Spatial Compression of Sensing Information for Exploiting the Vacant Frequency Resource Using Radio Sensors

Recently, broadband wireless communication has been significantly enhanced; thus, frequency spectrum scarcity has become an extremely serious problem. Spatial frequency reuse based on spectrum databases has attracted significant attention. The spectrum database collects wireless environment information, such as the radio signal strength indicator (RSSI), estimates the propagation coefficient for the propagation loss and shadow effect, and finds a vacant area where the secondary system uses the frequency spectrum without harmful interference to the primary system. Wireless sensor networks are required to collect the RSSI from a radio environmental monitor. However, a large number of RSSI values should be gathered because numerous sensors are spread over the wireless environment. In this study, a data compression technique based on spatial features, such as buildings and houses, is proposed. Using computer simulation and experimental evaluation, we confirm that the proposed compression method successfully reduces the size of the RSSI and restores the original RSSI in the recovery process.