Study of Large Overlaid Cognitive Radio Networks: From Throughput Scaling to Asymptotic Multiplexing Gain

Details Study of Large Overlaid Cognitive Radio Networks: From Throughput Scaling to Asymptotic Multiplexing Gain Study of Large Overlaid Cognitive Radio Networks: From Throughput Scaling to Asymptotic Multiplexing Gain

2011-03-04

arxiv.org/abs/1103.0843v1

Computer Science

Networking and Internet Architecture

Scientific paper

We study the asymptotic performance of two overlaid wireless ad-hoc networks that utilize the same temporal, spectral, and spatial resources based on random access schemes. The primary network consists of Poisson distributed legacy users with density $n$ and the secondary network consists of Poisson distributed cognitive radio users with density $m = n^{\beta}$ ($\beta > 0$, $ \beta \neq 1$) that utilize the spectrum opportunistically. Both networks are \emph{decentralized} and deploy ALOHA protocols where the secondary nodes are equipped with range-limited \emph{perfect} spectrum sensors to monitor and protect primary transmissions. We study the problem in two distinct regimes, namely $\beta > 1$ and $0 < \beta < 1$. We show that in both cases, the two networks can achieve their corresponding stand-alone throughput scaling even without secondary spectrum sensing (i.e., sensing range set to zero), which implies the need for a more comprehensive performance metric than just throughput scaling to evaluate the influence of the overlaid interactions. We thus introduce a new criterion, termed the \emph{asymptotic multiplexing gain}, which captures the effect of spectrum sensing and inter-network interferences. Furthermore, based on this metric we demonstrate that spectrum sensing can substantially improve the network performance when $\beta > 1$. On the contrary, spectrum sensing turns out to be unnecessary when $\beta < 1$.

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