The cloud radio access network (C-RAN) has become the foundational structure for various emerging communication paradigms, leveraging the flexible deployment of distributed access points (APs) and centralized task processing. In this paper, we propose a cross-layer optimization framework based on a practical finite-length coding communication system in C-RAN, aiming at maximizing bandwidth efficiency while providing statistical quality of service (QoS) for individual services. Based on the theoretical results from effective capacity and finite-length coding, we formulate a joint optimization problem involving modulation and coding schemes (MCS), retransmission count, initial bandwidth allocation and AP selection, which reflects the coordinated decision of parameters across the physical layer, data link layer and transport layer. To tackle such a mixed-integer nonlinear programming (MINLP) problem, we firstly decompose it into a transmission parameter decision (TPD) sub-problem and a user association (UA) sub-problem, which can be solved by a binary search-based algorithm and an auction-based algorithm respectively. Simulation results demonstrate that the proposed model can accurately capture the impact of QoS requirements and channel quality on the optimal transmission parameters. Furthermore, compared with fixed transmission parameter setting, the proposed algorithms achieve the bandwidth efficiency gain up to 27.87% under various traffic and channel scenarios.

翻译：云无线接入网（C-RAN）凭借分布式接入点（AP）的灵活部署与集中式任务处理，已成为多种新兴通信范式的基础架构。本文针对C-RAN中基于实际有限长编码的通信系统，提出一种跨层优化框架，旨在最大化带宽效率的同时为单个业务提供统计服务质量（QoS）保障。基于有效容量与有限长编码的理论成果，我们构建了一个联合优化问题，涉及调制编码方案（MCS）、重传次数、初始带宽分配与AP选择，这反映了物理层、数据链路层及传输层参数的协同决策。为求解该混合整数非线性规划（MINLP）问题，我们首先将其分解为传输参数决策（TPD）子问题与用户关联（UA）子问题，分别通过基于二分搜索的算法与基于拍卖的算法求解。仿真结果表明，所提模型能够准确刻画QoS需求与信道质量对最优传输参数的影响。此外，与固定传输参数设置相比，所提算法在不同流量与信道场景下可实现高达27.87%的带宽效率增益。