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arXiv 1606.08253cs.ITmath.IT
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无线编码缓存:拓扑学视角

Wireless Coded Caching: A Topological Perspective

Jingjing Zhang, Petros Elia

摘要:

我们探讨的性能编码缓存在SISO BC设置,其中一些用户有更高的链接容量比其他。专注于一个二进制和固定的拓扑模型,其中强链接有一个固定的归一化容量1,并在弱链接有减少归一化容量$τ<1$,我们确定-作为缓存大小和$τ$的函数-最佳吞吐量性能,在一个因素最多为8。实现这种性能的传输方案采用简单形式的干扰增强,并利用弱链路衰减干扰的特性,从而即使在涉及弱用户时也允许多播速率保持较高。这种方法改善了多播中不均匀拓扑的负面影响,现在允许所有用户实现与$τ=1$相关联的最佳性能,即使$τ$大约与$τ\geq 1-(1-w)^g$一样低,其中$g$是编码缓存增益,并且其中$w$是弱用户的分数。这导致了有趣的结论,对于编码多播,弱用户不需要降低所有用户的性能,但相反,在一定程度上,强用户可以提升弱用户的性能,而不会对自己的性能造成任何惩罚。此外,对于较小范围的$τ$,我们还看到,实现接近最佳性能的优点是,与$τ= 1$的情况相比,强用户不会遭受任何额外的延迟。

英文摘要:

We explore the performance of coded caching in a SISO BC setting where some users have higher link capacities than others. Focusing on a binary and fixed topological model where strong links have a fixed normalized capacity 1, and where weak links have reduced normalized capacity $τ<1$, we identify --- as a function of the cache size and $τ$ --- the optimal throughput performance, within a factor of at most 8. The transmission scheme that achieves this performance, employs a simple form of interference enhancement, and exploits the property that weak links attenuate interference, thus allowing for multicasting rates to remain high even when involving weak users. This approach ameliorates the negative effects of uneven topology in multicasting, now allowing all users to achieve the optimal performance associated to $τ=1$, even if $τ$ is approximately as low as $τ\geq 1-(1-w)^g$ where $g$ is the coded-caching gain, and where $w$ is the fraction of users that are weak. This leads to the interesting conclusion that for coded multicasting, the weak users need not bring down the performance of all users, but on the contrary to a certain extent, the strong users can lift the performance of the weak users without any penalties on their own performance. Furthermore for smaller ranges of $τ$, we also see that achieving the near-optimal performance comes with the advantage that the strong users do not suffer any additional delays compared to the case where $τ= 1$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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