Considering network slicing in a cellular network, one of the most intriguing tasks is slice enforcement over air interfaces across multiple cells. The challenges lie in several aspects. First, resources allocated to different slices must achieve soft isolation at the link level. Second, users’ diverse QoS requirements must be satisfied even when communication links experience fading and interference. Third, long-term slicing policies must be conformed, no matter how unbalanced they are. To address these challenges, link-level slice enforcement is first formulated as a resource allocation problem that minimizes radio resource consumption while ensuring link-level soft slice isolation, guaranteeing users’ diverse QoS requirements, and conforming to slicing policies. Next, this problem is tackled via a deep reinforcement learning (DRL) based approach, through which LinkSlice is designed as an iterative two-stage algorithm. The first stage determines transmission rates for each link based on DRL. It is embedded with a graph neural network (GNN) to characterize link interference. Based on the transmission rates from the first stage, the second stage allocates resources to each slice. Performance results show that LinkSlice converges quickly to a near-optimal solution. It gracefully tackles the three challenges of link-level slice enforcement while further improving throughput by 18.5%.

中文翻译：

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LinkSlice：基于深度强化学习的细粒度网络切片执行

考虑到蜂窝网络中的网络切片，最有趣的任务之一是跨多个小区的空中接口执行切片。挑战存在于几个方面。首先，分配给不同切片的资源必须在链路级别实现软隔离。其次，即使通信链路经历衰落和干扰，也必须满足用户多样化的 QoS 要求。第三，长期的切片政策，无论多么不平衡，都必须符合。为了应对这些挑战，链路级切片执行首先被定义为一个资源分配问题，在确保链路级软切片隔离的同时，最大限度地减少无线电资源消耗，保证用户多样化的 QoS 要求，并符合切片策略。下一个，这个问题是通过一种基于深度强化学习 (DRL) 的方法来解决的，通过该方法，LinkSlice 被设计为一种迭代的两阶段算法。第一阶段根据 DRL 确定每个链路的传输速率。它嵌入了一个图神经网络（GNN）来表征链路干扰。根据第一阶段的传输速率，第二阶段为每个切片分配资源。性能结果表明，LinkSlice 可以快速收敛到接近最优的解决方案。它优雅地解决了链路级切片执行的三个挑战，同时将吞吐量进一步提高了 18.5%。根据第一阶段的传输速率，第二阶段为每个切片分配资源。性能结果表明，LinkSlice 可以快速收敛到接近最优的解决方案。它优雅地解决了链路级切片执行的三个挑战，同时将吞吐量进一步提高了 18.5%。根据第一阶段的传输速率，第二阶段为每个切片分配资源。性能结果表明，LinkSlice 可以快速收敛到接近最优的解决方案。它优雅地解决了链路级切片执行的三个挑战，同时将吞吐量进一步提高了 18.5%。