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For the time-frequency overlapped signals, a low-complexity single-channel blind source separation (SBSS) algorithm is proposed in this paper. The algorithm does not only introduce the Gibbs sampling theory to separate the mixed signals, but also adopts the orthogonal triangle decomposition-M (QRD-M) to reduce the computational complexity. According to analysis and simulation results, we demonstrate that the separation performance of the proposed algorithm is similar to that of the per-survivor processing (PSP) algorithm, while its computational complexity is sharply reduced.

For the interference hidden in the expected multi-carrier signal, this paper proposes a novel detection and recognition algorithm. The algorithm cannot only detect the single-carrier interference (SCI) by the high-order cumulant, but also finds the multi-carrier signal based on spectrum character. Besides, the algorithm can distinguish the modulation types of the SCI. The algorithm does not depend on any prior knowledge and data-aided, which is propitious to practical applications. The analysis and simulation results demonstrate that the proposed algorithm is effective.