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Blind Frequency Hopping Spectrum Estimation: A Bayesian Approach

Frequency hopping signals have been widely employed in wireless networks due to its robustness in anti-jamming and interference. In the scenario of coexistent networks, however, each user inevitably receives multiple unknown frequency hopping signals from different networks. In wireless networks with energy constraint, conventional re-transmission strategy is not affordable if collision happens. This paper considers the blind frequency hopping signal estimation problem in energy-constraint wireless senor networks, where re-transmission can be avoided due to the capability of robust spectrum estimation. We develop a novel high-resolution time-frequency representation by exploiting sparsity to allow signal sampled with sub-Nyquist ratio and achieve robust estimation performance. In our work, this problem is formulated in a probabilistic framework to induce sparsity statistically. Apart from sparsity, the piecewise smoothness in time-frequency domain is further leveraged with a clustering alike procedure by exerting a dependent Dirichlet process prior over the variance parametric space in an integrated manner. Results of numerical experiments show that the proposed algorithm can achieve superior performance particularly in sub-Nyquist sampling and low signal-to-noise ratio (SNR) scenarios compared with other recently reported ones.