This paper addresses energy-efficient processing in the Open Radio Access Network (O-RAN) within the O-RAN Cloud (O-Cloud) infrastructure, focusing on optimal processor power scaling while scheduling packets under delay constraints. Traditionally, real-time packet scheduling tends to be suboptimal, as it prioritizes processing capacity over energy efficiency. To address this issue, we formulate a Mixed Integer Programming (MIP) model and develop a heuristic approach, along with a Convolutional Neural Network(CNN) for real-time scheduling. To enhance the CNN model, we introduce the CNN Binary Search Scheduling (CNN-BSS) algorithm, guided by the heuristic solution. Simulations show a 29.17 % reduction in energy consumption, significantly outperforming conventional methods.

Cooperative multi-agent reinforcement learning (MARL) is a promising solution for many large-scale multi-agent system (MAS) scenarios. A MARL framework is usually based on a decentralized scheme that enables communication between all agents in a given architecture. The agents exchange information to maximize their average reward and increase the overall system performance. However, this decentralized information sharing results in high communication costs, which is a critical issue for environments with limited communication bandwidth. On the other hand, a predefined inter-agent communication architecture may limit potential cooperation. This paper addresses such issues in a vehicle-to-everything (V2X) network, a typical example of MAS with strict Quality of Service (QoS) requirements. For efficient utilization of limited network resources, a solution to the resource-sharing problem between Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) links is required. We propose a POST-Attentional Communication Actor-Critic (POST-2AC) model that learns when communication is needed and how to integrate shared information for cooperative decision-making. Our learning method uses an attention approach combined with the critic-network to label the agents local information based on its importance so that each agent learns to trade off its performance and communication cost. The simulation results show that the proposed model achieves better performance than the state-of-the-art baselines.

Due to the rapid adoption of 5G networks and the increasing number of devices and base stations (gNBs) connected to it, manually identifying malfunctioning machines or devices that cause a part of the networks to fail becomes more challenging. Furthermore, data collected from the networks are not always sufficient. To overcome these two issues, we proposed a novel root cause analysis (RCA) framework that integrates graph neural networks (GNNs) with graph structure learning (GSL) to infer hidden dependencies from available data. The learned dependencies are the graph structure utilized to predict the root cause machines or devices. We found that despite the fact that the data is often incomplete, the GSL model can infer fairly accurate hidden dependencies from data with a large number of nodes and generate informative graph representation for GNNs to identify the root cause. Our experimental results showed that higher accuracy of identifying a root cause and victim nodes can be achieved when the number of nodes in an environment is increased.

5G is the fifth generation of cellular networks. It enables billions of connected devices to gather and share information in real time; a key facilitator in Industrial Internet of Things (IoT) applications. It has more capabilities in terms of bandwidth, latency/delay, processing powers and flexibility to utilize either edge or cloud resources. Furthermore, 6G is expected to be equipped with the new capability to converge ubiquitous communication, computation, sensing and controlling for a variety of sectors, which heightens the complexity in a more heterogeneous environment This increased complexity, combined with energy efficiency and Service Level Agreement (SLA) requirements makes application of Machine Learning (ML) and distributed ML necessary. A decentralized approach stemming from distributed learning is a very attractive option compared with a centralized architecture for model learning and inference. Distributed ML exploits recent Artificial Intelligence (AI) technology advancements to allow collaborated ML, whilst safeguarding private data, minimizing both communication and computation overhead along with addressing ultra-low latency requirements. In this paper, we review a number of distributed ML architectures and designs, that focus on optimizing communication, computation and resource distribution. Privacy, information security and compute frameworks, are also analyzed and compared with respect to different distributed ML approaches. We summarize the major contributions and trends in this area and highlight the potential of distributed ML to help researchers and practitioners make informed decisions on selecting the right ML approach for 5G and Beyond related AI applications. To enable distributed ML for 5G and Beyond, communication, security, and computing platform often counter balance each other, thus, consideration and optimization of these aspects at an overall system level is crucial to realize the full potential of AI for 5G and Beyond. These different aspects do not only pertain to 5G, but will also enable careful design of distributed machine learning architectures to circumvent the same hurdles that will inevitably burden 5G and Beyond network generations. This is the first survey paper that brings together all these aspects for distributed ML.

Mobile Edge Computing (MEC) refers to the concept of placing computational capability at the edge of the network to reduce the latency in handling the client requests. The performance of an edge server is adversely affected when it is overloaded, especially if it crashes due to overload and causes service failures. In this paper, a solution to prevent node from getting overloaded is analyzed by introducing an admission control policy. An adaptive admission control policy based low complexity RL (Reinforcement Learning) SALMUT (Structure-Aware Learning for Multiple Thresholds) is validated using several scenarios mimicking real world deployments. This approach performs as well as to the state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic), but requires an order of magnitude less time to train, and outputs easily interpretable policy.

In this paper, a rotating clustering scheme to increase the average achievable rates for the downlink of a coordinated multicell multiple-input multiple-output (MIMO) system is proposed. The users' data signals are transmitted cooperatively via the base stations of a cluster within the sectorized multicell heterogeneous cellular network. The performance of the system with the proposed clustering methods using a simulated annealing algorithm as the user scheduler is investigated. The simulations demonstrate the effectiveness of the proposed methods in conjunction with the throughput maximization and proportionally fair scheduling metrics.