Performance Evaluation of Cyclostationary Based Cooperative Sensing Using Field Measurements
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|Julkaisu||IEEE Transactions on Vehicular Technology|
|Varhainen verkossa julkaisun päivämäärä||2015|
|DOI - pysyväislinkit|
|Tila||Julkaistu - 2016|
This paper focuses on evaluating the gains obtained through cooperative spectrum sensing in real-world while using cyclostationary based mobile sensors. In cooperative sensing (CS), different secondary users (SUs) in a geographical neighborhood cooperate to detect the presence of a primary user (PU). As compared to single-user sensing, cooperation provides diversity gains in the face of multipath fading and shadowing. The effectiveness of the CS is demonstrated by analyzing data acquired in two extensive field measurement campaigns. The first measurement campaign (MC-I) focuses on measurements at fixed locations while the second measurement campaign (MC-II) focuses on a scenario where measurements are taken inside a moving car. These measurements are carried out for DVB-T channels in Finland’s Capital Region, which consists of urban and suburban environments. Hard decision rules such as OR, AND, MAJORITY and soft decision rule such as SUM (sum of cyclostationary test statistics) are employed and their detection performances are compared to a cyclostationary based single-user detector. A performance parameter of relative increase in probability of detection (RIPD) is used to efficiently demonstrate the cooperation gain obtained relative to local sensing. It is shown that cooperation can significantly improve the performance of a sensor severely affected by fading and shadowing effects. Furthermore, it is shown that increasing the number of collaborating users beyond few users (5-8) does not in practice bring significant improvement in terms of the expected RIPD. The performances of CS schemes evaluated from MC-I are also compared to the corresponding simulated CS results using empirical channel models and terrain data for the same experimental parameters. It is shown that the use of empirical or theoretical models may result in detection errors in practical conditions and measurements should be used to improve the accuracy in such scenarios.