To maintain reliability of content-oriented wireless caching networks (CWCNs), repair mechanism is of necessity to be considered due to the natural that storage entities are individually unreliable and thus subject to failure on account of hardware error, network congestion or software updating. Meanwhile, recommendation is tunable for edge caching performance improvement. In this paper, we study the revenue maximization problem for CWCNs with both repair and recommendation considerations. The formulated problem is an integer non-convex and non-linear problem, and thus is difficult to be solved. The difficulties are intrinsically derived from the implicit weighted sum costs (WSCs) as regards storage and repair of each content and the coupling among the Boolean variables. For the sake of analytical tractability, a two-step methodology is developed. Specifically, we first explore the optimal storage and repair amount among the content providers to minimize the WSCs in terms of successfully fixing any occurred data corruption for the stored contents. Thereof, an explicit instance is provided to show how the contents can be coded, stored and then repaired in our network given that an error occurs. Based on the obtained storage and repair amount vectors, we solve the resultant joint caching and recommendation decision making problem (DMP). To be more specific, we decouple the DMP into a pair of subproblems, namely a cache placement and a recommendation optimization subproblems. For each subproblem, a globally optimal and a time-efficient suboptimal solutions are developed, respectively. Later, a versatile iterative paradigm is devised to do the decision making jointly. The convergence performance and the complexity analysis of the proposed algorithms are rigorously analyzed. Numerical results confirm the convergence performance of our iterative algorithms and illustrate their revenue improvements compared to various baseline schemes.

中文翻译：

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具有修复和推荐考虑的面向内容的无线缓存网络 (CWCN) 的收入最大化

为了保持面向内容的无线缓存网络（CWCN）的可靠性，由于存储实体个体不可靠，因此容易因硬件错误、网络拥塞或软件更新而失败，因此有必要考虑修复机制。同时，推荐是可调整的以提高边缘缓存性能。在本文中，我们研究了 CWCN 的收益最大化问题，同时考虑了修复和推荐。公式化的问题是整数非凸非线性问题，因此难以求解。这些困难本质上来自于每个内容的存储和修复以及布尔变量之间的耦合的隐式加权总成本（WSC）。为了便于分析，开发了一种两步法。具体来说，我们首先探索内容提供者之间的最佳存储和修复量，以在成功修复存储内容的任何发生的数据损坏方面最小化 WSC。因此，提供了一个明确的实例来展示在发生错误的情况下如何在我们的网络中对内容进行编码、存储和修复。基于获得的存储和修复量向量，我们解决了由此产生的联合缓存和推荐决策问题（DMP）。更具体地说，我们将 DMP 解耦为一对子问题，即缓存放置和推荐优化子问题。对于每个子问题，分别开发全局最优和时间高效的次优解决方案。后来，设计了一个通用的迭代范式来共同进行决策。严格分析了所提出算法的收敛性能和复杂度分析。数值结果证实了我们迭代算法的收敛性能，并说明了与各种基线方案相比它们的收入改进。