Joint Optimization-Based QoS and PAPR Reduction Technique for Energy-Efficient Massive MIMO System

Abstract

Massive multiple input multiple output (MIMO) is an innovative wireless communication technology that significantly enhances the capacity and efficiency of data transmission in modern networks. Massive multiple input multiple output, despite benefits, faces difficulties in managing numerous antennas affecting signal quality, peak-to-average power ratio (PAPR), and energy efficiency due to its scale and complexity. However, overcoming these complexities is vital for unlocking massive multiple input multiple output’s potential in high-quality, energy-efficient wireless communication. A proposed joint optimization-based technique aims to address these issues in massive MIMO systems. In phase 1, joint optimization with quality of service maximizes capacity via power distribution, employing the hybrid spider wasp Fick’s law algorithm. In phase 2, zero-forcing with symbol-level linear precoding, especially the stacked convolutional sparse bidirectional long short-term memory autoencoder (SCS–BiLSTMAE) network, efficiently reduces peak-to-average power ratio, collaborating for reduced bit rate error and peak-to-average power ratio through adaptive mapping. Refining the model involves precise hyperparameter tuning using the enhanced influencer buddy optimization algorithm. The proposed framework not only demonstrates exceptional performance, but the metrics also underscore the model's effectiveness in addressing diverse challenges, establishing it as a robust solution for quality enhancement, peak-to-average power ratio reduction, and energy efficiency. Additionally, the proposed model attains 28.14% greater system capacity than existing methods.