A Data-Driven Target Signal Extraction Method Based on Multimodal Clues for Co-Channel Interference Cancellation

Abstract

Interference cancellation (IC) is crucial for ensuring the continuous operability of wireless communication systems based on the Internet of Things (IoT). This study focuses on blind signal separation (BSS)-based IC for co-channel multiuser systems. Considering humans’ selective auditory attention abilities, we propose a novel top-down auto-focusing target signal extraction (TSE) method, which has been developed according to our recently established data-driven BSS scheme. In the signal separation stage, we input clues regarding the target signal into the separation system to guide it toward the target signal; as a result the final output is only the desired signal. This approach mitigates the global permutation ambiguity and eliminates the need for prior knowledge or estimation of signal numbers in existing BSS schemes. This study focuses on the clue encoder and clue fusion layer, which can be integrated into existing data-driven single-channel BSS schemes. In the clue encoding process, we use multiple modal clues as inputs to leverage their complementary advantages. Additionally, we apply a multimodal clue feature space alignment method to reduce the impact of feature space distribution differences on the interactions of multimodal clue information. In the clue fusion process, we propose an attention-based clue fusion scheme to provide more informative target signal clues for at each time frame. Finally, during the training process, we implement a multitask learning approach that considers the losses in different modal clues, thereby enabling the separation system to function properly even when some clues are unavailable. Numerical results confirm the effectiveness of the proposed scheme.