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Lessons Learned from an Extensive Spectrum Occupancy Measurement Campaign and a Stochastic Duty Cycle Model

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Abstract

Several measurement campaigns have shown that numerous spectrum bands are vacant although licenses have been issued by the regulatory agencies. Dynamic spectrum access (DSA) has been proposed in order to alleviate this problem and increase the spectral utilization. In this paper we present our spectrum measurement setup and discuss lessons learned during our measurement activities. We compare measurement results gathered at three locations and show differences in the background noise processes. Additionally, we introduce a new model for the duty cycle distribution that has multiple applications in the DSA research. We point out that fully loaded and completely vacant channels should be modelled explicitly and discuss the impact of duty cycle correlation in the frequency domain. Finally, we evaluate the efficiency of an adaptive spectrum sensing process as an example for applications of the introduced model.

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Notes

  1. A primary user denotes a system possessing the license to access a frequency band. The systems accessing spectrum white spaces opportunistically are called secondary users [6]. Primary users are often also called incumbent users.

  2. Global System for Mobile Communication (GSM).

  3. IN: Latitude: 50° 47ʹ 24.01ʺ North, Longitude: 6° 3ʹ 47.42ʺ East.

  4. NE: Latitude: 50° 50ʹ 32.34ʺ North, Longitude: 5° 43ʹ 14.93ʺ East.

  5. AB: Latitude: 50° 46ʹ 8.90ʺ North, Longitude: 6° 4ʹ 42.59ʺ East.

  6. The ISM-band is reserved for industrial, scientific, and medical applications.

  7. For few samples the data transfer from the mote to the laptop failed but the slow changing temperature is still sufficiently described.

  8. The results presented in [32] have been computed using the whole measurement traces and kernel-based CDF estimation as offered by MATLAB. Here, we use representative subtraces that describe the daytime spectrum occupancy. Additionally, we use the empirical CDF since the kernel-based estimation does not accurately estimate steps in the CDF that may occur if single subbands show clearly different duty cycles.

  9. In [32] we applied the symmetric Kullback-Leibler divergence as goodness-of-fit metric. We switched to other metrics due to the characteristics of the applied CDF estimation. The kernel-based CDF estimation, as executed in [32], results in rather smooth curves but the empirical CDF is more realistic and reproduces further details, e.g., sharp steps. The Kullback-Leibler divergence is too sensitive to such characteristics and the other metrics turned out to be more robust for our application.

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Acknowledgements

The authors would like to thank the RWTH Aachen University and the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) for providing financial support through UMIC research centre. We would also like to thank the European Union for providing partial funding of this work through the ARAGORN project. Additionally, we would like to thank the International School Maastricht, Maastricht, Netherlands, and Marten Bandholz and Sonja Bone for providing us access to the outdoor measurement locations. Finally, we would like to thank Jin Wu and Gero Schmidt-Kärst for their support during the measurements.

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  1. Department of Wireless Networks, RWTH Aachen University, Kackertstrasse 9, 52072, Aachen, Germany

    Matthias Wellens & Petri Mähönen

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Correspondence to Matthias Wellens.

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Wellens, M., Mähönen, P. Lessons Learned from an Extensive Spectrum Occupancy Measurement Campaign and a Stochastic Duty Cycle Model. Mobile Netw Appl 15, 461–474 (2010). https://doi.org/10.1007/s11036-009-0199-9

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