A Resource Allocation Method of Heterogeneous Wireless Cognitive Networks Based on Convex Optimization Theory

Abstract

This paper studies the resource allocation in the heterogeneous wireless cognitive networks (HWCNs), and we used an end to end model to analyze the delay of the service of secondary users (SUs) in the HWCNs, and proposed a joint resource allocation algorithm based on convex optimization theory considering the arrival probability of the primary users(PUs) to minimize the delay of end-to-end communication among the HWCN. We allocate the bandwidth of different radio access technologies (RATs) and the power of different SUs jointly in the HWCNs. Besides, data being transported by each SU are split to send corporately based on allocated bandwidth and power. The numerical results show that the proposed algorithm can improve the delay performance of the system significantly and it outperforms other algorithms in the references.

Resource Allocation Method of Cognitive Satellite Terrestrial Networks Under Non-Ideal Spectrum Sensing

“…Besides, it is assumed that both h S and h I are available based on the ideal channel state information (CSI). Satellite users' communication is interrupted by PUs, and the arrival of PUs is described by PU activity matrix (φ) model, which improves system throughput performance compared with Poisson model [26]. The PU's average channel idle time is equal to the reciprocal of the mean of the activity matrix.…”

“…In ( 6) C ij represents channel capacity of RAT j that SU i used, t ij is the time delay caused by the arrival of PU j during SU i communication [26], [27]:…”