Nikolaos Prodromos

@conference{nokeyc,

title = {Simulation – based Beamforming Optimization in Moving Drones},

author = {Christos Bouras and Damianos Diasakos and Apostolos Gkamas and Vasileios Kokkinos and Philippos Pouyioutas and Nikolaos Prodromos},

url = { https://telematics.upatras.gr/wp-content/uploads/2024/10/WMNC2024_cameraready_final.pdf},

year  = {2024},

date = {2024-11-12},

urldate = {2024-11-12},

booktitle = {15th IFIP Wireless and Mobile Computing (IFIP WMNC), November 11 – 12, 2024, Venice, Italy, pp 1-6 (to appear)

},

pages = {1 - 6},

abstract = {The integration of drone technology with 5G networks presents novel opportunities for enhancing wireless communication systems. This paper explores the application of beamforming optimization techniques in dynamic environments, specifically focusing on moving drones in a simulated environment based on the DeepMIMO O1 scenario. By leveraging the unique properties of the O1 drone setup of DeepMIMO simulation environment, which simulates realistic urban mobility patterns at millimeter-wave (mmWave) frequencies, we propose a novel beamforming algorithm designed to optimize the signal quality and stability in highly mobile aerial networks. Key performance metrics used in this study include Signal-to-Noise Ratio (SNR), battery consumption, and power consumption of both the drones and the base station. Our findings indicate that the adaptive beamforming algorithm not only enhances the SNR and reduces power consumption but also optimizes battery usage compared 

to conventional beamforming methods. This study enhances the understanding of mmWave beamforming dynamics in aerial scenarios but also lays the groundwork for future advancements 

in drone-based communication networks.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokeyb,

title = {Optimizing Network Slices: A Comparative Analysis of Allocation Algorithms for 5G Environments},

author = {Nikolaos Prodromos and Damianos Diasakos and Vasileios Kokkinos and Apostolos Gkamas and Philippos Pouyioutas and Christos Bouras},

url = {https://telematics.upatras.gr/wp-content/uploads/2024/10/FCN2024_camera_ready.pdf},

year  = {2024},

date = {2024-09-30},

urldate = {2024-09-30},

booktitle = {2024 International Conference on Future Communications and Networks (FCN 2024), November 18 - 22, 2024, Valetta, Malta (to appear)},

number = {1- 6},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{4662,

title = {Dynamic Bandwidth Allocation in MIMO 5G Networks},

author = {Nikolaos Prodromos and Damianos Diasakos and Vasileios Kokkinos and Apostolos Gkamas and Christos Bouras and Philippos Pouyioutas},

url = {https://telematics.upatras.gr/wp-content/uploads/2024/06/IWCMC2024_CR.pdf},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {The 20th International Wireless Communications & Mobile Computing Conference (IWCMC 2024), May 27 – 31, 2024, Ayia Napa, Cyprus},

pages = {97 - 102},

abstract = {The advent of 5G technology has ushered in a new era of wireless communication, characterized by its promise of high data rates, low latency, and enhanced connectivity. In this context, Multiple-Input Multiple-Output (MIMO) systems have emerged as a key enabler, leveraging advanced antenna arrays to simultaneously serve multiple users with increased spectral efficiency. This paper investigates the dynamic resource allocation problem in a MIMO 5G environment, where each user possesses distinct bandwidth requirements. The focus is on optimizing user allocation while considering the limited bandwidth and user capacity of base stations. By harnessing the 

power of deep learning techniques, the proposed solution aims to efficiently manage the allocation of users to base station antennas, thereby maximizing overall network performance while accommodating heterogeneous user demands.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}