中文标题#
移動邊緣計算和開放無線接入網的進步：利用人工智慧和機器學習為無線系統賦能
英文标题#
Advancements in Mobile Edge Computing and Open RAN: Leveraging Artificial Intelligence and Machine Learning for Wireless Systems
中文摘要#
移動邊緣計算（MEC）和開放無線接入網（ORAN）是下一代無線通信系統發展中的變革性技術。 MEC 將計算資源推向終端用戶，實現低延遲和高效處理，而 ORAN 促進了無線網絡的互操作性和開放性，從而推動了創新。 本文探討了這兩個領域的最新進展，特別關注人工智慧（AI）和機器學習（ML）技術如何被用於解決複雜的無線挑戰。 在 MEC 中，深度強化學習（DRL）被用於優化計算卸載，確保節能解決方案並滿足服務質量（QoS）要求。 在 ORAN 中，AI/ML 被用於開發用於網絡切片、調度和在線訓練的智能 xApps，以提高網絡適應性。 本閱讀報告對多篇關鍵論文進行了深入分析，討論了所採用的方法，並突出了這些技術在提高網絡效率和可擴展性方面的影響。
英文摘要#
Mobile Edge Computing (MEC) and Open Radio Access Networks (ORAN) are transformative technologies in the development of next-generation wireless communication systems. MEC pushes computational resources closer to end-users, enabling low latency and efficient processing, while ORAN promotes interoperability and openness in radio networks, thereby fostering innovation. This paper explores recent advancements in these two domains, with a particular focus on how Artificial Intelligence (AI) and Machine Learning (ML) techniques are being utilized to solve complex wireless challenges. In MEC, Deep Reinforcement Learning (DRL) is leveraged for optimizing computation offloading, ensuring energy-efficient solutions, and meeting Quality of Service (QoS) requirements. In ORAN, AI/ML is used to develop intelligent xApps for network slicing, scheduling, and online training to enhance network adaptability. This reading report provides an in-depth analysis of multiple key papers, discusses the methodologies employed, and highlights the impact of these technologies in improving network efficiency and scalability.
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