Efficient Content Delivery in User-Centric and Cache-Enabled Vehicular Edge Networks with Deadline-Constrained Heterogeneous Demands
Efficient Content Delivery in User-Centric and Cache-Enabled Vehicular Edge Networks with Deadline-Constrained Heterogeneous Demands
复制标题
DOI：
10.1109/tvt.2023.3300954
复制
发表时间：
2022-02
影响因子：
6.8
通讯作者：
Md Ferdous Pervej;Richeng Jin;Shih-Chun Lin;H. Dai
中科院分区：
文献类型：
--
作者：
Md Ferdous Pervej;Richeng Jin;Shih-Chun Lin;H. Dai
现代互联车辆（CV）经常需要各种类型的内容来进行关键任务决策和车载用户娱乐。这些内容需要在现有无线电接入技术（RAT）解决方案可能无法确保的严格期限内完全递送给请求者CV。出于上述考虑，本文利用车辆边缘网络（VENs）中的内容缓存与软件定义的以用户为中心的虚拟小区（VC）为基础的RAT解决方案，从邻近边缘服务器提供所请求的内容。此外，为了捕捉的CV的异构需求，我们引入了一个偏好流行的权衡在他们的内容请求模型。为此，我们制定了一个联合优化问题的内容放置，CV调度，VC配置，VC-CV关联和无线电资源分配，以尽量减少长期的内容传输延迟。然而，联合问题是高度复杂的，不能在多项式时间内有效地解决。因此，我们分解成一个缓存放置问题和内容交付延迟最小化问题给定的该高速缓存放置策略的原始问题。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）和一个简单的线性搜索算法来解决。我们广泛的模拟结果表明，所提出的方法相比，现有的基线缓存命中率（KIDS），最后期限违规和内容交付延迟的有效性。
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 article 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.