Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences


UC Berkeley


2009 Research Summary

Developing Fundamental System-level Metrics for Spectrum Sensing

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Rahul Tandra, Mubaraq Mishra and Anant Sahai

"Spectrum holes" represent the potential opportunities for non-interfering (safe) use of spectrum and can be considered as multidimensional regions within frequency, time, and space. The core challenge for secondary radio systems is to be able to robustly sense when they are within such a spectrum hole. The problem is that fading, interference, and noise are all uncertain.

So how are we to determine whether a particular approach to sensing holes is better than another one? One strategy is to simply simulate the performance over a large area (like the continental USA) using real-world information about the distribution of primary transmitters. However, this does not allow the easy development of engineering intuition and design guidance.

The traditional signal detection triad of sensitivity, probability of false-alarm P_{FA}, and probability of missed detection P_{MD} are not much of a help since they operate at too low of a level, miss the spatial dimension to the problem entirely, and cannot operate easily with non-probabilistic uncertainty. So far, we have developed a pair of metrics: the "Weighted Probability of Area Recovered (WPAR)" metric is introduced to measure the performance of a sensing strategy and the "Fear of Harmful Interference" F_{HI} metric is introduced to measure its safety.

These new metrics explicitly consider the impact of asymmetric uncertainties (and misaligned incentives) in the system model. So far, we have used these new metrics to show that fading uncertainty forces the WPAR performance of single-radio sensing algorithms to be very low for small values of F_{HI}, even for ideal detectors. Cooperative sensing algorithms enable a much higher WPAR, but only if users are guaranteed to experience independent fading. Finally, in-the-field calibration for wideband (but uncertain) environment variables (e.g., interference and shadowing) can robustly guarantee safety (low F_{HI}), even in the face of potentially correlated users, without sacrificing WPAR.

We have begun to verify these metrics using real data on television tower placements from the FCC along with population distribution data from the USA census. In addition, we are extending the metrics to also capture the time-dimension of spectrum holes.

Figure 1
Figure 1: Data taken from the FCC, the USA Census, showing the magnitude of spectrum holes in the television bands across the USA

R. Tandra, M. Mishra, and A. Sahai, "What Is a Spectrum Hole and What Does it Take to Recognize One?" Proceedings of the IEEE, January 2009 (to appear).
A. Sahai, M. Mishra, R. Tandra, and K. Woyach, "Cognitive Radios for Spectrum Sharing," IEEE Signal Processing Magazine, DSP Applications Column, January 2009 (to appear).