Title: Deep Reinforcement Learning-Based RAN Slicing with Efficient Inter-Slice Isolation in Tactical Wireless Networks Show Chinese title

Title: 基于深度强化学习的战术无线网络RAN切片与高效片间隔离

Abstract: The next generation of tactical networks (TNs) is poised to further leverage the key enablers of 5G and beyond 5G (B5G) technology, such as radio access network (RAN) slicing and the open RAN (O-RAN) paradigm, to unlock multiple architectural options and opportunities for a wide range of innovative applications. RAN slicing and the O-RAN paradigm are considered game changers in TNs, where the former makes it possible to tailor user services to users requirements, and the latter brings openness and intelligence to the management of the RAN. In TNs, bandwidth scarcity requires a dynamic bandwidth slicing strategy. Although this type of strategy ensures efficient bandwidth utilization, it compromises RAN slicing isolation in terms of quality of service (QoS) performance. To deal with this challenge, we propose a deep reinforcement learning (DRL)-based RAN slicing mechanism that achieves a trade-off between efficient RAN bandwidth sharing and appropriate inter- and intra-slice isolation. The proposed mechanism performs bandwidth allocation in two stages. In the first stage, the bandwidth is allocated to the RAN slices. In the second stage, each slice partitions its bandwidth among its associated users. In both stages, the slicing operation is constrained by several considerations related to improving the QoS of slices and users that in turn foster inter- and intra-slice isolation. The proposed RAN slicing mechanism is based on DRL algorithms to perform the bandwidth sharing operation in each stage. We propose to deploy the mechanism in an O-RAN architecture and describe the O-RAN functional blocks and the main DRL model lifecycle management phases involved. We also develop three different implementations of the proposed mechanism, each based on a different DRL algorithm, and evaluate their performance against multiple baselines across various parameters. Show Chinese abstract

Abstract: 下一代战术网络（TNs）准备进一步利用5G及超越5G（B5G）技术的关键推动因素，如无线接入网（RAN）切片和开放RAN（O-RAN）范式，以解锁多种架构选项和广泛创新应用的机会。 RAN切片和O-RAN范式被视为TNs中的变革者，其中前者使根据用户需求定制用户服务成为可能，而后者则为RAN管理带来了开放性和智能性。 在TNs中，带宽稀缺需要动态带宽切片策略。 尽管此类策略确保了带宽的有效利用，但在服务质量（QoS）性能方面损害了RAN切片隔离。 为了解决这一挑战，我们提出了一种基于深度强化学习（DRL）的RAN切片机制，以在高效的RAN带宽共享与适当的切片间和切片内隔离之间实现权衡。 所提出的机制分两个阶段进行带宽分配。 在第一阶段，带宽被分配给RAN切片。 在第二阶段，每个切片在其关联用户之间划分其带宽。 在两个阶段中，切片操作受到若干考虑因素的约束，这些因素旨在提高切片和服务的质量，从而促进切片间和切片内的隔离。 所提出的RAN切片机制基于DRL算法，在每个阶段执行带宽共享操作。 我们提议在O-RAN架构中部署该机制，并描述涉及的O-RAN功能块和主要DRL模型生命周期管理阶段。 我们还开发了三种不同的机制实现方式，每种都基于不同的DRL算法，并针对各种参数评估其相对于多个基线的性能。