are incorporated beneath the antenna element. The AMC surface improves the radiation efficiency and stabilizes the antenna characteristics by providing in-phase reflection near the operating frequencies. Simulation results demonstrate that the integration of the AMC structure significantly enhances the antenna gain and impedance matching performance. In particular, the incorporation of a AMC array increases the antenna gain from approximately 3.4 dB to 6.4 dB while maintaining stable reflection coefficient characteristics. The proposed design demonstrates improved gain performance and compact structure, making it a promising candidate for millimeter-wave wireless communication applications.- 17 March 2026
- Published by MDPI AG in Engineering Proceedings
- Vol. 124 (1) , 101
Open AccessHigh-Gain Artificial Magnetic Conductor-Integrated Antenna for 5G Communication Systems
This article presents a meta-surface-based antenna configuration aimed at enhancing the gain performance for millimeter-wave wireless communication systems. The proposed structure consists of a rectangular meta-surface with circular cut-outs placed beneath a rectangular ring to improve the electromagnetic characteristics of the antenna. A rectangular monopole antenna is designed to operate at dual frequency bands around 38 GHz and 43 GHz. To further enhance radiation performance, Artificial Magnetic Conductor (AMC) structures are incorporated beneath the antenna element. The AMC surface improves the radiation efficiency and stabilizes the antenna characteristics by providing in-phase reflection near the operating frequencies. Simulation results demonstrate that the integration of the AMC structure significantly enhances the antenna gain and impedance matching performance. In particular, the incorporation of a AMC array increases the antenna gain from approximately 3.4 dB to 6.4 dB while maintaining stable reflection coefficient characteristics. The proposed design demonstrates improved gain performance and compact structure, making it a promising candidate for millimeter-wave wireless communication applications. - 30 October 2023
- Published by Springer Nature in Arabian Journal for Science and Engineering
- Vol. 49 (5) , 7487-7505
Identification of Systems Having Unstable Dynamics and Time Delays Using Delayed Recurrent Neural Networks
- 30 October 2023
- Published by Springer Nature in Arabian Journal for Science and Engineering
- Vol. 49 (5) , 7487-7505
Identification of Systems Having Unstable Dynamics and Time Delays Using Delayed Recurrent Neural Networks