An energy efficient spectrum sensing scheme for the cognitive radio based internet of things

Abstract

Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. Within that context, this thesis makes the following contributions: Firstly, for saving the cooperative bandwidth of the spectrum sensing process, we present an enhanced sum rate in the cluster based cognitive radio relay network (CCRRN) utilizing a reporting framework in the sequential approach. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained compared to a conventional non-sequential approach, therefore making it applicable for the future Internet of things (IoT). In addition, the sum rate of the primary network (PN) and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the "n-out-of-k" rule. The simulation results show that the proposed sequential approach with a relay achieves a better sensing gain and an enhanced sum rate when compared with the conventional non-sequential approach with no relay under any condition. Secondly, state-of-the-art energy detection (ED) based spectrum sensing requires perfect knowledge of noise power and is vulnerable to noise uncertainty. An eigenvalue-based spectrum sensing approach performs well in such an uncertain environment, but does not mitigate the spectrum scarcity problem, which evolves with the future IoT rollout. For this reason, we propose a multi-user multiple-input and multiple-output (MU-MIMO) based cognitive radio scheme for the Internet of things (CR-IoT) with weighted-Eigenvalue detection (WEVD) for the analysis of sensing, system throughput, energy efficiency and expected lifetime. In this scheme, each CR-IoT user is being equipped with multiple-input and multiple-output (MIMO) antennas; we calculate the weighted Eigenvalue detection ratio, which is defined as the ratio between the difference of the maximum eigenvalue and minimum eigenvalue to the sum of the maximum eigenvalue and minimum eigenvalue. This mitigates against the spectrum scarcity problem, enhances system throughput, improves energy efficiency, prolongs expected lifetime and lowers error probability. Simulation results confirm the effectiveness of the proposed scheme; here the WEVD technique demonstrates a better detection gain, an enhanced system throughput, a lower energy consumption, prolonged expected lifetime and a lower error probability in comparison to the conventional scheme with Eigenvalue based detection (EVD) and ED techniques in a noise uncertainty environment. Thirdly, we address the issues of enhancing sensing gain, average throughput, energy consumption and network lifetime in a cognitive radio based Internet of things (CR-IoT) network under the non-sequential approach. As a solution, we propose a Dempster-Shafer theory based throughput analysis of an energy efficient spectrum sensing scheme for a heterogeneous CR-IoT network under the sequential approach, which utilizes both the signal-to-noise ratio (SNR) to evaluate the degree of reliability, and the reporting time slot to merge as a flexible sensing time slot in order to evaluate spectrum sensing more accurately. Before making a global decision based on both the Dempster-Shafer theory and the "n-out-of-k" rule at the fusion center, a flexible sensing time slot is applied to adapt its sensing data. Using the proposed Dempster-Shafer theory, evidence is aggregated during the reporting time slot and then a global decision is made at the fusion center. Simulation results show that the proposed approach improves sensing performance by 13% over previous approaches. In addition, it also improves overall throughput, reduces energy consumption, prolongs expected lifetime and reduces global error probability compared to the previous approaches under any condition. Finally, in a noise uncertain environment, the sensing performance of the conventional ED scheme is significantly degraded because of noise fluctuation, which is caused by the noise temperature, interference, and filtering. To mitigate this problem, we propose an analysis approach of the cooperative spectrum sensing and sum rate calculation for CR-IoT networks in noise uncertain environments using the Kullback–Leibler divergence (KLD) technique, excluding the deep fading CR-IoT users at a coordinator centre. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, an enhanced sum rate, a lower energy consumption, a longer network lifetime, a lower global error probability and a lower reporting overhead when compared to the conventional ED scheme in a noise uncertain environment