Integrated Access and Backhaul (IAB) is a cost-effective and adaptable solution for deploying ultra-dense next-generation (5G and 6G) cellular networks to increase the likelihood of Line-of-Sight (LOS) coverage. This technology allows wireless backhaul connections to be established using the same technology and specifications as available in the access links. However, the absence of a physical testbed or a dataset that can be used for simulation in the millimeter wave (mmWave) band prevents researchers’ validation of the proposed algorithms in the IAB scenario. In this paper, we propose a novel data generator based on a Generative Adversarial Network (GAN), trained on a real dataset from a mobile network that operates in Europe, and maintains a significant market share that returns accurate traffic data for an IAB network. Also, we introduce IAB-CNPos, an intelligent IAB node positioning framework using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that indicates IAB node positions to increase the coverage network with minimal deployment cost. Furthermore, we integrate this data generator with the SeBaSi simulator (an IAB simulator based on Sionna), which obtains accurate, data-consistent, and realistic end-to-end IAB simulation results. The performance results indicate that the data generator successfully passes the Kolmogorov–Smirnov (KS) criterion, so, it could operate as a verified data generator. Furthermore, we use the SeBaSi simulator, integrated with the data generator, to evaluate the performance of an IAB network in the London City scenario.

Open Radio Access Network (O-RAN) architectures enhance flexibility for 6G and NextG networks. However, it also brings significant challenges in O-RAN testing with evaluating abundant, high-dimensional key performance indicators (KPIs). In this paper, we introduce a novel two-stage framework to learn temporally-aware low-dimensional representations of O-RAN testing KPIs. To be specific, stage one employs an information-theoretic H-score to train a hybrid self-attentive transformer and echo state network (ESN) reservoir, called Transformer-ESN, capturing temporal dynamics and producing task-aligned 8-dimensional embeddings. Stage two evaluates these embeddings by training a lightweight multilayer perceptron (MLP) predictor exclusively on them for key target KPIs such as reference signal received quality (RSRQ) and spectral efficiency. Using real-world O-RAN testbed data (video streaming with interference), our approach demonstrates a significant advantage specifically when training samples are very limited. In this scenario, the low-dimensional representations learned from the Transformer-ESN yield mean square error (MSE) reductions of up to 41.9% for RSRQ and 29.9% for spectral efficiency compared to predictions from the original high-dimensional data. The framework exhibits high efficiency for O-RAN testing, significantly reducing testing complexities for O-RAN systems.

The H2020 Distributed management of Network Slices in beyond 5G(MonB5G) project aims to provide zero-touch management and orchestration to support network slicing at scale to reduce the management burden on mobile operators by leveraging distribution of operations along with advanced data-driven Artificial Intelligence (AI)-based mechanisms. However, while this approach shows promise and large companies with abundant data and ML expertise are developing powerful MLdriven services, a critical aspect that remains to be analyzed is its business case. The vast majority of potentially valuable ML services, such as predictive maintenance, Quality of Service (QoS) optimization, network security enhancements, remain stuck at the idea or prototype stage. This paper delves into an analysis of how the MonB5G solutions in particular the tuples (Monitoring System (MS), Analytics Engine (AE), Decision Engine (DE) and Actuator (ACT) could be applied within the network management and orchestration market while investigating various business models and value chains. Numerical results based on experimental data have also been performed to evaluate the OpEX (Operational Expenditure) benefits associated with different network management techniques, for centralized and distributed systems.

This paper demonstrates an implementation of Federated Learning (FL) for streaming applications using cloud-native technology. Compared to a centralized management, by adopting a decentralized approach, the FL method improves convergence time, reduces communication overhead, and increases network energy efficiency. The cloud-native FL architecture presented comprises three sites, each with its own Kubernetes (K8s) cluster. The edge sites run FL Analytical Engines (AEs)/clients for local training and updates, and the central site runs the aggregation server for FL training. Some other relevant workloads deployed at the clusters are the video streaming server, the orchestrator, and monitoring components. As for the RAN, we showcase a multi-gNB setup from which we obtain monitoring data via custom sampling functions. Following the description of the testbed infrastructure and setup, this demonstration presents the real-time visualization of network parameters during FL training, and the enhancement of video streaming through proactive Central Processing Unit (CPU) scaling, made possible by the resource forecasting.

In recent years, wireless networks are evolving complex, which upsurges the use of zero-touch artificial intelligence (AI)-driven network automation within the telecommunication industry. In particular, network slicing, the most promising technology beyond 5G, would embrace AI models to manage the complex communication network. Besides, it is also essential to build the trustworthiness of the AI black boxes in actual deployment when AI makes complex resource management and anomaly detection. Inspired by closed-loop automation and Explainable Artificial intelligence (XAI), we design an Explainable Federated deep learning (FDL) model to predict per-slice RAN dropped traffic probability while jointly considering the sensitivity and explainability-aware metrics as constraints in such non- IID setup. In precise, we quantitatively validate the faithfulness of the explanations via the so-called attribution-based log-odds metric that is included as a constraint in the run-time FL optimization task. Simulation results confirm its superiority over an unconstrained integrated-gradient (IG) post-hoc FDL baseline.

The evolution of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) in the telecommunications industry have intensified the issues of network management at large scales. Dynamic service orchestration and adaptive resource allocation became a necessity for network operators to manage the rapid growth of users and data-intensive applications. The impact of network automation on energy consumption and overall operating costs is often overlooked. Guaranteeing strict performance constraints of Ultra-Reliable Low Latency Communication (URLLC) services while enhancing energy efficiency is one of the major critical problems of future communication networks, given the urgency to reduce carbon emissions and energy consumption. In this work, we study the problem of zero-touch Service Function Chain (SFC) orchestration for multi-domain networks, targeting the latency reduction of URLLC services while improving energy efficiency for beyond-5G networks. Specifically, we propose SCHE2MA, a Service CHain Energy-Efficient Management framework based on distributed Reinforcement Learning (RL), that can intelligently deploy SFCs with shared VNFs per se into a multi-domain network. Finally, we evaluate SCHE2MA through model validation and simulation while demonstrating its ability to jointly reduce average service latency by 103.4% and energy consumption by 17.1% compared to a centralized RL solution.

Artificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the final consumer in the form of advanced digital use-cases. In this paper, we propose a novel model-free deep reinforcement learning (DRL) framework, called collaborative statistical Actor-Critic (CS-AC) that enables a scalable and farsighted slice performance management in a 6G-like RAN scenario that is built upon mobile edge computing (MEC) and massive multiple-input multiple-output (mMIMO). In this intent, the proposed CS-AC targets the optimization of the latency cost under a long-term statistical service-level agreement (SLA). In particular, we consider the Q-th delay percentile SLA metric and enforce some slice-specific preset constraints on it. Moreover, to implement distributed learners, we propose a developed variant of soft Actor-Critic (SAC) with less hyperparameter sensitivity. Finally, we present numerical results to showcase the gain of the adopted approach on our built OpenAI-based network slicing environment and verify the performance in terms of latency, SLA Q-th percentile, and time efficiency. To the best of our knowledge, this is the first work that studies the feasibility of an AI-driven approach for massive network slicing under statistical SLA.

To harness the full potential of beyond 5G (B5G) communication systems, zero-touch network slicing (NS) is viewed as a promising fully-automated management and orchestration (MANO) system. This paper proposes a novel knowledge plane (KP)-based MANO framework that accommodates and exploits recent NS technologies and is termed KB5G. Specifically, we deliberate on algorithmic innovation and artificial intelligence (AI) in KB5G. We invoke a continuous model-free deep reinforcement learning (DRL) method to minimize energy consumption and virtual network function (VNF) instantiation cost. We present a novel Actor-Critic-based NS approach to stabilize learning called, twin-delayed double-Q soft Actor-Critic (TDSAC) method. The TDSAC enables central unit (CU) to learn continuously to accumulate the knowledge learned in the past to minimize future NS costs. Finally, we present numerical results to showcase the gain of the adopted approach and verify the performance in terms of energy consumption, CPU utilization, and time efficiency.

Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new paradigm enabling the automation of resource management and orchestration (MANO) in multi-tenant beyond 5G (B5G) networks. In this paper, we tackle the problem of cloud-RAN (C-RAN) joint slice admission control and resource allocation by first formulating it as a Markov decision process (MDP). We then invoke an advanced continuous deep reinforcement learning (DRL) method called twin delayed deep deterministic policy gradient (TD3) to solve it. In this intent, we introduce a multi-objective approach to make the central unit (CU) learn how to re-conFigure computing resources autonomously while minimizing latency, energy consumption and virtual network function (VNF) instantiation cost for each slice. Moreover, we build a complete 5G C-RAN network slicing environment using OpenAI Gym toolkit where, thanks to its standardized interface, it can be easily tested with different DRL schemes. Finally, we present extensive experimental results to showcase the gain of TD3 as well as the adopted multi-objective strategy in terms of achieved slice admission success rate, latency, energy saving and CPU utilization.