The Cloud Radio Access Network (C-RAN) with centralized processing features achieves efficient and unified resource management to meet the quality of service (QoS) requirements, while results in an increment of energy consumption. To reach a tradeoff between energy efficiency and QoS, jointly considering baseband unit (BBU) computing resource, remote radio head (RRH) power, and fronthaul (FH) link capacity optimization for delay-aware traffic is an NP-hard problem. In this paper, we propose a system energy efficiency optimization model jointing multiple resources allocation for C-RAN downlink transmission. The end-to-end delay (De) in the proposed model is formulated by the established user data queue model, which satisfies the strict Lyapunov stability. Then, based on defining an improved Drift-Plus-Penalty function \(F_{DPP}\) to transform the proposed original problem into two sub-problems which are BBU service rate allocation and RRH power control problems. The optimal BBU service rate and RRH transmission power of a single slot are obtained through solving a linear equation and applying a convolution neural network (CNN), respectively. Further, we propose an iterative-based optimization algorithm to achieve the optimal resource allocation for each slot. The simulation results show that the proposed optimization algorithm effectively reaches the balance between energy efficiency and QoS, and achieves better energy efficiency compared with the decomposition allocation method based on heuristic algorithm and BBU scheduling based on first-fit-decreasing (FFD) algorithm with lower computational complexity.

具有集中处理特性的云无线接入网（C-RAN）实现了高效和统一的资源管理，以满足服务质量（QoS）要求，同时导致能耗增加。为了在能效和 QoS 之间取得权衡，联合考虑基带单元 (BBU) 计算资源、远程射频头 (RRH) 功率和前传 (FH) 链路容量优化延迟感知流量是一个 NP-hard 问题。在本文中，我们提出了一种联合多资源分配的系统能效优化模型，用于C-RAN下行链路传输。所提出模型中的端到端延迟 (De) 由已建立的用户数据队列模型制定，满足严格的 Lyapunov 稳定性。然后，在定义改进的 Drift-Plus-Penalty 函数的基础上\(F_{DPP}\)将提出的原始问题转化为两个子问题，即BBU服务速率分配和RRH功率控制问题。通过求解线性方程和应用卷积神经网络 (CNN) 分别获得单时隙的最佳 BBU 服务速率和 RRH 传输功率。此外，我们提出了一种基于迭代的优化算法来实现每个时隙的最佳资源分配。仿真结果表明，所提出的优化算法有效地达到了能量效率和QoS之间的平衡，与基于启发式算法的分解分配方法和基于首次适配递减（FFD）算法的BBU调度相比，具有更好的能量效率，且功耗更低。计算复杂度。