The growing complexity of next-generation telecommunication networks demands more intelligent and adaptive management approaches to address the diverse requirements of emerging services. This paper proposes a novel network slicing framework that integrates the Multi-Access Technology Real-Time Intelligent Controller (mATRIC) within the O-RAN system, enabling resource unification of 5G and Wi-Fi networks and efficient allocation. The framework dynamically adapts to unify the 5G and Wi-Fi access technologies. Specifically, we propose slice deployment policies based on two Deep Reinforcement Learning (DRL) algorithms: the on-policy Trust Region Policy Optimization (TRPO) and the off-policy Soft Actor-Critic (SAC) approaches, which aims to optimize slice deployments across varying service requirements. To evaluate the effectiveness of the proposed policies, we compare them against the on-policy Advantage Actor-Critic (A2C) and off-policy Deep Deterministic Policy Gradient (DDPG) methods. Extensive simulations demonstrate that the on-policy algorithms achieve superior performance in terms of slice acceptance ratios and resource utilization, highlighting their potential to enhance slice deployment in future heterogeneous networks beyond 5G and into 6G.

Network slicing technology promises to be a critical enabler of fifth-generation (5G) and sixth-generation (6G) wireless networks, allowing the infrastructure to be divided into several virtual slices. Mobile network operators are focused on developing novel solutions to implement various slices to address diverse use cases and guarantee key performance indicators (KPIs) such as latency, resource utilization, and energy efficiency. Energy efficiency (EE) KPIs are particularly important during slice deployments to ensure a reduced carbon footprint. However, deploying slices with high energy efficiency presents significant challenges. This paper proposes an energy-aware design policy for deploying slices by optimizing energy consumption and deployment capacity. A deep reinforcement learning (DRL) approach is employed, utilizing an actor-critic architecture to train a learning network modeled with a pointer network structure and an attention mechanism. A search strategy is also proposed to refine learning parameters and determine the final design policy. Compared to two existing approaches, the proposed algorithms demonstrate improved performance in terms of energy efficiency and cumulative acceptance ratio. Specifically, the EE KPI achieved by the proposed approach is enhanced to 69.7%.

End-to-End network slicing is an emerging technology with huge potential to carry flexible resource provisioning for the next generation of mobile networks (5G). With the powerful SDN and NFV technologies, multiple and logical network slices of various services can be deployed to the same underlying infrastructure. Physical resources are flexibly configured for different use cases. Therefore, it is necessary to explore how to guide the implementation of network slices via a general design policy. In this paper, we propose a service-aware network slicing design policy for 5G use cases, not only to satisfy various service requirements for different applications but also to enhance resource efficiency with fluctuated traffic demands. Firstly, multiple design objectives of slices are presented to guarantee different services in diverse use cases. Then we formulate a deterministic optimization model of the network slicing design problem and further extend it into a robust version considering uncertain traffic demands. Besides, a heuristic algorithm is proposed to obtain a promising network slicing design policy with the low computational effort, which is inspired by the multiple-objective particle swarm optimization (MOPSO) algorithm. The effectiveness of the present formulations and algorithms is verified by a series of simulations.

Network Slicing is regarded as a promising tech-nology to provide required services for different application scenarios by means of supporting multiple slices to embed the underlying physical network. However, it is still a challenging task to design network slicing aiming at providing specific services for different types of tenants while making efficient use of physical resources. Thus, the paper attempts to tackle the problem by proposing a novel end-to-end network slicing design policy. We start from modeling the mathematical model of the physical network and network slicing requests by using the Formal Concept Analysis methodology. Then, multiple design objectives for various use cases are defined, and the relevant resource constraints are also provided. Moreover, we propose a meta-heuristic algorithm based on the Multi-Objective Particle Swarm Optimization for the network slicing design problem. Extensive simulations illustrate that the resource efficiency of our proposed algorithm is better than the existing algorithms in terms of both node and link resources utilization.