Association of networked flying platforms with small cells for network centric 5G+ C-RAN

5G+ systems expect enhancement in data rate and coverage area under limited power constraint. Such requirements can be fulfilled by the densification of small cells (SCs). However, a major challenge is the management of fronthaul links connected with an ultra dense network of SCs. A cost effective and scalable idea of using network flying platforms (NFPs) is employed here, where the NFPs are used as fronthaul hubs that connect the SCs to the core network. The association problem of NFPs and SCs is formulated considering a number of practical constraints such as backhaul data rate limit, maximum supported links and bandwidth by NFPs and quality of service requirement of the system. The network centric case of the system is considered that aims to maximize the number of associated SCs without any biasing, i.e., no preference for high priority SCs. Then, two new efficient greedy algorithms are designed to solve the presented association problem. Numerical results show a favorable performance of our proposed methods in comparison to exhaustive search.Comment: Submitted to IEEE PIMRC 2017, 7 pages and 5 figure

Association of networked flying platforms with small cells for network centric 5G+ C-RAN

5G+ systems expect enhancement in data rate and coverage area under limited power constraint. Such requirements can be fulfilled by the densification of small cells (SCs). However, a major challenge is the management of fronthaul links connected to an ultra dense network of SCs. A cost effective and scalable idea of using network flying platforms (NFPs) is employed here, where the NFPs are used as fronthaul hubs that connect the SCs to the core network. The association problem of NFPs and SCs is formulated considering a number of practical constraints such as backhaul data rate limit, maximum supported links and bandwidth by NFPs and quality of service requirement of the system. The network centric case of the system is considered that aims to maximize the number of associated SCs without any biasing, i.e., no preference for high priority SCs. Then, two new efficient greedy algorithms are designed to solve the presented association problem. Numerical results show a favorable performance of our proposed methods in comparison to exhaustive search. 2017 IEEE.Scopu