Multicast throughput for large scale cognitive networks
In this paper, we focus on the achievable throughput of cognitive networks consisting of the primary ad hoc network (PaN) and the secondary ad hoc network (SaN). We construct PaN and SaN by placing nodes according to Poisson point processes of density n and m respectively over a unit square region. We directly study the multicast throughput of cognitive network to unify that of unicast and broadcast sessions. In order to ensure the priority of primary users in meanings of throughput, we design a metric called throughput decrement ratio (TDR) to measure the ratio of the throughput of PaN in presence of SaN to that of PaN in absence of SaN. Endowing PaN with the right to determine the threshold of the TDR, we propose multicast schemes based on TDMA and multihop routing for the two networks respectively and derive their achievable multicast throughput depending on the given threshold. Specially, we show when PaN has sparser density than SaN, to be specific, \(n=o\left({\frac{m} {(\log m)^2}}\right),\) and if PaN only cares about the order of its throughput, SaN can simultaneously achieve the same order of the aggregated multicast throughput as it were a stand-alone network in absence of PaN.
multicast throughput cognitive networks gaussian channel model asymptotic scalability