[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125730-en":3,"doc-seo-125730-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125730,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Experimental Testing and Validation of Adaptive Equalizer Using Machine Learning Algorithm","Increasing demand for high-speed data transmission drives advances in wireless communication, yet impairments in wireless channels degrade network performance. Channel equalization mitigates these effects, and machine learning enables data-driven solutions without explicit channel models. The paper proposes a hybrid adaptive equalizer combining tracking and training modes, using ML to classify RF signal environments such as high, medium, low, and open-space clutter. Results compare Logistic Regression, KNN, SVM, Naive Bayes, Decision Tree, and Random Forest using accuracy, sensitivity, specificity, F1 score, and confusion matrices.","Experimental Testing and Validation of Adaptive Equalizer Using Machine Learning Algorithm  \nAnnapurna H. S1, A. Rijuvana Begum2  \n1Research scholar, PRIST Deemed To be University, Thanjavur, TN-612002, India  \ne-mail: [anupankaj1@gmail.com](anupankaj1@gmail.com)  \n2Research Supervisor, PRIST Deemed To be University, Thanjavur, TN-612002, India  \ne-mail: [arbbegum@gmail.com](arbbegum@gmail.com)  \nAbstract— Due to the increasing demand for high-speed data transmission, wireless communication has become more advanced. Unfortunately, the various kinds of impairments that can occur when carrying data symbols through a wireless channel can affect the network performance. Some of the solutions that are proposed to address these issues include channel equalization, and that can be solved through machine learning techniques. In this paper, a hybrid approach is proposed that combines the features of tracking mode and training mode of adaptive equalizer. This method utilizes the concept of machine learning (ML) to classify different environments (highly, medium, low, open space cluttered) based on the measurements of their RF signal. The results of the study revealed that the proposed method can perform well in real-time deployments. The performance of ML algorithms namely Logistic Regression, KNN Classifier, SVM Classifier, Naive Bayes, Decision Tree classifier and Random Forest classifier is analyzed for different number of samples such as 10, 50 and 100. The performance of these algorithms is evaluated by comparing their accuracy, sensitivity, specificity, F1 score and Confusion Matrix. The objective of this study is to demonstrate that a single ML algorithm cannot perform well in all kinds of environments. In order to choose the best algorithm for a given environment, the decision device has to analyze the various factors that affect the performance of the system. For instance, the random forest classifier performed well in terms of accuracy (100 percent), specificity (100 percent), sensitivity (100 percent), and F1_score (100 percent) . On the other hand, the logistic regression algorithm did not perform well in low cluttered environment.  \nKeywords-Classification; Machine Learning; SVM; Cognitive radio; Spectrum sensing.  \nI. INTRODUCTION  \nThe rapid emergence and evolution of new wireless communication technologies such as the Internet of Things and virtual reality are expected to have a significant impact on the future of wireless communications. To meet the increasing demand for high-speed data, a higher bandwidth is required. Due to the complexity of these new applications, the development of new wireless communication systems has become more challenging. For instance, the low-latency requirements of large-scale networks have become an issue [1],[2] . Due to the increasing number of wireless communication applications, the fading of the channel is becoming worse. This issue usually occurs due to the lack of bandwidth [3] . The effect of phase and amplitude distortion and the limited band channel are some of the factors that cause the inter symbol interference (ISI) . The evolution of the channel distortion is a time-related phenomenon that affects the performance of mobile communication. Therefore, the adaptive equalizers should be able to recognize the various characteristics of the channel [4- 6] . To achieve channel equalization, an adaptive filter first needs to adjust the taps weight. This process can be carried out through a training sequence mode, which is designed to adjust the coefficients according to the specific criteria determined by the  \nalgorithm. Therefore, in order to maintain a reliable transmission of data, an adaptive equalizer must be implemented in the receiver. This type of device is usually used in mobile transmissions since the channel model is unknown. The best performance of this type of adaptive equalizer is usually obtained with the use of non-linear structures [7] .  \nDueto the remarkable success","cbCaiteSTVlXdkcc","https://ap.wps.com/l/cbCaiteSTVlXdkcc","pdf",561140,1,11,"English","en",105,"# Introduction\n## Motivation and challenges in wireless communication\n## Role of adaptive equalizers and channel equalization\n## Machine learning for wireless communication and equalization","[{\"question\":\"What problem does the paper address in wireless communication?\",\"answer\":\"It addresses performance degradation caused by impairments that occur when transmitting symbols through wireless channels, and the resulting need for effective channel equalization.\"},{\"question\":\"How does the proposed adaptive equalizer combine tracking and training modes?\",\"answer\":\"It uses a hybrid approach that leverages both tracking mode and training mode features, selecting behavior based on environment classification derived from RF measurements.\"},{\"question\":\"Which machine learning algorithms are evaluated and how is performance measured?\",\"answer\":\"Logistic Regression, KNN, SVM, Naive Bayes, Decision Tree, and Random Forest are tested, and performance is evaluated using accuracy, sensitivity, specificity, F1 score, and confusion matrix results.\"}]","Experimental Testing and Validation of Adaptive Equalizer Using Machine Learning Algorithm | 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problem does the paper address in wireless communication?","Question",{"text":75,"@type":76},"It addresses performance degradation caused by impairments that occur when transmitting symbols through wireless channels, and the resulting need for effective channel equalization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed adaptive equalizer combine tracking and training modes?",{"text":80,"@type":76},"It uses a hybrid approach that leverages both tracking mode and training mode features, selecting behavior based on environment classification derived from RF measurements.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms are evaluated and how is performance measured?",{"text":84,"@type":76},"Logistic Regression, KNN, SVM, Naive Bayes, Decision Tree, and Random Forest are tested, and performance is evaluated using accuracy, sensitivity, specificity, F1 score, and confusion matrix 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