Δημοσιεύσεις

@article{Kakkavas2020740,

title = {A Software Defined Radio Cross-Layer Resource Allocation Approach for Cognitive Radio Networks: From Theory to Practice},

author = {G. Kakkavas and K. Tsitseklis and V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091103528&doi=10.1109%2fTCCN.2019.2963869&partnerID=40&md5=f3ef66fa9a91cbcdb443849280fc72ab},

doi = {10.1109/TCCN.2019.2963869},

issn = {23327731},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Transactions on Cognitive Communications and Networking},

volume = {6},

number = {2},

pages = {740-755},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Software Defined Radio (SDR)-enabled cognitive radio network architectures are expected to play an important role in the future 5G networks. Despite the increased research interest, the current implementations are of small-scale and provide limited functionality. In this paper, we contribute towards the alleviation of the limitations in SDR deployments by developing and evaluating a resource allocation approach for cognitive radios implemented with SDR technology over two testbeds of the ORCA federation. Resource allocation is based on a Markov Random Field (MRF) framework realizing a distributed cross-layer computation for the secondary nodes of the cognitive radio network. The proposed framework implementation consists of self-contained modules developed in GNU Radio realizing cognitive functionalities, such as spectrum sensing, collision detection, etc. We demonstrate the feasibility of the MRF based resource allocation approach and provide extensive results and performance analysis that highlight its key features. The latter provide useful insights about the advantages of our framework, while allowing to pinpoint current technological barriers of broader interest. © 2015 IEEE.},

note = {cited By 66},

keywords = {5G mobile communication systems; Analog circuits; Cognitive radio; Computation theory; Magnetorheological fluids; Markov processes; Network architecture; Open source software; Radio; Radio receivers; Resource allocation; Structural frames, Cognitive radio network; Collision detection; Cross layer resource allocations; Markov Random Fields; Performance analysis; Research interests; Software-defined radios; Technological barriers, Software radio},

pubstate = {published},

tppubtype = {article}

}

@article{Stamou2019,

title = {Autonomic Network Management and cross-layer optimization in Software Defined radio environments},

author = {A. Stamou and G. Kakkavas and K. Tsitseklis and V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061190116&doi=10.3390%2ffi11020037&partnerID=40&md5=458dca75a6acc2729e6c5b6043737655},

doi = {10.3390/fi11020037},

issn = {19995903},

year  = {2019},

date = {2019-01-01},

journal = {Future Internet},

volume = {11},

number = {2},

publisher = {MDPI AG},

abstract = {The demand for Autonomic Network Management (ANM) and optimization is as intense as ever, even though significant research has been devoted towards this direction. This paper addresses such need in Software Defined (SDR) based Cognitive Radio Networks (CRNs). We propose a new framework forANMand network reconfiguration combining Software Defined Networks (SDN) with SDR via Network Function Virtualization (NFV) enabled Virtual Utility Functions (VUFs). This is the first approach combining ANM with SDR and SDN via NFV, demonstrating how these state-of-the-art technologies can be effectively combined to achieve reconfiguration flexibility, improved performance and efficient use of available resources. In order to show the feasibility of the proposed framework, we implemented its main functionalities in a cross-layer resource allocation mechanism for CRNs over real SDR testbeds provided by the Orchestration and Reconfiguration Control Architecture (ORCA) EU project. We demonstrate the efficacy of our framework, and based on the obtained results, we identify aspects that can be further investigated for improving the applicability and increasing performance of our broader framework. © 2019 by the authors.},

note = {cited By 10},

keywords = {Analog circuits; Cognitive radio; Network function virtualization; Network management; Radio; Radio receivers; Resource allocation; Software defined networking; Testbeds; Transfer functions; Virtual reality, Autonomic network management; Cognitive radio networks (CRNs); Cross layer optimization; Cross layer resource allocations; Network re-configuration; Reconfiguration control; Software-defined radios; State-of-the-art technology, Software radio},

pubstate = {published},

tppubtype = {article}

}