Channel gain map tracking via distributed kriging

A collaborative algorithm is developed to estimate the channel gains of wireless links in a geographical area. The spatiotemporal evolution of shadow fading is characterized by judiciously extending an experimentally verified spatial-loss field model. Kriged Kalman filtering (KKF), which is a tool with widely appreciated merits in spatial statistics and geosciences, is adopted and implemented in a distributed fashion to track the time-varying shadowing field using a network of radiometers. The novel distributed KKF requires only local message passing yet achieves a global view of the radio frequency environment through consensus iterations. Numerical tests demonstrate superior tracking accuracy of the collaborative algorithm compared with its noncollaborative counterpart. Furthermore, the efficacy of the global channel gain knowledge obtained is showcased in the context of cognitive radio resource allocation.