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 the spectrum database is attracting significant attention. The spectrum database collects the 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. To collect RSSI from the radio environ-mental monitor, which is a radio sensor, wireless sensor networks are required. However, a large number of RSSIs should be gathered because numerous sensors are spread over the wireless environment. In this paper, a data compression technique based on spatial features, such as buildings and houses, is proposed. Using a computer simulation and an experimental evaluation, we confirm that the proposed compression successfully reduces the size of the RSSI and restores the original RSSI in the recovery process.