Source: http://jmoe.org/index.php/jmoe/article/view/1484
Timestamp: 2019-04-22 06:48:42+00:00

Document:
The accelerated data and apps growth represents significant challenges to the next generation of mobile networks. Amongst them, it is highlighted the necessity for a co-existence of new and old patterns during the transition of architectures. Thus, this paper has investigated solutions for offloading into a hybrid architecture, also known as H-CRAN (Heterogeneous Cloud Radio Access Network Architecture), that centralizes processing and searches a better use of the network resources. The strategy of optimization was analyzed through the evolutive algorithm PSO (Particle Swarm Optimization), in order to find a suboptimal solution to the allocation of two levels (TLA) in the H-CRAN architecture and another one based on FIFO (First In, First Out), for benchmarking purposes. SNR (Noise Interference Signal) average, Maximum Bit Rate, the number of users with or without connections and number of connections in RRHs and macro were used as performance measurements. Through the results, it was noticed an improvement of approximately 60% in the Maximum Bit Rate when compared to the traditional approach, enabling a better service to the users.
DUAN, Xiaoyu; WANG, Xianbin. Authentication handover and privacy protection in 5G hetnets using software-defined networking. IEEE Communications Magazine, v. 53, n. 4, p. 28-35, 2015.
RAZA, Muhammad Rehan et al. Demonstration of dynamic resource sharing benefits in an optical C-RAN. Journal of Optical Communications and Networking, v. 8, n. 8, p. 621-632, 2016.
KHAN, Muhammad; ALHUMAIMA, Raad S.; AL-RAWESHIDY, Hamed S. QoS-Aware Dynamic RRH Allocation in a Self-Optimized Cloud Radio Access Network With RRH Proximity Constraint. IEEE Transactions on Network and Service Management, v. 14, n. 3, p. 730- 744, 2017.
WU, Jun et al. Cloud radio access network (C-RAN): a primer. IEEE Network, v. 29, n. 1, p. 35-41, 2015.
ZHANG, Biling et al. Resource Allocation for 5G Heterogeneous Cloud Radio Access Networks with D2D Communication: A Matching and Coalition Approach. IEEE Transactions on Vehicular Technology, 2018.
MAROTTA, Marcelo A. et al. Design considerations for software defined wireless networking in heterogeneous cloud radio access networks. Journal of Internet Services and Applications, v. 8, n. 1, p. 18, 2017.
KHAN, M.; ALHUMAIMA, R. S.; AL-RAWESHIDY, H. S. Quality of service aware dynamic BBU-RRH mapping in cloud radio access network. In: Emerging Technologies (ICET), 2015 International Conference on. IEEE, 2015. p. 1-5.
DA PAIXAO, ErmÃ­nio Augusto Ramos et al. Optimized load balancing by dynamic BBU-RRH mapping in C-RAN architecture. In: Fog and Mobile Edge Computing (FMEC), 2018 Third International Conference on. IEEE, 2018. p. 100-104.
LEE, Ying Loong et al. Dynamic Network Slicing for Multitenant Heterogeneous Cloud Radio Access Networks. IEEE Transactions on Wireless Communications, v. 17, n. 4, p. 2146-2161, 2018.
TRAN, Huu Q. et al. On the energy efficiency of NOMA for wireless backhaul in multi-tier heterogeneous CRAN. 2017.WANG, Ke; ZHAO, ZhongYuan; CEN, Yi. Allocating Multi-type Resources in Heterogeneous Cloud Radio Access Networks. Mobile Networks and Applications, v. 23, n. 3, p. 611-623, 2018.
KHAN, M.; FAKHRI, Zainab H.; AL-RAWESHIDY, Hamed S. Semistatic Cell Differentiation and Integration With Dynamic BBU-RRH Mapping in Cloud Radio Access Network. IEEE Transactions on Network and Service Management, v. 15, n. 1, p. 289-303, 2018.
PENG, Mugen et al. System architecture and key technologies for 5G heterogeneous cloud radio access networks. IEEE network, v. 29, n. 2, p. 6-14, 2015.
CASTRO, Bruno Souza Lyra et al. Modelo de propagacÂ¸ao para redes Ëœ sem fio fixas na banda de 5, 8 GHZ em cidades tÂ´Ä±picas da regiaoËœ amazonica. 2010.
SULYMAN, Ahmed Iyanda et al. Radio propagation path loss models for 5G cellular networks in the 28 GHz and 38 GHz millimeter-wave bands. IEEE Communications Magazine, v. 52, n. 9, p. 78-86, 2014.
SULYMAN, Ahmed Iyanda et al. Directional radio propagation path loss models for millimeter-wave wireless networks in the 28-, 60-, and 73-GHz bands. IEEE Transactions on Wireless Communications, v. 15, n. 10, p. 6939-6947, 2016.
DAHLMAN, Erik; PARKVALL, Stefan; SKOLD, Johan. 4G: LTE/LTE advanced for mobile broadband. Academic press, 2013.
Shannon C E, "Communication in the Presence of Noise``. Proceedings of the IRE, vol. 37 pp. 10-21, 1949.
PHAIWITTHAYAPHORN, Prapassorn et al. Cell throughput based sleep control scheme for heterogeneous cellular networks. In: Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2017 14th International Conference on. IEEE, 2017. p. 584-587.

References: v. 
 v. 
 v. 
 v. 
 v. 
 v. 
 v. 
 v. 
 v. 
 v. 
 v.