The advancements of machine learning-based (ML) decision-making algorithms
created various research and industrial opportunities. One of these areas is
ML-based near-real-time network management applications (xApps) in Open-Radio
Access Network (O-RAN). Normally, xApps are designed solely for the desired
objectives, and fine-tuned for deployment. However, telecommunication companies
can employ multiple xApps and deploy them in overlapping areas. Consider the
different design objectives of xApps, the deployment might cause conflicts. To
prevent such conflicts, we proposed the xApp distillation method that distills
knowledge from multiple xApps, then uses this knowledge to train a single model
that has retained the capabilities of Previous xApps. Performance evaluations
show that compared conflict mitigation schemes can cause up to six times more
network outages than xApp distillation in some cases.

提出了一种 xApp 蒸馏方法，通过从多个 xApp 中蒸馏知识来训练一个保留了先前 xApp 能力的单一模型，相对于冲突缓解方案，在某些情况下 xApp 蒸馏可以减少多达六倍的网络故障。

xApp 蒸馏：B5G O-RAN 中基于人工智能的冲突缓解

xApp Distillation: AI-based Conflict Mitigation in B5G O-RAN

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.

本文提出并比较了 min-max fairness 和 Vickrey-Clarke-Groves（VCG）基于拍卖的 x-haul 和 DU-CU 资源分配机制，以创建一个可持续发展的多租户 O-RAN 生态系统，其中使用 PON 虚拟化技术的 x-haul 接口在 RU 和边缘云之间提供光学连接，并设计了有效的启发式算法来提高经济效率和网络资源利用率。