Low-Complexity, Distributed Characterization of Interferers in Wireless Networks

We consider a large-scale wireless network that uses sensors along its edge to estimate the characteristics of interference from neighboring networks or devices. Each sensor makes a noisy measurement of the received signal strength (RSS) from an interferer, compares its measurement to a threshold, and then transmits the resulting bit to a cluster head (CH) over a noisy communication channel. The CH computes the maximum likelihood estimate (MLE) of the distance to the interferer using these noise-corrupted bits. We propose and justify a low-complexity threshold design technique in which the sensors use nonidentical thresholds to generate their bits. This produces a dithering effect that provides better performance than previous techniques that use different non-identical thresholds or the case in which all the sensor motes use an identical non-optimal threshold. Our proposed technique is also shown (a) to be of low complexity compared to previous non-identical threshold approaches and (b) to provide performance that is very close to that obtained when all sensors use the identical, but unknown, optimal threshold. We derive the Cramér-Rao bound (CRB) and also show that the MLE using our dithered thresholds is asymptotically both efficient and consistent. Simulations are used to verify these theoretical results.