Low Complexity Wireless Interference Identification in Antagonistic Environments Based on Multi-Task Learning

As the fundamental premise of anti-interference communication, wireless interference identification (WII) has garnered extensive research and yielded substantial results, especially for deep learning (DL)-enabled WII. However, existing studies are conducted typically under the closed-set assumption. This aspect poses a challenge in identifying the unknown interference signals under the open-set assumption. To tackle this issue, this paper proposes a multi-task learning-enabled WII (MTL-WII) algorithm in antagonistic environments. Firstly, we generate the semantic feature space of known classes, calculating the semantic center vectors of each known class through multi-task learning. Secondly, we obtain the semantic feature space of the test set incorporating interference signals of known and unknown classes through the established mapping relationships. Subsequently, the signal's semantic feature vectors are classified using clustering methods to determine their category attribution. To reduce computational complexity, this paper further proposes a binarized MTL-WII algorithm (BMTL-WII). By binarizing the semantic spatial generative network in MTL-WII, both the weights and activations of the semantic spatial generative network replace the 32-bit floating-point numbers with 1-bit fixed-point numbers. Experimental results show that the MTL-WII model achieves an average identification accuracy of 95.4%, with an 89.3% accuracy for unknown interference signals. Compared to the MTL-WII algorithm, the BMTL-WII algorithm reduces the number of floating-point operations by 64.2%, and the amount of memory access is reduced by 88%, at the cost of identification accuracy decrease by only 1.8% of the binarized part of the network structure.