The increasing cloudification and softwarization of networks foster the
interplay among multiple independently managed deployments. An appealing reason
for such an interplay lies in distributed Machine Learning (ML), which allows
the creation of robust ML models by leveraging collective intelligence and
computational power. In this paper, we study the application of the two
cornerstones of distributed learning, namely Federated Learning (FL) and
Knowledge Distillation (KD), on the Wi-Fi Access Point (AP) load prediction use
case. The analysis conducted in this paper is done on a dataset that contains
real measurements from a large Wi-Fi campus network, which we use to train the
ML model under study based on different strategies. Performance evaluation
includes relevant aspects for the suitability of distributed learning operation
in real use cases, including the predictive performance, the associated
communication overheads, or the energy consumption. In particular, we prove
that distributed learning can improve the predictive accuracy centralized ML
solutions by up to 93% while reducing the communication overheads and the
energy cost by 80%.

通过研究分布式学习的两个基本方法 —— 联邦学习（FL）和知识蒸馏（KD）在 Wi-Fi 接入点（AP）负载预测用例上的应用，本文证明了分布式学习可以将集中式机器学习解决方案的预测准确性提高 93％，并将通信开销和能源成本降低 80％。

Wi-Fi AP 负载预测的分布式学习

Distributed Learning for Wi-Fi AP Load Prediction

The evolution of wireless mobile networks towards cloudification, where Radio
Access Network (RAN) functions can be hosted at either a central or distributed
locations, offers many benefits like low cost deployment, higher capacity, and
improved hardware utilization. Nevertheless, the flexibility in the functional
deployment comes at the cost of stringent fronthaul (FH) capacity and latency
requirements. One possible approach to deal with these rigorous constraints is
to use FH compression techniques. To ensure that FH capacity and latency
requirements are met, more FH compression is applied during high load, while
less compression is applied during medium and low load to improve FH
utilization and air interface performance. In this paper, a model-free deep
reinforcement learning (DRL) based FH compression (DRL-FC) framework is
proposed that dynamically controls FH compression through various configuration
parameters such as modulation order, precoder granularity, and precoder weight
quantization that affect both FH load and air interface performance. Simulation
results show that DRL-FC exhibits significantly higher FH utilization (68.7% on
average) and air interface throughput than a reference scheme (i.e. with no
applied compression) across different FH load levels. At the same time, the
proposed DRL-FC framework is able to meet the predefined FH latency constraints
(in our case set to 260 $\mu$s) under various FH loads.

通过深度强化学习控制前传带宽压缩，该方法在不同的前传负载水平下实现了显著的前传带宽利用率和空中接口吞吐量提升，并能够满足预定的前传时延约束。

基于学习的时延受限前端压缩优化在 C-RAN 中

Learning-Based Latency-Constrained Fronthaul Compression Optimization in  C-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。