1-Bit Massive MIMO Transmission: Embracing Interference with Symbol-Level Precoding

Li, Ang; Masouros, Christos; Swindlehurst, A. Lee; Yu, Wei

doi:10.1109/mcom.001.2000601

Cited by 105 publications

(53 citation statements)

References 15 publications

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“…e rapid development of Internet information technology has an important impact on students' growth, learning, and development. Although with the gradual deepening of China's educational reform, physical education teaching has made remarkable progress [6][7][8]. However, because some teaching ideas can not fully meet the needs of modern physical education, the e ect of physical education is poor and even can not meet the actual needs of modern physical education and talent training to some extent.…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation Method of College Sports Long Jump Training Course Based on Genetic Algorithm

Han

Wang

Zhao

2022

Mobile Information Systems

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In order to improve the accuracy of the quality evaluation of college sports long jump training courses, this paper studies the quality evaluation method of college sports long jump training courses based on a genetic algorithm. According to the purpose of college long jump training course evaluation, basic principles of teaching quality evaluation and classroom teaching evaluation standards constructs the establishment process of evaluation index system and calculates the weight of long jump training course quality evaluation index based on AHP method, so as to provide index basis for subsequent quality evaluation; The quality evaluation model of the long jump training course is constructed based on the genetic algorithm. Through cross operation and mutation operation based on adaptive mutation probability, the high accuracy evaluation of college sports long jump training course quality is realized. The experimental results show that the evaluation accuracy of this design method is higher than 97.21%, and the evaluation satisfaction is higher than 97.15%, which significantly improves the accuracy of the university sports long jump training courses and has practical significance in the course evaluation.

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Section: Introductionmentioning

confidence: 99%

Quality Evaluation Method of College Sports Long Jump Training Course Based on Genetic Algorithm

Han

Wang

Zhao

2022

Mobile Information Systems

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“…e paper resulted in a useful model for churn prediction classified customers based on a telecom company's churn. Li et al [28] have examined how symbol-level precoding may enhance the error-rate performance of large MIMO systems deploying 1-bit digital converters. Using logarithmic fuzzy preference programming, Ahmadi [29] has ranked and categorized the growthaffected knowledge-based economic indicators.…”

Section: Literature Review Of Churn Prediction Methodsmentioning

confidence: 99%

Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions

Mirabdolbaghi

Amiri

2022

Discrete Dynamics in Nature and Society

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Currently, Customers are struggling to retain their business in today’s competitive markets. Thus, the issue of customer churn becomes a significant challenge for the industries. In order to achieve this, it is vital to have an efficient churn prediction system. In this paper, we discuss methods for reducing features using PCA, Autoencoders, LDA, T-SNE, and Xgboost. In this paper, a model for predicting light GBM churn is proposed. The model consists of five steps. The first step is to preprocess the data so that missing and corrupt values can be handled and the data can be scaled. Secondly, implementing a comprehensive feature reduction system based on popular algorithms reduces the features and selects the most suitable one. In the third step, light GBM’s hyperparameter is tuned using Bayesian hyperparameter optimization and genetic optimization algorithms. Lastly, interpreting the model and evaluating the impact of the features on model outputs by using the SHAP method, and finally ranking the churners by customer lifetime value. Aside from evaluating and choosing the best feature reduction methods, the proposed method is also evaluated using four famous datasets. It outperforms other ensemble and ML algorithms like AdaBoost, SVM, and decision tree on over seven evaluation metrics: accuracy, area under the curve (AUC), Kappa, Mathews correlation coefficient (MCC), Brier score, F1 score, and EMPC. In light of the evaluation metrics, our model shows a significant improvement in handling imbalanced datasets in churn prediction. Finally, in this paper, interpretability and how the features affect the model’s output are presented by the SHAP method. Then CLV ranking is suggested for better decision-making.

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“…Xie et al [42] have proposed many heuristic or metaheuristic algorithms/methods to solve this NP-hard problem. Using the traditional undesired multiuser 3 Wireless Communications and Mobile Computing interference and the interference caused by imperfect hardware components, Li et al [43] summarize constructive interference (CI) and explain how it can benefit the 1-bit signal design. As a future multichannel communication application for terahertz (THz), Feng et al [44] presented a 220 GHz four-channel, noncontiguous, and manifold-coupled waveguide multiplexer.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

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1-Bit Massive MIMO Transmission: Embracing Interference with Symbol-Level Precoding

Cited by 105 publications

References 15 publications

Quality Evaluation Method of College Sports Long Jump Training Course Based on Genetic Algorithm

Quality Evaluation Method of College Sports Long Jump Training Course Based on Genetic Algorithm

Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

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