The evolution of 6G envisions a wide range of applications and services characterized by highly differentiated and stringent Quality of Service (QoS) requirements. Open Radio Access Network (O-RAN) technology has emerged as a transformative approach that enables intelligent software-defined management of the RAN. A cornerstone of O-RAN is the RAN Intelligent Controller (RIC), which facilitates the deployment of intelligent applications (xApps and rApps) near the radio unit. In this context, QoS management through O-RAN has been explored using network slice and machine learning (ML) techniques. Although prior studies have demonstrated the ability to optimize RAN resource allocation and prioritize slices effectively, they have not considered the critical integration of Service Level Agreements (SLAs) into the ML learning process. This omission can lead to suboptimal resource utilization and, in many cases, service outages when the target Key Performance Indicators (KPIs) are not met. This work introduces RSLAQ, an innovative xApp designed to ensure robust QoS management for RAN slicing while incorporating SLAs directly into its operational framework. RSLAQ translates operator policies into actionable configurations, guiding resource distribution and scheduling for RAN slices. Using deep reinforcement learning (DRL), RSLAQ dynamically monitors RAN performance metrics and computes optimal actions, embedding SLA constraints to mitigate conflicts and prevent outages. Extensive system-level simulations validate the efficacy of the proposed solution, demonstrating its ability to optimize resource allocation, improve SLA adherence, and maintain operational reliability (> 95%) in challenging scenarios.

As 6G scenarios grow in complexity, network operators need an automated and optimized approach to managing Quality-of-Service (QoS) in the radio access network (RAN). Transitioning from resource-focused management to one that considers evolving operator intents is essential. This can be achieved by translating service-level agreement (SLA) intents into operational rules to ensure compliance and save costs. O-RAN introduced the RAN intelligent controller (RIC) and intelligent applications (xApps) to enhance the RAN operation. Furthermore, RAN slicing has been shown as the most promising method to provide O-RAN-based QoS in 6G. However, while existing methods optimize resources allocated per slice, they often overlook SLAs. This leads to suboptimal resource usage and outages when the system fails to meet target key performance indicators (KPIs). This work introduces SLAQ, an xApp designed to translate service operator requirements to optimize spectrum resource sharing. SLAQ monitors key performance metrics (KPMs) and employs deep reinforcement learning (DRL) to make decisions within the RAN. SLA policies are modeled and integrated into the xApp to optimize resource distribution while preventing SLA conflicts and outages. Our highly-detailed system-level simulations show that SLAQ effectively learns optimal actions, achieving high communication reliability, i.e., close to $99 \%$ for ultra-reliable low-latency communications.

User demand on network communication infrastructure has never been greater with applications such as extended reality, holographic telepresence, and wireless brain-computer interfaces challenging current networking capabilities. Open RAN (O-RAN) is critical to supporting new and anticipated uses of 6G and beyond. It promotes openness and standardisation, increased flexibility through the disaggregation of Radio Access Network (RAN) components, supports programmability, flexibility, and scalability with technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and cloud, and brings automation through the RAN Intelligent Controller (RIC). Furthermore, the use of xApps, rApps, and Artificial Intelligence/Machine Learning (AI/ML) within the RIC enables efficient management of complex RAN operations. However, due to the open nature of O-RAN and its support for heterogeneous systems, the possibility of misconfiguration problems becomes critical. In this paper, we present a thorough analysis of the potential misconfiguration issues in O-RAN with respect to integration and operation, the use of SDN and NFV, and, specifically, the use of AI/ML. The opportunity for AI/ML to be used to identify these misconfigurations is investigated. A case study is presented to illustrate the direct impact on the end user of conflicting policies amongst xApps along with a potential AI/ML-based solution to this problem. This research presents a first analysis of the impact of AI/ML on misconfiguration challenges in O-RAN.