The advent of 5G and the emergence of 6G networks demand unprecedented flexibility and efficiency in Radio Access Network (RAN) resource management to satisfy diverse service-level agreements (SLAs). Existing RAN slicing frameworks predominantly rely on per-slice resource reservation, which ensures performance isolation but leads to inefficient utilization, particularly under bursty traffic. We introduce HyRA, a hybrid resource allocation framework for RAN slicing that combines dedicated per-slice allocations with shared resource pooling across slices. HyRA preserves performance isolation while improving resource efficiency by leveraging multiplexing gains in bursty traffic conditions. We formulate this design as a bi-level stochastic optimization problem, where the outer loop determines the dedicated and shared resource budgets and the inner loop performs per-UE scheduling under a novel water-filling approach. By using the sample-average approximation, the Karush-Kuhn-Tucker (KKT) conditions of the inner loop, and Big-M encoding, we transform the problem into a tractable mixed-integer program that standard optimization solvers can solve. Extensive simulations under diverse demand patterns, SLA configurations, and traffic burstiness show that HyRA achieves up to 50-75% spectrum savings compared to dedicated-only and shared-only baselines. These results highlight HyRA as a viable approach for resource-efficient, SLA-compliant RAN slicing in future mobile networks.

翻译：5G的到来和6G网络的出现，对无线接入网（RAN）资源管理提出了前所未有的灵活性和效率要求，以满足多样化的服务等级协议（SLA）。现有的RAN切片框架主要依赖于每切片的资源预留，这确保了性能隔离，但导致了资源利用效率低下，尤其是在突发流量场景下。本文提出了HyRA，一种面向RAN切片的混合资源分配框架，它将每切片的专用资源分配与跨切片的共享资源池相结合。HyRA在保持性能隔离的同时，通过利用突发流量条件下的复用增益，提高了资源效率。我们将此设计建模为一个双层随机优化问题，其中外层循环确定专用和共享资源预算，内层循环则基于一种新颖的注水方法执行每用户设备（UE）调度。通过使用样本平均近似、内层循环的Karush-Kuhn-Tucker（KKT）条件以及Big-M编码，我们将该问题转化为一个可处理的混合整数规划问题，可由标准优化求解器求解。在多种需求模式、SLA配置和流量突发性下的广泛仿真表明，与纯专用和纯共享基线方案相比，HyRA可实现高达50-75%的频谱节省。这些结果凸显了HyRA作为未来移动网络中一种资源高效且符合SLA的RAN切片可行方案。