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Optimizing Content Caching to Maximize the Density of Successful Receptions in Device-to-Device Networking

机译:优化内容缓存以最大化成功密度   设备到设备网络中的接收

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摘要

Device-to-device (D2D) communication is a promising approach to optimize theutilization of air interface resources in 5G networks, since it allowsdecentralized opportunistic short-range communication. For D2D to be useful,mobile nodes must possess content that other mobiles want. Thus, intelligentcaching techniques are essential for D2D. In this paper we use results fromstochastic geometry to derive the probability of successful content delivery inthe presence of interference and noise. We employ a general transmissionstrategy where multiple files are cached at the users and different files canbe transmitted simultaneously throughout the network. We then formulate anoptimization problem, and find the caching distribution that maximizes thedensity of successful receptions (DSR) under a simple transmission strategywhere a single file is transmitted at a time throughout the network. We modelfile requests by a Zipf distribution with exponent $\gamma_r$, which results inan optimal caching distribution that is also a Zipf distribution with exponent$\gamma_c$, which is related to $\gamma_r$ through a simple expressioninvolving the path loss exponent. We solve the optimal content placementproblem for more general demand profiles under Rayleigh, Ricean and Nakagamismall-scale fading distributions. Our results suggest that it is required toflatten the request distribution to optimize the caching performance. We alsodevelop strategies to optimize content caching for the more general case withmultiple files, and bound the DSR for that scenario.
机译:设备到设备(D2D)通信是一种有前途的方法,可以优化5G网络中空中接口资源的利用率,因为它允许分散的机会性短距离通信。为了使D2D有用,移动节点必须拥有其他移动设备想要的内容。因此,智能缓存技术对于D2D至关重要。在本文中,我们使用来自随机几何的结果来推导在存在干扰和噪声的情况下成功进行内容传递的概率。我们采用一种通用的传输策略,其中多个文件被缓存在用户处,并且不同的文件可以同时在整个网络中传输。然后,我们提出一个优化问题,并找到一种缓存分配,该缓存分配可以在简单的传输策略下最大化成功接收的密度(DSR),在该传输策略中,一次在整个网络上传输单个文件。我们通过具有指数$ \ gamma_r $的Zipf分布对文件请求进行建模,这会导致最佳的缓存分布,这也是具有指数$ \ gamma_c $的Zipf分布,这通过涉及路径损耗指数的简单表达式与$ \ gamma_r $有关。我们针对瑞利(Rayleigh),莱斯(Ricean)和中上小规模衰落分布下更一般的需求情况,解决了最佳的内容放置问题。我们的结果表明,需要扩展请求分布以优化缓存性能。我们还开发了针对多个文件的更一般情况优化内容缓存的策略,并为该方案绑定了DSR。

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