Cooperative Sensing and Allocation Algorithm of Cognitive Radio Spectrum Based on Artificial Intelligence

Abstract In recent years, with the rapid development of artificial intelligence technology, people’s demand for wireless spectrum resources is increasing, which poses a huge challenge to the originally tight and limited wireless spectrum resources. On the other hand, the traditional fixed spectrum cooperative sensing and allocation algorithms result in extremely low spectrum utilization for a considerable part of the licensed spectrum. The purpose of this paper is to study the cooperative sensing and allocation algorithm of cognitive RS (radio spectrum) based on artificial intelligence. This dissertation focuses on cooperative perception and cognitive radio systems, respectively, from the aspects of cooperative perception of user fairness, maximization of system energy efficiency, and user detection when user access is busy. Firstly, a joint optimization model of fairness cooperative spectrum sensing and allocation is established to compensate the sensing overhead of cooperative users to ensure its fairness; then, define and analyze the energy efficiency of the cognitive system, and establish a joint optimization model of cooperative spectrum sensing and allocation based on artificial intelligence to maximize energy efficiency, and optimize wireless sensing and allocation parameters while ensuring maximum system energy efficiency. The experimental results show that when = 0.7, the algorithm proposed in this study has reached 100% of the RS perception performance, while the traditional algorithm only has 93%. The algorithm proposed in this paper has greater advantages in perception and distribution performance.

Download Full-text

Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks: A Dynamic Cooperative Method

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2015.p0197 ◽

2015 ◽

Vol 19 (2) ◽

pp. 197-204 ◽

Cited By ~ 2

Author(s):

Yi Li ◽

◽

Jun Peng ◽

Fu Jiang ◽

Kaiyang Liu ◽

...

Keyword(s):

Energy Efficiency ◽

Cognitive Radio ◽

Sensor Networks ◽

Spectrum Sensing ◽

Data Transmission ◽

Cooperative Spectrum Sensing ◽

Optimization Method ◽

Joint Optimization ◽

Transmission Scheme ◽

Cognitive Radio Sensor Networks

To address the inherent energy constraint in cognitive radio sensor networks, a novel joint optimization method of spectrum sensing and data transmission for energy efficiency is investigated in this paper. To begin with, a cooperative spectrum sensing scheme based on dynamic censoring is employed to shorten sensing time and save unnecessary spectrum sensing energy. Then to jointly optimize the energy efficiency, the distortion constrained probabilistic transmission scheme is utilized. Afterwards the sensing threshold solving issue can be formulated as a nonlinear minmax optimization problem with the detection probability and false alarm probability constraints. Solving by the Matlab software with the free OPTI toolbox, simulation results demonstrate that significant energy can be saved via the the proposed joint optimization method in various mobile cloud scenarios.

Download Full-text

Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

Entropy ◽

10.3390/e24010129 ◽

2022 ◽

Vol 24 (1) ◽

pp. 129

Author(s):

Mingdong Xu ◽

Zhendong Yin ◽

Yanlong Zhao ◽

Zhilu Wu

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Cognitive Radio Network ◽

Cooperative Spectrum Sensing ◽

Signal To Noise Ratio ◽

Primary User ◽

Radio Spectrum ◽

Local Information ◽

Channel State ◽

Single Node

cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

Download Full-text

Energy Efficiency Analysis of Soft and Hard Cooperative Spectrum Sensing Schemes in Cognitive Radio Networks

2014 IEEE 79th Vehicular Technology Conference (VTC Spring) ◽

10.1109/vtcspring.2014.7022904 ◽

2014 ◽

Cited By ~ 9

Author(s):

Saud Althunibat ◽

Fabrizio Granelli

Keyword(s):

Energy Efficiency ◽

Cognitive Radio ◽

Spectrum Sensing ◽

Cognitive Radio Networks ◽

Cooperative Spectrum Sensing ◽

Efficiency Analysis ◽

Radio Networks ◽

Energy Efficiency Analysis

Download Full-text

Development and Demonstration of Graphical User Interface Spectrum Sensing Algorithm using some Wireless Systems in South Africa

Journal of Applied Science & Process Engineering ◽

10.33736/jaspe.164.2015 ◽

2016 ◽

Vol 2 (2) ◽

Author(s):

Jide Julius Popoola ◽

Rex van Olst

Keyword(s):

Cognitive Radio ◽

User Interface ◽

Spectrum Sensing ◽

Graphical User Interface ◽

Dynamic Spectrum Access ◽

Cooperative Spectrum Sensing ◽

Radio Spectrum ◽

Spectrum Utilization ◽

Application Program ◽

Radio Technology

The wireless communication industry using radio spectrum is recently going through major innovations and advancements. With this transformation, the demand for and usage of radio spectrum has increased exponentially making radio spectrum indeed a scarce natural resource. In order to solve this problem, the possibility of opening up the unused portions of licensed spectrum by sharing using cognitive radio technology has been in the spotlight for maximizing radio spectrum utilization as well to as ensure sufficient radio spectrum availability for future wireless services and applications. With this objective in mind, this paper looks at the principles and technologies of cooperative spectrum sensing in cognitive radio environment in improving radio spectrum utilization. The paper provides a comprehensive review on spectrum sensing as a key functional requirement for cognitive radio technology by focusing on its application on dynamic spectrum access that enables unused portions of licensed spectrum to be used in an opportunistic manner as long as the operation of the unlicensed user will not affect that of the licensed user. In satisfying this dynamic spectrum access requirement, a friendly interactive graphical user interface (GUI) spectrum sensing application program was developed. The detail activities involve in the development of the application program, also known as spectrum sensing and detection algorithm (SSADA), was fully documented and presented in the paper. The developed graphical user interface application program after successfully developed was evaluated. The performance evaluations of developed graphical user interface sensing algorithm show that the algorithm performs favourably well. The program overall evaluation results provide bedrock information on how to improve cooperative spectrum sensing gain without incurring a cooperative overhead.

Download Full-text