AI 中文总结
本文针对O-RAN架构，构建整合延迟与AI推理成本的端到端能耗模型，优化基带与AI任务部署，分析能量-延迟权衡，为AI驱动服务的O-RAN部署提供实用指导。
AI 中文摘要
开放无线接入网（O-RAN）架构引入了灵活的功能拆分与开放接口，支持基带处理的分布式与集中式部署。这种灵活性虽为提升资源利用率提供了机遇，却也在能效与延迟间引入了根本权衡。本文构建了一种基于吞吐量的O-RAN端到端能耗模型，并通过整合详细的延迟建模与特定应用的人工智能/机器学习（AI/ML）推理成本对其进行扩展。所提出的端到端建模框架，全面表征了接入、城域与长途网络段中与处理、传输及推理相关的能耗与延迟。基于该通用模型，本文构建了一个优化问题，用于在候选O-RAN配置中选择基带处理与AI推理任务的部署位置，以分析网络负载、服务器频率及能量预算约束下的能量-延迟权衡。借助代表性硬件平台与现实流量假设，本文评估了对应不同O-RAN功能配置的多种基带处理部署方案。研究结果表明，用户服务质量要求与网络负载条件共同决定了基带处理与AI推理任务的最优部署位置，凸显了能效与延迟间的固有权衡。该分析为支持新兴AI驱动服务的延迟感知与高能效O-RAN部署提供了实用见解。
英文摘要
The Open Radio Access Network (O-RAN) architecture introduces flexible functional splits and open interfaces that enable distributed and centralized deployment of baseband processing. While this flexibility offers opportunities for improved resource utilization, it also introduces fundamental trade-offs between energy efficiency and latency. In this paper, we develop a throughput-based end-to-end energy consumption model for O-RAN and extend it by incorporating detailed latency modeling and application-specific Artificial Intelligence/Machine Learning inference costs. The proposed end-to-end modeling framework provides a general representation of processing, transport, and inference-related energy and delay across the access, metro, and long-haul network segments. Building on this general model, we formulate an optimization problem that selects the placement of baseband processing and AI inference tasks across candidate O-RAN configurations to analyze energy-latency tradeoffs under network load, server frequency, and energy-budget constraints. Using representative hardware platforms and realistic traffic assumptions, we evaluate multiple baseband processing placements corresponding to different O-RAN functional configurations. Our results reveal how user quality of service requirements and network load conditions jointly determine the optimal placement of baseband processing and AI inference tasks, highlighting the inherent trade-off between energy efficiency and latency. The analysis provides practical insights for latency-aware and energy-efficient O-RAN deployments supporting emerging AI-driven services.
