spatial radio resources management for Heterogeneous 5G Millimeter wave Networks | ||
| Menoufia Journal of Electronic Engineering Research | ||
| Article 3, Volume 31, Issue 2, July 2022, Pages 21-25 PDF (1014.38 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjeer.2022.136697.1054 | ||
| Authors | ||
| ahmed ali rosas* 1; Mona Shokair2; moawad Ibrhim3 | ||
| 1Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt | ||
| 2Dept. of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering, Menoufia University, Menoufia, Egypt. | ||
| 3Dept. of Electronics and electrical communication, Faculty of electronic Engineering, Menoufia University, Egypt. | ||
| Abstract | ||
| The availability of abundant Spectrum resources, power consumption and inter cell interference can be assumed as a bulky challenges towards meeting the high capacity demands and coverage resources expected in fifth-generation 5G technologies. By integrating Millimeter wave (mm-wave) into 5G technologies, allowing a huge number of links can be transmitted concurrently by reusing the same spectrum and providing a high density of access nodes. Also, flexible backhauling and access wireless links can be considered for heterogeneous access 5G technologies with a high directional antenna arrays. The scheme proposed in this paper can significantly enhance network throughput in mm-wave 5G heterogeneous networks by utilizing a genetic based algorithm GA for optimally power and radio resources management based on spectrum sharing with time and space-division multiple access. The proposed algorithm allows backhaul and access links with /without conflicting flows to be transmitted simultaneously without/with interference. Through a comprehensive simulations, we show that the proposed genetic algorithm achieves significant enhancement in terms of user throughput and resources utilization. | ||
| Keywords | ||
| 5G; mm-wave; heterogeneous network; backhauling; genetic algorithm | ||
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