The increasing complexity of Beyond 5G and 6G networks necessitates new paradigms for autonomy and assur- ance. Traditional O-RAN control loops rely heavily on RIC- based orchestration, which centralizes intelligence and exposes the system to risks such as policy conflicts, data drift, and unsafe actions under unforeseen conditions. In this work, we argue that the future of autonomous networks lies in a multi-agentic architecture, where specialized agents collaborate to perform data collection, model training, prediction, policy generation, verification, deployment, and assurance. By replacing tightly- coupled centralized RIC-based workflows with distributed agents, the framework achieves autonomy, resilience, explainability, and system-wide safety. To substantiate this vision, we design and evaluate a traffic steering use case under surge and drift conditions. Results across four KPIs: RRC connected users, IP throughput, PRB utilization, and SINR, demonstrate that a naive predictor-driven deployment improves local KPIs but destabilizes neighbors, whereas the agentic system blocks unsafe policies, preserving global network health. This study highlights multi- agent architectures as a credible foundation for trustworthy AI- driven autonomy in next-generation RANs.

Accurate channel estimation is essential for reliable downlink synchronization (DLSync) in 5G and beyond wireless systems, especially during the initial access (IA) phase. This work focuses on synchronization signal blocks (SSBs), which play a crucial role in delivering system information from the base station (gNodeB) to user equipment (UE). We propose a deep learning-based approach using an inception-style neural network to estimate the channel across the SSB time-frequency grid. Our model outperforms traditional techniques such as demodulation reference signal (DMRS) interpolation and least squares (LS) estimation, especially under practical wireless conditions like multipath delay spread and Doppler shift. The proposed model achieves a bit error rate (BER) of 10−4at an SNR of 20 dB, significantly better than the 3 × 10−3BER of conventional methods. Moreover, we have observed 4-5 dB gain at high SNR with respect to LMMSE and LS estimators. These results demonstrate that our model offers more reliable and energy-efficient synchronization, even in challenging real-world environments.

Call muting-unexpected silences during voice calls due to extended RTP packet loss is a major challenge in high-mobility 5G environments, severely degrading Mean Opinion Score (MOS) and user experience. We propose DRLCQ, a Deep Reinforcement Learning-based framework that dynamically tunes Cell Individual Offset (CIO) in real time to reduce mute events and enhance voice quality. Integrated as an xApp within the O-RAN Near-RT RIC, DRLCQ leverages live network KPIs (e.g., SINR, jitter, packet loss) to learn optimal handover decisions. Evaluated against static and heuristic baselines, DRLCQ achieves over 20% fewer call mute incidents and up to 85% higher MOS, demonstrating a scalable and intelligent solution for AI-native RAN control.

This paper proposes a Generative AI-based Digital Twin (GDT) platform for automated network feature performance evaluation, designed for Beyond 5G (B5G) networks. The platform addresses the inefficiencies of manual evaluation by utilizing a conditional Generative Adversarial Network (cGAN) to simulate network performance based on historical data and new AI/ML features. The Network GDT integrates a novel Digital Twin Augmenting Condition (DTAC) framework, allowing for real-time simulation and performance evaluation of network features. This system significantly reduces the time and cost associated with manual evaluations, improves decision-making, and optimizes Quality of Service (QoS) and Quality of Experience (QoE). The cGAN-based model dynamically generates synthetic data, enabling comprehensive performance insights and proactive AI solution testing under various network scenarios. Experimental results demonstrate high prediction accuracy for congestion use case, validating the robustness of the proposed system. The platform's dual-phase strategy ensures that AI-based solutions are rigorously tested in simulated environments before deployment in real networks, minimizing risks and enhancing stability. This approach provides a scalable and efficient solution for future B5G networks, paving the way for more reliable and optimized wireless communication systems.

The evolution of telecommunications technology is at the cusp of a major transition from 5G to Beyond 5G networks. With this imminent shift, the demand for robust and efficient mitigation solutions has become increasingly vital. AI-based mitigation solutions for solving B5G network problems are directly pushed into the actual field or manually evaluated by the operator first. With Big Data involved in 5G and Beyond, evaluating them manually or without evaluation, pushing them into the real field might have severe consequences in the actual network. There is no intelligent and proactive platform to test the implications of ML models on the networks. In this paper, we propose a pioneering approach that involves the development of an AI-based 5G network clone to serve as a performance evaluation ground for AI-based mitigation solutions tailored for B5G networks. Our methodology outlines the initial phase of evaluating these mitigation solutions within the simulated environment of the AI-based 5G network clone, followed by their subsequent deployment in real-world network infrastructures. This strategy aims to ascertain the efficacy, reliability, and adaptability of the proposed solutions before their integration into the next-generation B5G networks.

This research paper presents a novel framework designed to automate the provisioning of ML services, intelligently tailoring the ML package based on various factors such as service profiles, regional resource usage patterns, operator-defined KPIs, and current ML resource utilization in the network. Our proposed framework employs dynamic and automatic cell grouping techniques using similarity metric correlation algorithms across Base Stations (BS). It selectively trains a representative cell within each group using the best available Machine Learning (ML) model automatically determined. The trained model of the representative cell is subsequently applied to the remaining BS within the same group. To evaluate the effectiveness of our solution, we conducted extensive evaluations using real-world operator data from 5G networks, encompassing a wide range of network KPIs. The results demonstrate the remarkable impact of our framework, showcasing substantial resource savings in terms of ML Server Processing time, memory consumed, and server util percentage. Furthermore, our approach significantly reduces the number of ML trainings required, all while maintaining high ML prediction accuracies. On average, our solution achieves an impressive 39.94% reduction in ML server processing time, a substantial 60.46% reduction in ML server memory, a remarkable 75.11% reduction in server util percentages, and a total of 649 fewer ML trainings for 5G operator data across various network KPIs. These achievements highlight the efficacy of our framework in optimizing resource allocation without compromising the accuracy of ML predictions.

Container technology has revolutionized the way software is being packaged and run. The telecommunications industry, now challenged with the 5G transformation, views containers as the best way to achieve agile infrastructure that can serve as a stable base for high throughput and low latency for 5G edge applications. These challenges make optimal scheduling of performance-sensitive containerized workflows a matter of emerging importance. Meanwhile, the wide adoption of Kubernetes across industries has placed it as a de-facto standard for container orchestration. Several attempts have been made to improve Kubernetes scheduling, but the existing solutions either do not respect current scheduling rules or only considered a static infrastructure viewpoint.To address this, we propose NetMARKS - a novel approach to Kubernetes pod scheduling that uses dynamic network metrics collected with Istio Service Mesh. This solution improves Kubernetes scheduling while being fully backward compatible. We validated our solution using different workloads and processing layouts. Based on our analysis, NetMARKS can reduce application response time up to 37 percent and save up to 50 percent of inter-node bandwidth in a fully automated manner. This significant improvement is crucial to Kubernetes adoption in 5G use cases, especially for multi-access edge computing and machine-to-machine communication.