中文标题#
從 DeepSense 到 Open RAN：動態頻譜感知中的 AI/ML 進展及其應用
英文标题#
From DeepSense to Open RAN: AI/ML Advancements in Dynamic Spectrum Sensing and Their Applications
中文摘要#
人工智能（AI）和機器學習（ML）在下一代無線通信系統中的集成已成為推動智能、自適應和可擴展網絡的基石。 本閱讀報告審視了動態頻譜感知（DSS）的關鍵創新，從基礎的 DeepSense 框架開始，該框架使用卷積神經網絡（CNN）和基於頻譜圖的分析進行實時寬帶頻譜監控。 在此基礎上，它突出了 DeepSweep 和寬帶信號拼接等進展，這些進展通過並行處理、語義分割和穩健的數據增強策略來解決可擴展性、延遲和數據集多樣性方面的挑戰。 報告隨後探討了開放無線接入網絡（ORAN），重點在於無人機實驗的 AI/ML 驅動增強、基於數字雙胞胎的優化、網絡切片和自癒 xApp 開發。 通過將基於 AI 的 DSS 方法與 ORAN 的開放、無供應商限制的架構相結合，這些研究強調了軟件定義的智能基礎設施在為 5G/6G 生態系統實現高效、彈性且自我優化的網絡方面的潛力。 通過這一綜合分析，報告突出了 AI 在塑造無線通信和自主系統未來中的變革作用。
英文摘要#
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.
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