Research on BPSK Signal Detection and Classification Algorithm Based on Multi-Task Learning

Binary Phase-Shift Keying (BPSK) signal detection is a critical aspect of wireless communications, particularly as the demand for high-performance and reliable communication systems grows in today's technologically advanced landscape. Despite its importance, detecting BPSK signals poses significant challenges, especially in harsh channel conditions characterized by high levels of noise and interference. This study introduces an innovative multi-task learning framework that leverages cutting-edge deep learning techniques to improve BPSK signal processing. Central to this framework is the incorporation of adaptive attention mechanisms, which enable the model to focus dynamically on relevant features of incoming signals, thereby enhancing detection capabilities. Furthermore, the use of contrastive learning allows the model to better differentiate between correct and incorrect signal states, improving its accuracy. Additionally, knowledge distillation techniques are employed to transfer learned insights from a more complex model to a simpler one, leading to enhanced performance while maintaining efficiency. The experimental results are promising, showcasing a remarkable 46% reduction in the bit error rate and a 36.84% improvement in computational efficiency. This advancement not only progresses methodologies used in signal detection but also signifies a vital step forward for next-generation communication systems, striking a balance between performance, adaptability, and resource efficiency.