The Sixth-Generation (6G) networks are expected to support a multitude of vertical services, each with distinct intent requirements. Managing such diverse demands requires intelligent automation and dynamic resource allocation that aligns service objectives with limited network resources. This paper presents an Intent-Based Network (IBN) optimization framework to automate resource management and resolve conflicting service objectives. The Multi-Objective IBN Slicing (M2O-IBNS) system translates high-level service intents into concrete network configuration targets and jointly maximizes network throughput while minimizing both Energy Consumption (EC) and the costs associated with Slice Creation (SC). It proactively detects and resolves conflicts among slices by dynamically adjusting their Service Level Agreement (SLA) thresholds based on predefined priorities. Our numerical results demonstrate the efficacy of M2O-IBNS by consistently achieving solutions within a tight 1% optimality gap from the theoretical upper bound, and significantly outperforming State-of-the-Art (SoA) approaches by increasing throughput by up to 24.8%. Furthermore, our analysis reveals a critical trade-off: prioritizing the SLA of a single high-priority slice can secure its performance objectives, but at the cost of a significant reduction in overall network performance. For instance, imposing a strict SLA for one slice led to a 65% decrease in the cumulative throughput of all other slices, although it also reduced EC by 30% and SC costs by 77%. These findings demonstrate that our intent-driven optimization provides a powerful mechanism for Mobile Network Operators (MNOs) to strategically manage network resources, prioritize critical services, and quantitatively balance performance trade-offs across the entire network.

The Sixth-Generation (6G) mobile networks aim to automate network resource allocation and support both new and existing vertical services, each with diverse Quality of Service (QoS) intent requirements. Digital Service Providers (DSPs) must consider specific intent expectations, and targets set by different services, and re-configure and prioritize the most critical intents when resources are insufficient. This paper presents an optimization model for a flexible network paradigm using an Intent-Based Network Slicing (IBNS) framework that can manage the complexity of QoS intents and identify slice intent conflicts through closed-loop evaluation and monitoring. It dynamically adjusts the slice configurations to handle and mitigate detected conflicts by executing the agreed-upon Service Level Agreement (SLA) objectives (%) for higher-priority slices to ensure that critical intents are addressed. According to the results, this approach successfully meets the SLA target we set for slice but it negatively impacts the performance of other slices and degrades their slice capacity.

The advent of Sixth-Generation (6G) mobile networks is expected to facilitate new services, each with diverse Quality of Service (QoS) intent requirements. Digital Service Providers (DSPs) must align with these requirements by prioritizing critical intents and reconfiguring resources when necessary. Network Slicing (NS) and Intent-Based Network (IBN) management have become essential tools for addressing the intent expectations of both current and future 6G services. This paper presents a robust optimization framework aimed at maximizing throughput while minimizing Energy Consumption (EC). The framework creates isolated network slices tailored to specific QoS intents, ensuring compliance with the performance metrics defined in Service Level Agreements (SLAs). Our findings indicate that, while this approach effectively meets SLA targets for specific slices, it conflicts with intent expectation of other slices and can compromise their performance, leading to significant degradation in throughput and EC costs. For instance, enforcing SLA objective for one specific slice means improving slice capacity while it has a huge decrease in the average throughput up to 65% and a decreased EC cost up to 81%.

The advent of the Fifth Generation (5G) and Beyond (B5G) mobile communications has led to the emergence of novel vertical services, necessitating the reconfiguration of network architecture to address stringent Quality of Service (QoS) demands, while simultaneously supporting reliable Vehicle-to-everything (V2X) communications. In this paper, our primary focus lies in enhancing the QoS of V2X applications utilizing Open Radio Access Network (O-RAN) architecture. To this end, we propose a novel optimization framework that creates dynamic RAN slicing covering tailored functional split selection per slice, and Multi-access Edge Computing (MEC) server placement within O-RAN architecture. In addition, Packet Duplication (PD) techniques are used in the proposed work to enhance V2X application reliability. First, we define the proposed problem as convex and linear, and then we useBendersa distributed technique that accelerates performance while ensuring the global optimal solution. The approach is validated through simulations, which demonstrate its capability to meet the QoS requirements of V2X applications and outperform existing methods. According to the results, there is a trade-off between the achieved throughput and the extra overhead due to replicated packets which incurs extra resources such as radio and computation to the network. For example, our approach can reach up to 42% increases in the served demand at the expense of 6% more spectrum usage when compared to the literature.

The unprecedented surge in mobile data traffic, along with the wide range of services and the corresponding different performance requirements, has raised the need for a new mobile network architecture. In that sense, 5G has been conceived as a software defined network able to provide service-tailored connectivity. Network slicing is a key mechanism to serve efficiently the diversified service requirements. In this paper, we formulate the joint RAN slicing and functional split with optimization of centralization degree (CD) and throughput. Our results show that even though in terms of CD we have more costs, we can better meet the service requirements and also have more throughput in the network.