BER Analysis of Precoders in Multi-User MIMO
Abstract: For Multi-Input Multi-Output (MIMO) transmission systems, we present a diagonal Precoder with a Minimum Bit Error Rate (MBER). This research builds on the findings with optimized the global transmission system (precoder and equalizer) using the Minimum Mean Square Error (MMSE) criterion, a new diagonal precoder that minimizes the BER is used to optimize the system. Our research is inspired by the notion that people are more likely to favor a solution that reduces the BER over the Mean Square Error from a practical standpoint. Monte Carlo simulations employing a Quadratic Amplitude Modulation are used to demonstrate performance improvement (QAM).

Authors: Amit Kumar, Ankit Pandit

File Name: Amit_10080-22-101.pdf
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Sentiment Analysis of Twitter Data using Support Vector Machine
Abstract: The large amount of data generated by users on social networks has increasingly aroused interest in analyzing the opinions and sentiments that are being expressed. For this, one of the most used techniques is machine learning, which needs large datasets to function properly. However, few datasets for this purpose are available, limiting the development of applications in the language. Thus, this work aims to collect Twitter messages and classify their sentiments to create a dataset for the analysis of sentiments. This research work uses 2,787 messages that are publicly available at GitHub. Using the collected data, the support vector machine (SVM) classifier achieves an accuracy of 94.37%.

Authors: Dr. Hemant N. Patel, Dr. Amit N. Patel

File Name: Hemant_10080-22-102.pdf
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