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handle: 10609/148992
5G mobile networks are envisioned to sub- stantiate new vertical services with diverse performance requirements. Slicing in the Radio Access Network (RAN) promises an efficient solution for these diversified needs of 5G networks, which foresees the separation of the Base Station (BS) functionality between the Central Unit (CU) and the distributed Remote Radio Heads (RRHs). In this paper, we formulate a Mixed Integer Programming (MIP) framework that maximizes the throughput by jointly selecting the optimal Functional Split (FS) and the routing path from a connected User Equipment (UE) to the CU, while satisfying the agreed Service Level Agreements (SLAs) of each service. Furthermore, we propose an effective heuristic, SlicedRAN, which creates isolated RAN slices premised on the ser- vice requirements connected through a Fronthaul/Backhaul (FH/BH) network and obtains near-optimal solutions in a short computing time compared to the MIP framework. Our results show that there is a trade-off between the architecture of the FH/BH network and the minimum SLA of each slice, which provides a solution to efficiently design a virtualized network infrastructure. According to the results, the SlicedRAN outperforms existing State-of-the-Art (SoA) up to 112% gain in throughput. Results are shown close to the optimal results, with a loss below 5%.
functional split, crosshaul, RAN slic- ing, network virtualization, 000, 5G mobile communication , Radio access networks , Bandwidth , Throughput , Quality of service , Network topology , Virtualization, 5G
functional split, crosshaul, RAN slic- ing, network virtualization, 000, 5G mobile communication , Radio access networks , Bandwidth , Throughput , Quality of service , Network topology , Virtualization, 5G
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 27 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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