Spectrum Map and Its Application in Resource Management in Cognitive Radio Networks

Measurements on radio spectrum usage have revealed an abundance of under-utilized bands of spectrum that belong to primary (licensed) networks. Prior knowledge about the occupancy of such bands and the expected achievable performance on those bands can help secondary (unlicensed) networks to devise effective strategies to improve utilization. Such prior spatio-temporal spectrum usage statistics can either be obtained from a database that is maintained by the primary networks or could be measured by customized sensors deployed by the secondary networks. In this paper, we use Shepard's interpolation technique to estimate a spatial distribution of spectrum usage over a region of interest, which we call the spectrum map. The interpolation is achieved by intelligently fusing the data shared by the the secondary nodes considering their mutual distances and spatial orientation with each other. The obtained map is a two-dimensional (2-D) interpolation function that is continuously differentiable and passes through all the spectrum usage values recorded at arbitrary locations; thus providing a reference for primary occupancy in that region. For determining the optimal locations for sensing primary activity, we use an iterative clustering technique that uses tree structured vector quantization. We use the spectrum map to estimate different radio and network performance metrics like channel capacity, network throughput, and spectral efficiency. As a comprehensive case study, we demonstrate how the spectrum map can be used for efficient resource allocation in TV white space. In particular, we consider an IEEE 802.22-based WRAN and show how the rendezvous probability can be improved for better radio resource allocation, thereby increasing the secondary spectrum utilization.