Though the key technology for 5G communication is the massive Multiple input and multiple output (MIMO), it has a major drawback when the user generated inference signals are to be handled by several other users. When these interference signals are used in various users, issues concerning system ability, power management issues and QoS related issues arises with the involvement of MIMO channel. Though internet of things (IoT) making use of MIMO is still under development, the requirements of the IoT is quite different when compared to several other connections. Thus, taking into account the demands, the solutions were framed with the help of the proposed system. One such solution is the Partition Square and Cross-Processing method that can possess several antennas at the given destination. Based on this solution obtained the data rate can be drastically increased correspondingly reducing the power consumed thus producing the most desirable result with high signal to noise ratio (SNR). Best victory is another such solution that solves the issues relating to power reduction thereby providing the best solution with minimum SNR. These solutions produced by the proposed system still has some disadvantages with respect to the following case: when the number of destination antennas is less than the number of source antennas. This case is considered as most common in the model proposed. For the above-mentioned case, near optimal solution is produced by both the algorithms. In this article, the transmitters are represented by the source antenna and the receiver is represented by the destination antenna. Thus, with the help of IoT connectivity, major benefits can be bought to the massive MIMO channel.
Building tools for code-mixed data is rapidly gaining popularity in the NLP research community as such data is exponentially rising on social media. Working with code-mixed data contains several challenges, especially due to grammatical inconsistencies and spelling variations in addition to all the previous known challenges for social media scenarios. In this article, we present a novel architecture focusing on normalizing phonetic typing variations, which is commonly seen in code-mixed data. One of the main features of our architecture is that in addition to normalizing, it can also be utilized for back-transliteration and word identification in some cases. Our model achieved an accuracy of 90.27% on the test data.
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