Cognitive Radio Resource Allocation Based on the Improved Quantum Genetic Algorithm

Han, Bin; Jiang, Hong; Luo, Ying; Zhou, Jinzhi

doi:10.2316/j.2019.206-0233

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Thus, under the condition of multichannel coexistence, it is crucial for SUs to make optimal decisions about which channel to access at different times. Recently, some scholars have researched them by machine learning [7]- [10]. Among them, MAB is a classical theory for selection.…”

Section: Introductionmentioning

confidence: 99%

A Fast Convergent Channel Selection Strategy in CRSN

Chen¹,

Jiang²,

Wu³

et al. 2020

International Journal of Robotics and Automation

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Accurate and fast convergence to the optimal channel is a challenge in a cognitive radio sensor network (CRSN) when multiple cognitive wireless channels coexist. Some traditional wireless channel selection methods can be used to study the optimal channel selection. However, their convergence speed cannot meet the requirements because of vast computation and time accumulation. In this paper, a rapid channel selection strategy based on machine learning called MAB-CQ (multi-armed bandit-channel quality) is proposed. This strategy maps the channel selection problem to the improved multiarmed bandit (MAB) model. In the model, the second users (SUs) and the channels in the CRSN correspond to the players and the arms of MAB, respectively. The optimal channel is determined based on the UCB (upper confidence bound) of MAB-CQ for each player. In addition, the UCB equation is creatively defined to balance the exploration and exploitation problem. At the same time, to reduce the computation complexity, coefficients about the factors are used to narrow down the exploratory scope of our strategy. As a result, an accuracy optimal channel and a fast convergence speed are achieved by iterative execution of MAB-CQ. Extensive experimental results demonstrate that the MAB-CQ can converge to nearly 100% within the 10 5 time slots. By comparison, MAB-CQ has obvious advantages in cumulative rewards, computational complexity and convergence speed.

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Section: Introductionmentioning

confidence: 99%

A Fast Convergent Channel Selection Strategy in CRSN

Chen¹,

Jiang²,

Wu³

et al. 2020

International Journal of Robotics and Automation

Self Cite

View full text Add to dashboard Cite

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A Chaotic Hybrid Immune Genetic Algorithm for Spectrum Allocation Optimization in ICRSN

Zhou

2020

Journal of Sensors

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The growing usage of the industrial cognitive radio sensor network (ICRSN) brings profound changes to the Internet of Things. The ICRSN is an emerging technique to transfer industrial data, which has strict and accurate communication requirements in a large number of areas such as environmental surveillance, building monitoring, control, and many other areas. The problem of maximizing the sum bandwidth by using a spectrum allocation algorithm has been extensively studied in this paper. Inspired by chaos theory and quantum computing, this work presents a new chaotic hybrid immune genetic algorithm (CHIGA). We then introduce a spectrum allocation model that considers both network reward, throughput, and convergence time. The improvement of CHIGA performance through experimental simulations is evaluated in terms of the sum network reward compared to methods based on simulated annealing (SA), ant colony optimization (ACO), and particle swarm optimization (PSO). Simulation results show that the CHIGA has a higher network reward and throughput existing optimized algorithms while maintaining total system throughput.

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Cognitive Radio Resource Allocation Based on the Improved Quantum Genetic Algorithm

Cited by 2 publications

References 14 publications

A Fast Convergent Channel Selection Strategy in CRSN

A Fast Convergent Channel Selection Strategy in CRSN

A Chaotic Hybrid Immune Genetic Algorithm for Spectrum Allocation Optimization in ICRSN

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