Multi-centers cooperative estimation based fast spectrum sensing

To reduce the huge consumption of traditional sensing, a multi-centers estimation based sensing scheme is proposed in this paper. Firstly, all potential channels are clustered into highly related groups with some channels selected as detecting channels (DCs) using an unsupervised algorithm. In each group, the states of other channels (estimated channels, ECs) are estimated according to their correlations with the DCs and the dependence on history to save sensing time. Specifically, number of groups (Ng) and number of DCs in each group (NDC) can be adjusted jointly to improve sensing performance. Moreover, two Hidden Markov Model (HMM) based estimation methods, namely joint estimation (JE) and cooperative estimation (CE), are formulated. In JE, the DCs are modeled as the observed vectors and utilized jointly to estimate ECs' states. While in CE, each DC estimates ECs' states separately and a weight-based cooperative algorithm is designed to merge their results. Tested with real-world measurement data, results show the reduced sensing consumption is considerable at the expense of slight sensing accuracy loss. On these bases, it is significant to note that NdC should be adjusted according to sensing consumption to optimize performance.