Big RF data assisted cognitive radio network coexistence in 3.5GHz band

Oluwaseyi Omotere, Lijun Qian, Riku Jäntti, Miao Pan, Zhu Han

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

6 Citations (Scopus)


In this paper, big Radio Frequency (RF) data assisted optimization is considered for future wireless networks employing cognitive radio technology with machine learning capability. A cognitive radio network (CRN) with multiple Secondary Users (SUs) may coexist with other wireless systems such as Small Cells (SC) and Radar systems, both Primary Users (PUs) with different level of priorities. Traditional spectrum sensing typically only gives information about the presence or absence of a PU. However, when multiple heterogeneous systems coexist, it becomes imperative to acquire the knowledge of the systems operating in a specific band at a particular time so as to choose an appropriate transmission strategy. In this work, we take advantage of the learning capability of a Neural Network Predictor (NNP) to obtain the statistics of the coexisted wireless systems from the RF traces collected in our Universal Software Radio Peripheral (USRP) based test bed. The NNP is able to learn the features of the RF traces and make accurate prediction of the signals prevalent in the wireless environment. Because of the augmented information learned from the RF traces, a novel optimization problem incorporating the outputs from the NNP is formulated to maximize the throughput of the CRN. The solution is derived using Karush- Kuhn-Tucker (KKT) and extensive simulations using the real RF traces are carried out. It is demonstrated that the NNP can detect the type and number of coexisted users reliably and the proposed scheme will improve the performance of the coexisted CRN.

Original languageEnglish
Title of host publication2017 26th International Conference on Computer Communications and Networks, ICCCN 2017
Number of pages8
ISBN (Electronic)9781509029914
Publication statusPublished - 14 Sep 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Computer Communications and Networks - Vancouver, Canada
Duration: 31 Jul 20173 Aug 2017
Conference number: 26

Publication series

NameProceedings : International Conference on Computer Communications and Networks
ISSN (Print)1095-2055


ConferenceInternational Conference on Computer Communications and Networks
Abbreviated titleICCCN


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