Design of Congestion Prediction Model on Network Towers using Rough Set Theory for High Speed Low Latency Communication

Wireless communication for data and a variety of wireless interacted devices have increased dramatically in the past few years. Millimeter wave (mmWave) technology can serve the primary objectives of 5G networks, which include high data throughput and low latency. But mmWave signals for communications lacking substantial diffraction and are consequently more susceptible to obstruction by environmental physical objects, which could cause communication lines to be disrupted and congestion takes place. Wireless data transmission suffers from blockages and path loss, causes high latency as well as reduces the data transmission speed and degrades in quality performance. To overcome the limitations, Rough Set Theory with hypertuned SVM is implemented and designed the congestion prediction model based on the behaviour of network towers for low latency and high-speed data transmission. The data from the different towers is initially collected and created as a dataset. Super MICE is a technique to replace the missing data. Then, the Rough Set Theory is utilized to cluster the data into equivalent classes based on the behaviour of 5G, 4G and 3G wireless network. Hypertuned SVM with a Gazelle optimization algorithm is applied to predict the congestion level by accurately selecting the hyperparameter. By employing performance metrics, the proposed approach is examined and contrasted with existing techniques. The evaluation of performance measurements for the proposed method includes informedness attained as 91%, Adjusted Rand Index obtained value as 0.83, Jaccard as 0.737. Accuracy, precision, sensitivity, error, F1_score, and NPV are also achieved at 93%, 92%, 94%, 7%, 92%, and 90%, respectively. According to this evaluation, the proposed model is superior to perform than the earlier used existing methods.