Modern connected vehicles (CVs) frequently require diverse types of content for mission-critical decision-making and onboard users' entertainment. These contents are required to be fully delivered to the requester CVs within stringent deadlines that the existing radio access technology (RAT) solutions may fail to ensure. Motivated by the above consideration, this paper exploits content caching in vehicular edge networks (VENs) with a software-defined user-centric virtual cell (VC) based RAT solution for delivering the requested contents from a proximity edge server. Moreover, to capture the heterogeneous demands of the CVs, we introduce a preference-popularity tradeoff in their content request model. To that end, we formulate a joint optimization problem for content placement, CV scheduling, VC configuration, VC-CV association and radio resource allocation to minimize long-term content delivery delay. However, the joint problem is highly complex and cannot be solved efficiently in polynomial time. As such, we decompose the original problem into a cache placement problem and a content delivery delay minimization problem given the cache placement policy. We use deep reinforcement learning (DRL) as a learning solution for the first sub-problem. Furthermore, we transform the delay minimization problem into a priority-based weighted sum rate (WSR) maximization problem, which is solved leveraging maximum bipartite matching (MWBM) and a simple linear search algorithm. Our extensive simulation results demonstrate the effectiveness of the proposed method compared to existing baselines in terms of cache hit ratio (CHR), deadline violation and content delivery delay.
翻译:现代联网汽车(CV)常常需要不同类型的内容,以进行核心决策和满足乘客的娱乐需求。这些内容需要在严格的截止期限内完全传递到请求的 CV,而现有的无线接入技术(RAT)解决方案可能无法保证这一点。为了解决这一问题,本文利用车辆边缘网络(VEN)中的内容缓存以及基于软件定义的以用户为中心的虚拟单元(VC)为基础的 RAT 解决方案,从附近的边缘服务器传递所请求的内容。此外,为了捕捉 CV 的异构需求,我们在其内容请求模型中引入了偏好-流行度的权衡。为此,我们制定了一个联合优化问题,旨在通过内容放置、CV 调度、VC 配置、VC-CV 关联和无线资源分配来最小化长期内容传递延迟。然而,联合问题非常复杂,不能在多项式时间内有效解决。因此,我们将原始问题分解为缓存放置问题和给定缓存放置策略的内容传递延迟最小化问题。我们使用深度强化学习(DRL)作为第一个子问题的学习解决方案。此外,我们将延迟最小化问题转化为基于优先级的加权和速率(WSR)最大化问题,利用最大二分匹配(MWBM)和简单的线性搜索算法进行求解。我们进行了大量的模拟实验,结果表明了所提出方法与现有基线方案相比在缓存命中率(CHR)、截止期限违规和内容传递延迟方面的有效性。