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Analysis and Algorithm for Robust Adaptive Cooperative Spectrum-Sensing

The optimal data-fusion rule was first established for multiple-sensor detection systems in 1986. Most subsequent works have been focused on the corresponding implementation aspects. The probability of false alarm and the probability of miss detection used in this data-fusion rule are quite difficult to precisely enumerate in practice. Although the improved data-fusion implementation techniques are now available, most existing cooperative spectrum-sensing techniques are still based on the simple energy-detection algorithm, which is prone to failure in many scenarios. In this paper, we propose a novel adaptive cooperative spectrum-sensing scheme based on our recently proposed single-reception spectrum-sensing technique. We also found that the commonly-used sample-average estimator for the cumulative weights in the data-fusion rule becomes unreliable in time-varying environments. To overcome this drawback, we adopt a temporal discount factor, which is crucial to the probability estimators. New theoretical analysis to justify the advantage of our proposed new estimators over the conventional sample-average estimators and to determine the optimal numerical value of the proposed discount factor is presented. The Monte Carlo simulation results are also provided to demonstrate the superiority of our proposed adaptive cooperative spectrum-sensing method in both stationary and time-varying environments.