This paper considers a device-based Fog Radio Access Network (F-RAN), where users’ smart devices (denoted by F-UEs) can be exploited to cache popular files so as to communicate them when requested by other F-UEs among them. This architecture restricts the involvement of the central baseband processing unit (BBU) to serving those F-UEs not immediately served by their peers, thus offloading the BBU's fronthaul spectrum and increasing the overall capacity of the network. This paper aims to further maximize fronthaul offloading in this architecture using network coding (NC). The latter exploits both the cached and previously received files by different F-UEs as side information to serve more requesting F-UEs in each transmission from their peers or the BBU so as to maximize fronthaul offloading. Being half-duplex devices (they can only either send or receive in any given time), the problem of BBU fronthaul offloading in this setting is formulated over two transmission phases on an NC graph. After showing that this problem is NP-hard, the paper proposes two novel heuristic algorithms to solve it in real-time. In addition, a lower-bound on the offloading gain of these algorithms is derived for a special file placement case, their asymptotic optimality is proven, and their complexities are analyzed. Simulation results both show that these proposed algorithms perform closely to the optimal solution and quantify the significant offloading gains achieved by them.

中文翻译：

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使用设备雾、缓存和网络编码的蜂窝前传卸载

本文考虑了一种基于设备的雾无线电接入网络（F-RAN），其中用户的智能设备（由 F-UE 表示）可以被利用来缓存流行文件，以便在其中的其他 F-UE 请求时进行通信. 这种架构限制了中央基带处理单元 (BBU) 的参与，为那些没有立即由其对等方提供服务的 F-UE 提供服务，从而卸载 BBU 的前传频谱并增加网络的整体容量。本文旨在使用网络编码 (NC) 在此架构中进一步最大化前传卸载。后者利用不同 F-UE 缓存的和先前接收的文件作为辅助信息，在来自其对等方或 BBU 的每次传输中为更多请求的 F-UE 提供服务，从而最大化前传卸载。作为半双工设备（它们只能在任何给定时间内发送或接收），此设置中的 BBU 前传卸载问题在 NC 图上的两个传输阶段中被公式化。在证明这个问题是 NP-hard 之后，本文提出了两种新颖的启发式算法来实时解决它。此外，针对特殊的文件放置情况导出了这些算法卸载增益的下限，证明了它们的渐近最优性，并分析了它们的复杂性。仿真结果都表明，这些提出的算法与最优解非常接近，并且量化了它们所实现的显着卸载增益。在证明这个问题是 NP-hard 之后，本文提出了两种新颖的启发式算法来实时解决它。此外，针对特殊的文件放置情况导出了这些算法卸载增益的下限，证明了它们的渐近最优性，并分析了它们的复杂性。仿真结果表明，这些提出的算法与最优解非常接近，并量化了它们所实现的显着卸载增益。在证明这个问题是 NP-hard 之后，本文提出了两种新颖的启发式算法来实时解决它。此外，针对特殊的文件放置情况导出了这些算法卸载增益的下限，证明了它们的渐近最优性，并分析了它们的复杂性。仿真结果都表明，这些提出的算法与最优解非常接近，并且量化了它们所实现的显着卸载增益。