Capacity- and Bayesian-Based Cognitive Sensing with Location Side Information

We investigate spectrum sensing by energy detection based on two different objective functions: a Bayesian sensing cost or the network weighted sum capacity. The Bayesian cost is a traditional detection measure which aims at minimizing a combination of the miss-detection and false-alarm probabilities, while the capacity objective is a communication measure which aims at maximizing the network throughput. Fading-dependent optimal sensing thresholds for each objective are derived in closed-form for different cases of location side information. To make sensing more robust to channel fading, we also propose fading-independent sub-optimal thresholds. Results show that location side information helps improve performance when using the threshold designed for that performance measure. However, the Bayesian-based threshold does not utilize the side information well in improving the network sum capacity. On the other hand, the capacity-based threshold captures the benefit of side information in both the capacity and Bayesian cost measures. Furthermore, it helps to significantly improve the network throughput. The proposed sensing schemes with location side information can also be generalized to a network with multiple cognitive users in a simple and distributed manner.