Multiple transmitter localization and communication footprint identification using energy measurements

Whitespace identification is a crucial first-step in the implementation of cognitive radios, where the problem is to determine the communication footprint of active primary transmitters in a given geographical area. To do this, a number of sensors are deployed at known locations chosen uniformly at random within the given area. The sensors’ decisions regarding the presence or absence of a signal at their location is transmitted to a fusion center, which then combines the received information to construct the spatial spectral usage map. Under this model, several innovations are presented in this work to enable fast identification of the available whitespace. First, using the fact that a typical communication footprint is a sparse image, two novel compressed sensing based reconstruction methods are proposed to reduce the number of transmissions required from the sensors compared to a round-robin querying scheme. Second, a new method based on a combination of the KK-means algorithm and a circular fitting technique is proposed for determining the number of primary transmitters. Third, a design procedure to determine the power thresholds for signal detection at sensors is discussed. The proposed schemes are experimentally compared with the round-robin scheme in terms of the average error in footprint identification relative to the area under consideration. Simulation results illustrate the improved performance of the proposed schemes relative to the round-robin scheme.