Comparison and Performance Analysis of Massive Mimo Based 5g Communication Network Through Potential Parameters

To provide high-speed, low-latency, and dependable wireless access, 5G communication networks have completely changed the telecoms sector. Massive Multiple-Input Multiple-Output (MIMO) stands out among the primary technologies adopted in 5G networks as a promising approach to addressing the applications' exploding demand for data. This research offers a hybrid deep learning (DL) approach that combines Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks for analyzing various functioning parameters to further improve the functioning of Massive MIMO in 5G networks. Throughput, energy efficiency, and residual energy are the three key performance indicators that are the focus of this study. Energy efficiency assesses the network's capacity to support high data rates while using the least amount of energy possible. Throughput quantifies the pace at which data may be transmitted over the network. The energy that is still present in the system after the intended tasks have been completed is referred to as residual energy. The huge MIMO system's spatial attributes, including spatial correlations and channel parameters, are extracted from the system using CNNs in the proposed hybrid DL model. RNNs are used to simulate the network's temporal dynamics, making it possible to forecast time-varying channel conditions and identify temporal dependencies in the data. The model's capacity to analyze complicated network behavior is further improved by the use of LSTM networks to manage long-term dependencies and memory retention. The presentation of the proposed hybrid DL prototype is assessed and contrasted with conventional techniques through thorough simulations and tests. The outcomes show that the hybrid DL strategy performs better than other approaches.