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


We study the asymptotic performance of two multihop 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 λ and the secondary network consists of Poisson distributed cognitive radio users with density λ = (λ) (β > 0, β 6= 1) that utilize the spectrum opportunistically. Both networks are decentralized and deploy ALOHA protocols where the secondary nodes are equipped with range-limited perfect spectrum sensors to monitor and protect primary transmissions. We study the problem in two distinct regimes, namely β > 1 and 0 < β < 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 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 β > 1. On the contrary, spectrum sensing turns out to be unnecessary when β < 1.

DOI: 10.1109/TWC.2014.042814.130963

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@article{Banaei2014LargeOC, title={Large Overlaid Cognitive Radio Networks: From Throughput Scaling to Asymptotic Multiplexing Gain}, author={Armin Banaei and Costas N. Georghiades and Shuguang Cui}, journal={IEEE Trans. Wireless Communications}, year={2014}, volume={13}, pages={3042-3055} }