Large Overlaid Cognitive Radio Networks: From Throughput Scaling to   Asymptotic Multiplexing Gain
  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. 
Armin Banaei, Costas N. Georghiades, and Shuguang Cui
cs.NI