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Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio

Research output: Contribution to journalArticlepeer-review

Dongzhu Liu, Kaibin Huang

Original languageUndefined/Unknown
Pages (from-to)2305-2318
Number of pages14
JournalIEEE Trans. Wireless Communications
Issue number4
Early online date12 Jan 2018
E-pub ahead of print12 Jan 2018
PublishedApr 2018

King's Authors


Multimedia content, especially videos, is expected to dominate data traffic in next-generation mobile networks. Caching popular content at the network edge, namely content helpers (base stations and access points), has emerged as a solution for low-latency content delivery. Compared with traditional wireless communication, content delivery has a key characteristic that many signals coexisting in the air carry identical popular content. However, they can interfere with each other at a receiver if their modulation-and-coding (MAC) schemes are adapted to individual channels following the classic approach. To address this issue, we present a novel idea of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures that all signals carrying identical content are encoded using an identical MAC scheme to achieve spatial MAC alignment. Consequently, interference can be harnessed as signals to improve the reliability of wireless delivery. In the remaining part of the paper, we focus on quantifying the gain that CAMAC can bring to a content-delivery network by using a stochastic-geometry model. Specifically, content helpers are distributed as a Poisson point process and each of them transmits a file from a content database based on a given popularity distribution. Given a fixed threshold on the signal-to-interference ratio for successful transmission, it is discovered that the successful content-delivery probability is closely related to the distribution of the ratio of two independent shot noise processes, named a shot-noise ratio. The distribution itself is an open mathematical problem that we tackle in this work. Using stable-distribution theory and tools from stochastic geometry, the distribution function is derived in closed form. Extending the result in the context of content-delivery networks with CAMAC yields the content-delivery probability in different closed forms. In addition, the gain in the probability due to CAMAC is shown to grow with the level

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