Multiband Spectrum Sensing and Power Allocation for aCognitive Radio-Enabled Smart Grid.

Yanwei Huang,Weibin Jiang,Riqing Chen,Ruiquan Lin,Hongjun Wang,Jun Wang

doi:10.3390/s21248384

Abstract

As part of an Internet of Things (IoT) framework, the Smart Grid (SG) relies on advanced communication technologies for efficient energy management and utilization. Cognitive Radio (CR), which allows Secondary Users (SUs) to opportunistically access and use the spectrum bands owned by Primary Users (PUs), is regarded as the key technology of the next-generation wireless communication. With the assistance of CR technology, the quality of communication in the SG could be improved. In this paper, based on a hybrid CR-enabled SG communication network, a new system architecture for multiband-CR-enabled SG communication is proposed. Then, some optimization mathematical models are also proposed to jointly find the optimal sensing time and the optimal power allocation strategy. By using convex optimization techniques, several optimal methods are proposed to maximize the data rate of multiband-CR-enabled SG while considering the minimum detection probabilities to the active PUs. Finally, simulations are presented to show the validity of the proposed methods.

Highlights

In Cognitive Radio (CR), the main concern is that the presences of Primary Users (PUs) should be detected properly and the transmission of PUs should not be interfered by Secondary Users (SUs)
We assumed the CR-enabled Smart Grid (SG) communication network consists of one original channel named Bb,1 with bandwidth 100 kHz and two cognitive channels named
The total bandwidth is usually divided into several narrowband sub-channels, such as Global System for Mobile communication (GSM) systems with 25 MHz bandwidth and 125 sub-channels, Narrowband Internet of Things (NB-IoT) systems with 180 kHz bandwidth and 12 sub-channels, and IEEE 802.11g systems with 16.25 MHz and 52 sub-channels, so the bandwidth of a single sub-channel usually ranges from tens of kHz to hundreds of kHz, and it is reasonable to assume the bandwidth of the original channel bought from the telecommunication operator is 100 kHz here

Summary

Introduction

The. Energy detection is the most often considered spectrum sensing method in the CR literature because of its simplicity and adequate performance. The received signal y(n) of energy detection is [16]: w(n) H0 y(n) = (1). The test statistic of energy detection is: N ∑ y2 ( n ) η= n =1 (2). The false alarm probability means the loss of access opportunities and will cause no harm to PUs so that it can be considered second. Based on this premise, in this paper, we chose the detection probability as the target detection probability

Objectives

Results

Conclusion