Harnessing Interference in Content Delivery by Spatial Signal Alignment
Dongzhu Liu, Kaibin Huang · 2017
As multimedia content is becoming increasingly dominant in mobile data traffic, low-latency content delivery will be a key feature for next-generation radio access networks. For wireless content delivery, many signals over 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. To cope with this issue, we present a novel idea of content adaptive MAC (CAMAC) to ensure that all signals carry identical content are encoded using an uniform MAC scheme and thus interference can be harnessed as signals, thereby improving the reliability of wireless delivery. In quantify the resultant performance gain, we consider a model of content delivery network where the content helpers are distributed as a Poisson point process and each of them randomly transmits a file based on a given popularity distribution. It is found that the (successful) content-delivery probability depends on the distribution of a shot-noise ratio, referring to the ratio of two independent shot-noise processes. Its distribution is an open mathematical problem that we tackle in this work using stable distribution theory and tools from stochastic geometry. The results allow the derivation of content-delivery probability in different closed forms. Then the gain in the probability due to CAMAC is quantified and shown to grow with the level of skewness in the content popularity distribution.