The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances of min-max fairness and Vickrey-Clarke-Groves (VCG) auction-based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approach minimizes the maximum OPEX of RUs through cost-sharing proportional to their demands, whereas the VCG auction-based approach minimizes the total OPEX for all resources utilized while extracting truthful demands from RUs. We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.

翻译：开放无线接入网络（O-RAN）通过解耦的射频单元（RU）、分布式单元（DU）和集中式单元（CU）实现基带功能处理的云化与网络功能虚拟化。这全面实现了云-RAN愿景，使得多个移动网络运营商（MNO）可以安装其专有或开放的RU，但通过开放的X-haul接口从公共可用的开放云中按需租赁用于DU-CU功能的计算资源。本文提出并比较了最小-最大公平性与Vickrey-Clarke-Groves（VCG）拍卖两种X-haul及DU-CU资源分配机制的性能，旨在构建一个对中小型及大型MNO均具有可持续性的多租户O-RAN生态系统。最小-最大公平方法通过按RU需求比例分摊成本来最小化RU的最大运营支出（OPEX），而基于VCG拍卖的方法在提取RU真实需求的同时，最小化所有使用资源的总OPEX。我们考虑基于时分波分复用（TWDM）无源光网络（PON）的X-haul接口，采用PON虚拟化技术灵活提供宏小区RU位置处的RU与边缘云之间，以及中心局位置处的开放云之间的光连接。此外，我们设计了高效启发式算法，其在经济效益与网络资源利用率方面显著优于传统贪婪资源分配算法和基于强化学习的算法。