Towards Cloud-Based Crowd-Augmented Spectrum Mapping for Dynamic Spectrum Access

Recently, large-scale spectrum measurements show that geo-location spectrum databases, as recommended by regulators (e.g., FCC, Ofcom, ECC) for TV white space (TVWS) discovery are notoriously inaccurate in Metropolitan areas because of inaccurate TV channel propagation models they adopted. To counter this challenge, we propose a cloud-based crowd-augmented spectrum mapping scheme. Our scheme aims to build accurate geo-location database in Metropolitan areas with high spatial resolution under minimum cost by jointly utilizing superior computing capacity of cloud servers, abundant spectrum sensing data from crowd of mobile white-spaces device (WSD) users and the well-established geo-statistical techniques. More specifically, our spectrum mapping scheme consists of three interdependent components (1) opportunistic mobile spectrum sensing, which exploits the high spatial diversity of mobile users along with low-cost embedded spectrum measuring solution to retrieve power spectrum density (PSD) information of the TV channels in a large Metropolitan region; (2) cloud-based geo-statistical spectrum mapping, which estimates PSD at unknown geographic locations by utilizing the abundant PSD data aggregated at the cloud server consisting of geo-statistical analysis and interpolation tools; (3) optimal spatial sampling, which further augments the accuracy of the spectrum map by selecting the optimal locations, at which additional spectrum sensing measurements are obtained to minimize the spectrum mapping error. To verify the performance of the proposed scheme, an experiment is conducted in our university campus. The experiment result shows that our proposed scheme can discover more spectrum opportunities than the information reported by commercially available geo-location spectrum databases.