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

TL;DR

This paper analyzes the asymptotic performance of overlaid cognitive radio networks, introducing the asymptotic multiplexing gain metric to evaluate interference effects and showing how spectrum sensing impacts primary network performance under different density regimes.

Contribution

It introduces the asymptotic multiplexing gain as a new performance metric and characterizes the impact of spectrum sensing on primary network performance in overlaid cognitive radio networks.

Findings

Spectrum sensing improves primary performance when secondary density grows faster (eta>1).

Secondary networks can achieve stand-alone throughput scaling without sensing.

Appropriate ALOHA parameters can enhance primary performance when secondary density grows slower (eta<1).

Abstract

We study the asymptotic performance of two multi-hop overlaid 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 \lambda^{(p)} and the secondary network consists of Poisson distributed cognitive radio users with density \lambda^{(s)} = (\lambda^{(p)})^{\beta} (\beta>0, \beta \neq 1) that utilize the spectrum opportunistically. Both networks are decentralized and employ ALOHA medium access protocols where the secondary nodes are additionally equipped with range-limited 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., the sensing range set to zero); this 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 asymptotic multiplexing gain, which captures the effect of inter-network interferences with different spectrum sensing setups. With this metric, we clearly demonstrate that spectrum sensing can substantially improve primary network performance when \beta>1. On the contrary, spectrum sensing turns out to be unnecessary when \beta<1 and setting the secondary network's ALOHA parameter appropriately can substantially improve primary network performance.