Design of Probabilistic Random Access in Cognitive Radio Networks

Abstract

In this paper, we focus on the design of probabilistic random access (PRA) for a cognitive radio network (CRN). The cognitive base station (CBS) allows the secondary users (SUs) to reuse the sub-channels of the primary users (PUs) provided that the interference of the SUs to the PUs is below a predetermined threshold. PUs transmit over a fixed set of channels with fixed transmission powers that are scheduled by the CBS. With this prior information, CBS optimizes the probabilistic random transmissions of the SUs. In each time slot, SUs transmit over a random number of channels d, chosen uniformly at random, according to a certain degree distribution function, optimized by the CBS. Once the signals of the SUs and PUs are received, CBS then implements successive interference cancellation (SIC) to recover both the SUs’ and PUs’ signals. In the signal recovery, we assume that the PUs’ signals can be recovered if the interference power (IP) of the SUs to the PUs is below a predetermined threshold. On the other hand, we assume the SUs’ signals can be recovered if its received SINR is above a predetermined threshold. We formulate a new optimization problem to find the optimal degree distribution function that maximizes the probability of successfully recovering the signals of an SU in the SIC process under the SINR constraints of the SUs while satisfying the IP constraints of the PUs. Simulation results show that our proposed design can achieve higher success probabilities and a lower number of transmissions in comparison with conventional schemes, thus, significantly improving signal recovery performance and reducing energy consumption.