SDN-based QoS provisioning and interference management in heterogeneous CRN

SDN-based QoS provisioning and interference management in heterogeneous CRN. Kakalou, I., Psannis, K., Siakavara, K., Goudos, S., K., & Ishibashi, Y. In 2018 7th International Conference on Modern Circuits and Systems Technologies, MOCAST 2018, pages 1-4, 2018.
doi abstract bibtex

Cognitive Radio is a promising technology to maximize spectrum efficiency that can be applied to dense residential infrastructure and heterogeneous networks. This paper proposes a Software Defined Networking (SDN) system model for coordinated adaptive spectrum sharing and interference management; the SDN system model leverages the fact that Primary User (PU) network traffic is bursty with an optimal reinforcement learning scheme and takes into account network topology information and data rate requirements of active flows. The scheme learns PU traffic, and as a result, it optimizes spectrum brokerage to the active flows for QoS provisioning and performs interference mitigation.

@inproceedings{
 title = {SDN-based QoS provisioning and interference management in heterogeneous CRN},
 type = {inproceedings},
 year = {2018},
 keywords = {Heterogeneous Cognitive Radio Networks,Software Defined Network},
 pages = {1-4},
 id = {3388d5e4-e5a5-3e33-9179-af1528187380},
 created = {2020-02-29T16:57:43.945Z},
 file_attached = {false},
 profile_id = {c69aa657-d754-373c-91b7-64154b7d5d91},
 last_modified = {2023-02-11T18:54:03.836Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Kakalou2018},
 private_publication = {false},
 abstract = {Cognitive Radio is a promising technology to maximize spectrum efficiency that can be applied to dense residential infrastructure and heterogeneous networks. This paper proposes a Software Defined Networking (SDN) system model for coordinated adaptive spectrum sharing and interference management; the SDN system model leverages the fact that Primary User (PU) network traffic is bursty with an optimal reinforcement learning scheme and takes into account network topology information and data rate requirements of active flows. The scheme learns PU traffic, and as a result, it optimizes spectrum brokerage to the active flows for QoS provisioning and performs interference mitigation.},
 bibtype = {inproceedings},
 author = {Kakalou, Ioanna and Psannis, Kostas and Siakavara, Katherine and Goudos, Sotirios K. and Ishibashi, Yutaka},
 doi = {10.1109/MOCAST.2018.8376585},
 booktitle = {2018 7th International Conference on Modern Circuits and Systems Technologies, MOCAST 2018}
}

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