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Interference Estimation and Rocevery in Transform Domain Communication System

The wireless communication networks are highly vulnerable to various types of electromagnetic interference, which seriously restricts the communication effectiveness, and the interference estimation and elimination are much essential issues in wireless communications. To solve the multi-task problems of the diverse interference estimation and cancelation with in transformation domain communication systems, the iterative sparse learning approach for multiple interference recovery and elimination simultaneously is proposed in this paper, which achieves the multiple learning for measured signals and structured sparse learning method in the area of simultaneous interference mitigation with signal recovery. Simulation results show that the proposed algorithms achieve convergence more rapidly, and the recovery performances are more accurate than other conventional algorithms in the condition of spectrum sensing with consistency, respectively.