Application of Artificial Intelligence in Cognitive Radio for Planning Distribution of Frequency Channels

Politanskyi, Ruslan; Klymash, Mykhailo

doi:10.1109/aiact.2019.8847908

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…In [43],the authors investigate the search of free and busy channels based on channel properties for clustering in the environment of cognitive radio networks. A free and busy channel pool is examined for a random distribution of free and busy channels for every user.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…From the study of the above work/papers [33][34][35][36][37][38][39][40][41][42][43][44], it is analysed that none of them considered the interference ratio as the main parameter for designing the reward function in the reinforcement learning technique. This is the reason that motivates us to design and develop the Reinforcement learning-based scheme by using the interference ratio as the main parameter.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks

M. Narender Pavan

2024

jes

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The Spectrum is a scarce resource in wireless networks, hence these resources need to be perfectly channelized for their better utilization. By the study of the previous decades, it is found that the spectrum is colossally underutilized and the main reason for the underutilization found out to be the policies that are fixed and not dynamic. The dynamic spectrum allocation of frequency bands may overcome this problem. Cognitive radio provides an important concept that can used to solve the problem of underutilization of spectrum. Reinforcement learning is a key technique that is widely used to learn the spectrum allocation behaviour and maximize the system’s efficiency. Therefore, in this work, we have designed and developed a Reinforcement learning-based model to allocate the channels among secondary users. Also, a Deep Reinforcement learning-based channel allocation algorithm (DRLCA) has been proposed. The proposed DRLCA is compared with existing JPCRL [1]. In our algorithm, the Python libraries were used for simulation. From the simulation results and analysis, it is found that the DRLCA outperforms the JPCRL in terms of channel utilization by 5%.

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Section: Related Work and Motivationmentioning

confidence: 99%

Section: Related Work and Motivationmentioning

confidence: 99%

Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks

M. Narender Pavan

2024

jes

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“…That's why it is necessary to study the information transfer methods aimed at improving energy efficiency (i.e. reducing energy costs), including artificial intelligence [1] for frequency allocation, blockchain technology for infrastructure sharing, and the search for new signal generation methods and their modulation [2][3][4][5] to overcome intersymbol and interchannel interference. All this leads to a global trend in the development of telecommunications technologies in the direction of their convergence [6].…”

Section: Introductionmentioning

confidence: 99%

Research of distribution of information flows in a network

Politanskyi¹,

Zarytska²,

Vistak³

et al. 2021

Math. Model. Comput.

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The algorithm for determining information flows in the network is developed and studied, taking into account restrictions on the input and output flows values in each node of the system. The algorithm is based on the Gaussian method of solving linear equations systems in case when the rank of the equivalent system matrix, is less than the number of unknown variables in the system. Using this algorithm, the capacities of sets of the integer solutions for networks with 2 nodes (flow intensity up to 200), 3 nodes (flow intensity up to 20) and 4 nodes (flow intensity up to 10) are calculated.

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“…The development of telecommunication technologies has led to an active search for new information transmission technologies which use complex chaotic signals [1], methods of signal processing [2] and modulation using artificial intelligence systems [3]. The transition to new frequency bands and the complexity of signal processing processes led to the search for new models and methods in radio systems [4,5].…”

Section: Introductionmentioning

confidence: 99%

Simulation of the Propagation of Electromagnetic Oscillations by the Method of the Modified Equation of the Telegraph Line

Politansky

Nytrebych

Petryshyn

et al. 2021

Phys. Chem. Solid St.

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The article considers the physical processes associated with the propagation of electromagnetic oscillations in a long line, the size of which is the same or slightly greater than the length of the electromagnetic wave (not more than ten times). As a research method, the differential-symbolic method is used, which is applied to the modified equation of the telegraph line. The boundary conditions for the two-point problem as well as additional parameters that are coefficients for the first derivatives in terms of coordinate and time in comparison with the classical equation of the telegraph line are considered as parameters for controlling the process of propagation of electromagnetic oscillations. Based on the differential-symbolic method, the boundary conditions of the two-point problem are found, under which the most characteristic oscillatory processes are realized in a long line. Based on the research, it is possible to draw conclusions about the effectiveness of analytical methods for the analysis of specific technical objects and control of the processes that take place in them.

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Application of Artificial Intelligence in Cognitive Radio for Planning Distribution of Frequency Channels

Cited by 10 publications

References 7 publications

Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks

Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks

Research of distribution of information flows in a network

Simulation of the Propagation of Electromagnetic Oscillations by the Method of the Modified Equation of the Telegraph Line

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