Performance Evaluation of Cyclostationary-Based Cooperative Sensing Using Field Measurements

Abstract

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.

Using Spectrum Maps for Surveillance Avoiding Path Planning

“…In our experiments we have used measured data to construct the spectral maps for different frequencies. The measurements are from DVB-T network, see [12]. For the radars, we placed three radars with 90 deg scanning sectors in the area.…”

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

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Optimization of Spectrum Utilization in Cooperative Spectrum Sensing

“…In the reporting step, SUs forward their observations or local decisions to the fusion center (FC). In the decision phase, FC makes a global decision about the presence or the absence of primary users based on the collected local observations by using a hard or a soft combining scheme [11,12,13,14]. The authors in [15] have shown the criteria for an effective decision approach selection analytically with high detection accuracy as an objective in the presence of realistic channel propagation effects for two different decision approaches as combining decision (CD) and sensing and combining decision (SCD).…”