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search.filters.applied.f.access: openAccess × DDC: 004 × DDC: 620 × Publisher: Heidelberg : Springer ×

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    5G transport network requirements for the next generation fronthaul interface
    (Heidelberg : Springer, 2017) Bartelt, J.; Vucic, N.; Camps-Mur, D.; Garcia-Villegas, E.; Demirkol, I.; Fehske, A.; Grieger, M.; Tzanakaki, A.; Gutiérrez, J.; Grass, E.; Lyberopoulos, G.; Fettweis, G.
    To meet the requirements of 5G mobile networks, several radio access technologies, such as millimeter wave communications and massive MIMO, are being proposed. In addition, cloud radio access network (C-RAN) architectures are considered instrumental to fully exploit the capabilities of future 5G RANs. However, RAN centralization imposes stringent requirements on the transport network, which today are addressed with purpose-specific and expensive fronthaul links. As the demands on future access networks rise, so will the challenges in the fronthaul and backhaul segments. It is hence of fundamental importance to consider the design of transport networks alongside the definition of future access technologies to avoid the transport becoming a bottleneck. Therefore, we analyze in this work the impact that future RAN technologies will have on the transport network and on the design of the next generation fronthaul interface. To understand the especially important impact of varying user traffic, we utilize measurements from a real-world 4G network and, taking target 5G performance figures into account, extrapolate its statistics to a 5G scenario. With this, we derive both per-cell and aggregated data rate requirements for 5G transport networks. In addition, we show that the effect of statistical multiplexing is an important factor to reduce transport network capacity requirements and costs. Based on our investigations, we provide guidelines for the development of the 5G transport network architecture.
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    Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization
    (Heidelberg : Springer, 2021) Goodarzi, Meysam; Cvetkovski, Darko; Maletic, Nebojsa; Gutiérrez, Jesús; Grass, Eckhard
    Clock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively.