Capacity Analysis and Call Admission Control in Distributed Cognitive Radio Networks

In this paper, homogeneous voice traffic in a single-channel cognitive radio network (CRN) is considered. We analyze the constant-rate voice capacity of a fully-connected network with slot-ALOHA and round-robin channel access, and propose two call admission control (CAC) algorithms for a non-fully-connected network with slot-ALOHA channel access. Different from the existing work in literature, transmission of multiple packets in a single time-slot is considered. Two discrete-time Markov chain based approaches are used for the capacity analysis of the two channel access schemes, respectively. It is shown that the number of voice packets that can be transmitted in a time-slot has a significant impact on the system capacity. The capacity analysis results of the slot-ALOHA scheme is used to develop a CAC procedure when all the voice flows have an identical statistical delay requirement. Further, two CAC algorithms (A1 and A2) are developed for a network with voice traffic flows having different delay requirements in which one (A1) is based on the theory of effective capacity and is considered as a benchmark to compare with the other. Simulation results demonstrate that algorithm A2 performs better than algorithm A1, and that a relaxed delay requirement leads to an increase in the network capacity.