Enhancing cognitive radios with spatial statistics: From radio environment maps to topology engine

Radio environment maps are a promising architectural concept for storing environmental information for use in cognitive wireless networks. However, if not applied carefully their use can lead to large amounts of measurement data communicated over wireless links, causing substantial overhead. We propose enhancing the basic radio environment map concept by spatial statistics and probabilistic models, enabling applications to benefit from environment data while reducing overhead. In this paper we discuss the development of a topology engine, an agent in the CWN collecting and processing spatial information about the environment for storage in the REM. We discuss both technical and architectural issues in enabling such an approach, and outline some of the potential application scenarios for the topology engine.