4 research outputs found

    WiBACK: A back-haul network architecture for 5G networks

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    Recently both academic and industry worlds has started to define the successor of Long Term Evolution (LTE), so-called 5G networks, which will most likely appear by the end of the decade. It is widely accepted that those 5G networks will have to deal with significantly more challenging requirements in terms of provided bandwidth, latency and supported services. This will lead to not only modifications in access and parts of core networks, but will trigger changes throughout the whole network, including the Back-haul segment. In this work we present our vision of a 5G Back-haul network and identify the associated challenges. We then describe our Wireless Backhaul (WiBACK) architecture, which implements Software Defined Network (SDN) concepts and further extends them into the wireless domain. Finally we present a brief overview of our pilot installations before we conclude.This work has been supported by the BATS research project which is funded by the European Union Seventh Framework Programme under contract n317533

    Link calibration and property estimation in self-managed wireless back-haul networks

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    Rural areas often lack affordable broadband Internet connectivity, mainly due to the CAPEX and especially the OPEX of traditional wireless carrier equipment, the vast and sparsely populated areas and, notably, the lack of trained personal. Addressing these issues we have developed a self-managed heterogeneous Wireless Back-Haul (WiBACK) architecture which may be deployed to complement or even replace traditional operator equipment. To optimally utilize fixed wireless point-to-point connectivity, its configuration is to be adjusted properly to the characteristics of the wireless channel. Due to lack of trained personal, time constraints during rapid temporary deployments or run-time network reconfigurations, this task must be automated. Some technologies already provide built-in ranging mechanisms, while others require external, often manual configuration. Such mechanisms should optimally exploit the individual PHY and MAC configuration options. The resulting link proper ties, such as capacity and latency, are utilized to optimally allocate resources for QoS-aware Pipes. Accordingly, in this paper, we present the AI Radio CalibrateLink primitive, discuss its crucial architectural role in separating spectrum from capacity management and present evaluation results of our resource model for IEEE 802.11a links
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