Abstract : In electronic countermeasure scenarios, wireless communication systems are highly susceptible to co-channel interference (CCI), making effective CCI mitigation essential for ensuring the normal operation of the system. However, the traditional adaptive methods are difficult to effectively suppress interference when the desired signal is overwhelmed by high-power, broadband interference. To tackle this issue, we first investigated the spatiotemporal correlation discrepancy of received signals, revealing that the narrowband signal of interest (SOI) exhibits significantly stronger spatiotemporal correlation compared with broadband interference. Based on this insight, we developed a spatiotemporal adaptive broadband interference cancellation (STABC) algorithm by innovatively adapting the alternating direction method of multipliers (ADMM) framework. Unlike deep learning-based interference cancellation methods that rely on large amounts of training data, the proposed approach operates without prior knowledge of signal directions or adaptive array training data, requiring only the bandwidth information of the SOI, making it suitable for real-time and resource-constrained scenarios. Furthermore, the algorithm circumvents the computationally expensive inversion of high-dimensional matrices, thereby achieving reduced computational complexity. Additionally, we conducted a theoretical analysis of the impact of multiple transmission paths on the STABC algorithm for antenna arrays. Extensive simulation results confirm that the proposed STABC algorithm achieves faster convergence and approaches the theoretical maximum signal to interference plus noise ratio (SINR) solution more closely. Additionally, under multipath conditions, the proposed algorithm demonstrates superior performance.
Index terms : Interference Cancellation, Spatiotemporal correlation, Alternating direction method of multipliers, Co-channel interference