Browsing by Author "Both, Cristiano Bonato"

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    AI-driven F-RANs: Integrating Decision Making Considering Different Time Granularities
    DeAlmeida, Jonathan M.; DaSilva, Luiz A.; Both, Cristiano Bonato; Ralha, Celia G.; Marotta, Marcelo A. (IEEE, 2021-06-07)
    Cloud and fog-based networks are promising paradigms for vehicular and mobile networks. Fog Radio Access Networks (F-RANs), in particular, can offload computation tasks to the network edge and reduce the latency. Artificial Intelligence (AI) techniques can be used in F-RANs to achieve, for example, enhanced energy efficiency and increased throughput. Nonetheless, the appropriate technique selection must consider the different time granularities at which decision-making occurs in F-RANs. We discuss the benefits and challenges of implementing an AI-driven F-RAN considering different timescales, highlighting key Machine Learning (ML) techniques for each granularity. Finally, we discuss the challenges and opportunities to integrate different ML solutions in F-RANs.
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    Flexible fine-grained baseband processing with network functions virtualization: Benefits and impacts
    Kist, Maicon; Wickboldt, Juliano Araujo; Granville, Lisandro Zambenedetti; Rochol, Juergen; DaSilva, Luiz A.; Both, Cristiano Bonato (Elsevier, 2019-03-14)
    The increasing demand for wireless broadband connectivity is leading mobile network operators towards new means to expand their infrastructures efficiently and without increasing the cost of operation. Network Functions Virtualization (NFV) is a step towards virtualization-based, low-cost flexible and adaptable networking services. In the context of centralized baseband architectures, virtualization is already employed to run baseband processing units as software on top of conventional data center hardware. However, current virtualization solutions consider atomic virtualization, i.e., single virtual machines implementing all baseband functionalities. In this article, we propose the fine-grained virtualization of baseband processing to achieve a more flexible distribution of the processing workload in centralized architectures. We also evaluate the benefits of our approach in terms of (i) the bandwidth requirements for each fine-grained distribution option, (ii) the latency experienced by mobile users for each fine-grained distribution option, and (iii) the total CPU usage of each fine-grained baseband processing function.
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    SDR Virtualization in Future Mobile Networks: Enabling Multi-Programmable Air-Interfaces
    Kist, Maicon; Rochol, Juergen; DaSilva, Luiz A.; Both, Cristiano Bonato (IEEE, 2018-01-01)
    The fifth generation of mobile networks is envisioned to provide connectivity services to a multitude of devices with vastly different requirements. Current mobile systems rely on inflexible hardware-based RF front-end that provide a “onesize- fits-all“ air-interface. Instead, future mobile networks should be flexible, providing different air-interfaces for particular users and applications. In this paper, we present HyDRA, a softwaredefined- radio virtualization layer that enables the execution of multiple programmable air-interfaces on top of one RF front-end. Our solution multiplexes digitized IQ signal samples of multiple virtual radios into a single stream. We have implemented HyDRA and experimentally evaluate its performance in a scenario that considers a base station executing LTE and NB-IoT VRs. Results obtained show that HyDRA is able to efficiently multiplex these two technologies, while the computational analysis shows that HyDRA is not CPU-intensive and can run in standard, commodity computers. We also show that HyDRA is a promising framework to enable RRH slicing, multi-radio access networks, and flexible multi-tenant networks.