A throughput analysis of an energy-efficient spectrum sensing scheme for the cognitive radio-based Internet of things

Md Sipon Miah,Enda Barrett,Michael Schukat

doi:10.1186/s13638-021-02075-2

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. In this paper, 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 using 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 using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by 13% over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

Highlights

1.1 Motivation The Internet of things (IoT) is a new machine-to-machine (M2M) communication paradigm that includes a variety of domains, protocols, and applications, which allow devices to communicate with each other using different communication technologies without human intervention [1]
1.2 Contributions The following major contributions are presented in this paper: We propose a novel algorithm for heterogeneous cognitive radio-based Internet of things (CR-IoT) networks under the sequential approach, in which each CR-IoT user uses a flexible sensing time slot by utilizing the reporting framework efficiently to sense the primary signal more accurately than the conventional scheme using a non-sequential approach
We experimentally analyze the sensing gain at the fusion center with a flexible sensing time slot using both the soft decision fusion rule and the hard decision fusion rule; we demonstrate that the proposed Dempster–Shafer theory enhances the sensing gain for heterogeneous

Summary

Introduction

1.1 Motivation The Internet of things (IoT) is a new machine-to-machine (M2M) communication paradigm that includes a variety of domains, protocols, and applications, which allow devices to communicate with each other using different communication technologies without human intervention [1]. The main benefit of matched filter detection is that effective spectrum sensing requires a short period of time compared to other methods. It needs full knowledge of the primary user signal features, including operating frequency, bandwidth, modulation type as well as order, packet layout, and pulse shaping. The sensing gain of the hard decision fusion rule is lower than that of the soft decision fusion rule, where each CR-IoT user sends the entire sensing result to the fusion center, making a decision using maximal ratio combining, square law combining, selection combining, and Dempster–Shafer theory While it delivers better sensing gain than the hard fusion rule, the control channel needs wide bandwidth. The average throughput, energy consumption, network lifetime, and global error probability were not analyzed

Contributions The following major contributions are presented in this paper

Organization The rest of this paper is structured as follows

The hard decision fusion rule

Findings

Conclusion and future work