Collaborative Learning-Based Modulation Recognition for 6G Multibeam Satellite Communication Systems via Blind and Semiblind Channel Equalization

Blind modulation recognition (BMR) involves identifying the modulation scheme of intercepted signals, an essential component of terrestrial radio management. While deep learning (DL) has advanced BMR research in terrestrial contexts, 6G multibeam mobile satellite (MMS) systems present many challenges. One pivotal hurdle is the reliance on perfect channel-state information (CSI) for signal equalization in terrestrial multiple-input–multiple-output (MIMO) BMR literature. In MMS systems, the vast satellite–ground distance can lead to outdated CSI, making traditional equalization techniques less effective. Addressing this, we introduce a viable BMR algorithm that leverages blind and semiblind channel equalization to overcome challenges like interbeam interference (IBI), limited CSI, and shadowed-Rician (SR) fading. By transforming the MMS system into an equivalent MIMO satellite system, we enable enhanced MIMO channel equalization to counteract IBI and SR attenuations. Our proposed locality–globality convolutional-transformer deep neural network merges the strengths of convolutional neural networks in local feature extraction with Transformers in global feature discernment. A decision fusion strategy is then incorporated to utilize cooperative diversity across multiple eavesdroppers. Numerical tests validate the effectiveness of our blind and semiblind equalization techniques, emphasizing their superiority over existing DL-based methods in satellite modulation signal identification even in conditions with limited or absent CSI.