Genetic Algorithm Based Optimum Finger Selection for Adaptive Minimum Mean Square Error Rake Receivers Discrete Sequence-CDMA Ultra-Wide Band Systems

Adame, Berhan Oumer; Salau, Ayodeji Olalekan

doi:10.1007/s11277-021-09199-0

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…Among other things, this is due to destructive interference at the signal level. However, there are solutions to reduce interference, so the use of such procedures on the UE side is necessary (there is much 5G-related research of this kind, such as [ 28 ]).…”

Section: Resultsmentioning

confidence: 99%

Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication

Makara

Csathó

Rácz

et al. 2023

Sensors

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A significant innovation for future indoor wireless networks is the use of the mmWave frequency band. However, an important challenge comes from the restricted propagation conditions in this band, which necessitates the use of beamforming and associated beam management procedures, including, for instance, beam tracking or beam prediction. A possible solution to the beam management problem is to use artificial-intelligence-based procedures to learn the hidden spatial propagation patterns of the channel and to use this knowledge to predict the best beam directions. In this paper, we present a deep-neural-network-based method that has memory that can be used to predict the best reception directions for moving users. The best direction is the highest expected signal level at the next moment. The resulting method allows for a user-side antenna management system. The result was evaluated using three different metrics, thus detailing not only its predictive ability, but also its usability.

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Section: Resultsmentioning

confidence: 99%

Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication

Makara

Csathó

Rácz

et al. 2023

Sensors

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“…MSE is a metric calculated using two parameters, the target data (true BPSK signal) and RNN output (predicted BPSK signal) [41]. Therefore the smaller value of MSE means a similarity between the target data and the network output data, which is what we are looking for in this case [42]. Fig.…”

Section: Architecture Of Rnnmentioning

confidence: 99%

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Alqahtani¹,

Alsulami²,

Alahmari³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability, scalability, and enhancement of wireless mesh networking. This standard uses a physical layer of binary phase-shift keying (BPSK) modulation and can be operated with two frequency bands, 868 and 915 MHz. The frequency noise could interfere with the BPSK signal, which causes distortion to the signal before its arrival at receiver. Therefore, filtering the BPSK signal from noise is essential to ensure carrying the signal from the sender to the receiver with less error. Therefore, removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor networks. Moreover, researchers have reported a positive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems, including ZigBee. Meanwhile, artificial neural network (ANN) and machine learning (ML) models outperformed results for predicting signals for detection and classification purposes. This paper develops a neural network predictive detection method to enhance the performance of BPSK modulation. First, a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network (WPAN) standard. Then, Gaussian noise was injected into the BPSK simulation model. To reduce the noise of BPSK phase signals, a recurrent neural networks (RNN) model is implemented and integrated at the receiver side to estimate the BPSK's phase signal. We evaluated our predictive-detection RNN model using mean square error (MSE), correlation coefficient, recall, and F1-score metrics. The result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient (R-value) metric for different signal-to-noise (SNR) values. In addition, our RNN-based model scored 98.71% and 96.34% based on recall and F1-score, respectively.

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“…Dynamic testing examines the dynamic behavior and results of a program by running it on sample test data to find bugs. Dynamic testing consists of three core components: generating test cases, running programs, and verifying program results [20]. Liu et al proposed how to reasonably and effectively select the test data as the input from the huge input, so as to meet the specific coverage criteria, and how to design good test data, which will affect the test quality and then the software quality.…”

Section: Related Workmentioning

confidence: 99%

Optimal Model of Software Testing Path Selection Based on Genetic Algorithm and Its Evolutionary Solution

Zhan

2022

Wireless Communications and Mobile Computing

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In software testing, the selection of test data is a difficult problem in structural testing. Whether the test data is appropriate or not is directly related to whether the error can be expected to be detected. In the process of software testing, the generation of test data is not only the core problem but also the key and difficulty of software testing. Because of the huge number of test cases and low test efficiency, a powerful optimization algorithm is needed to optimize the initial test cases. As a robust search method, genetic algorithm shows unique advantages and high efficiency in solving high-complexity problems such as large space, multipeak, nonlinear, and global optimization. Based on the application of genetic algorithm, this paper analyzes the optimization path by classifying and calculating the objective function and introducing NSGA-II algorithm, measures the distance between each branch on the processing path sample set, and sorts the path set to obtain the optimal solution. On the basis of the designed model, the experimental results show that the error control rate of the model is 89.4%. Moreover, because of the superiority of NSGA-II algorithm, the probability of comprehensive cross mutation is increased by 56.7%.

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Genetic Algorithm Based Optimum Finger Selection for Adaptive Minimum Mean Square Error Rake Receivers Discrete Sequence-CDMA Ultra-Wide Band Systems

Cited by 13 publications

References 21 publications

Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication

Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Optimal Model of Software Testing Path Selection Based on Genetic Algorithm and Its Evolutionary Solution

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