Abstract
As the limited communication spectrum can not meet the demand of the exponential growth of intelligent connected devices in the internet of things(IoT) and typical smart city applications, in this paper, we propose a tractable spectrum sensing method based on Rao detection over non-Gaussian noise, such as generalized Gaussian noise(GGN), Gaussian mixture noise(GMN) and symmetric alpha-stable distribution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{S}\alpha \text{S}$ </tex-math></inline-formula> ) noise, multi-path fading channels environment to alleviate the issue of spectrum scarcity. In this method, there are unknown parameters in the multi-path fading channels. When the probability density function (P.D.F.) of non-Gaussian noise has a closed-form expression, the spectrum sensing method based on Rao detection is used. Otherwise the P.D.F. for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{S}\alpha \text{S}$ </tex-math></inline-formula> noise is estimated firstly by using non-parametric kernel estimation method, which addresses the issue that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{S}\alpha \text{S}$ </tex-math></inline-formula> noise has no closed-form P.D.F. expression, and then the performance of spectrum sensing is derived based on the theory of Rao detection in multi-path fading channels over typical smart city applications. Simulation results show that the accuracy of estimated P.D.F. for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{S}\alpha \text{S}$ </tex-math></inline-formula> noise and the performance of spectrum sensing under different <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> values over indoor, outdoor, and vehicle fading channels environment.
Highlights
Wireless communications play an increasingly prominent role in the internet of things(IoT) and typical smart city applications, since they are able to provide ubiquitous and transparent service [1]–[4]
We develop a novel spectrum sensing method based on the theory of Rao detection over non-non-Gaussian noise multi-path fading channels environment
Smart connected devices in the IoT and typical smart city applications need a lot of spectrum to communicate
Summary
Wireless communications play an increasingly prominent role in the internet of things(IoT) and typical smart city applications, since they are able to provide ubiquitous and transparent service [1]–[4]. J. Li et al.: Spectrum Sensing With Non-Gaussian Noise Over Multi-Path Fading Channels Towards Smart Cities With IoT resources. Non-Gaussian noise and multi-path fading channels are two important factors that affect the performance of spectrum sensing. The problem is more challenging as we need to sense the PUs signals affected by non-Gaussian noise and multi-path fading channels [16]. We develop a novel spectrum sensing method based on the theory of Rao detection over non-non-Gaussian noise multi-path fading channels environment. SYSTEM MODEL Fig. 1 is the system model of CRN and shows a spectrum sensing scenario with the coexistence of PUs and SUs, which share spectrum resources over SαS noise multi-path fading channels without harmful interference. The red dotted line indicates that harmful wireless interference may be generated between PUs and SUs
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