标题： 从DeepSense到Open RAN：动态频谱感知中的AI/ML进展及其应用 显示英文标题

标题： From DeepSense to Open RAN: AI/ML Advancements in Dynamic Spectrum Sensing and Their Applications

摘要： 人工智能（AI）和机器学习（ML）在下一代无线通信系统中的集成已成为推动智能、自适应和可扩展网络的基石。 本阅读报告审视了动态频谱感知（DSS）的关键创新，从基础的DeepSense框架开始，该框架使用卷积神经网络（CNN）和基于频谱图的分析进行实时宽带频谱监控。 在此基础上，它突出了DeepSweep和宽带信号拼接等进展，这些进展通过并行处理、语义分割和稳健的数据增强策略来解决可扩展性、延迟和数据集多样性方面的挑战。 报告随后探讨了开放无线接入网络（ORAN），重点在于无人机实验的AI/ML驱动增强、基于数字孪生的优化、网络切片和自愈xApp开发。 通过将基于AI的DSS方法与ORAN的开放、无供应商限制的架构相结合，这些研究强调了软件定义的智能基础设施在为5G/6G生态系统实现高效、弹性且自我优化的网络方面的潜力。 通过这一综合分析，报告突出了AI在塑造无线通信和自主系统未来中的变革作用。 显示英文摘要

摘要： The integration of Artificial Intelligence (AI) and Machine Learning (ML) in next-generation wireless communication systems has become a cornerstone for advancing intelligent, adaptive, and scalable networks. This reading report examines key innovations in dynamic spectrum sensing (DSS), beginning with the foundational DeepSense framework, which uses convolutional neural networks (CNNs) and spectrogram-based analysis for real-time wideband spectrum monitoring. Building on this groundwork, it highlights advancements such as DeepSweep and Wideband Signal Stitching, which address the challenges of scalability, latency, and dataset diversity through parallel processing, semantic segmentation, and robust data augmentation strategies. The report then explores Open Radio Access Networks (ORAN), focusing on AI/ML-driven enhancements for UAV experimentation, digital twin-based optimization, network slicing, and self-healing xApp development. By bridging AI-based DSS methodologies with ORAN's open, vendor-neutral architecture, these studies underscore the potential of software-defined, intelligent infrastructures in enabling efficient, resilient, and self-optimizing networks for 5G/6G ecosystems. Through this synthesis, the report highlights AI's transformative role in shaping the future of wireless communication and autonomous systems.