Location estimation based spatiotemporal sensing in cognitive radio networks

In order to exploit both spatial and temporal opportunities more thoroughly, this paper investigates the problem of spatiotemporal spectrum sensing for opportunity-heterogeneous cognitive radio networks, where secondary users located in different locations may experience different spectrum reuse opportunities. While the majority of existing researches focus on either spatial or temporal available resources detecting, we propose to fuse the log likelihood ratios of the observations weighted by spatial correlation of selected temporal sensing users rather than all users, which are the closest to the primary transmitter according to its location estimation from spatial sensing. In addition, we analyze the secondary users´ maximum interference constrained transmission powers (MICTP) and the corresponding interference probability through simulation. Compared with traditional detection methods, the proposed selective weighted soft combing method improves the probability of detecting spectral holes and achievable capacity without increasing much overhead.