Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing

Abstract

Due to irregular deployment of small base stations (SBSs), the interference in cognitive heterogeneous networks (CHNs) becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the interference context. In this case, how to analyze system capacity to obtain a closed-form expression becomes a crucial problem. In this paper we employ stochastic methods to formulate the capacity of CHNs and achieve a closed-form expression. By using discrete-time Markov chains (DTMCs), the spectrum mobility with respect to the arrival and departure of macro base station (MBS) users is modeled. Then an integral method is proposed to derive the interference based on stochastic geometry (SG). Also, the effect of sensing accuracy on network capacity is discussed by concerning false-alarm and miss-detection events. Simulation results are illustrated to show that the proposed capacity analysis method for CHNs can approximate the conventional sum methods without rigorous requirement for channel station information (CSI). Therefore, it turns out to be a feasible and efficient way to capture the network capacity in CHNs.

Keywords

Cognitive heterogeneous networks / Markov chain / Stochastic geometry / Homogeneous Poisson point process (HPPP)

References

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Funding