Background: Massive MIMO systems are vital to 5G networks, enhancing spectral efficiency, reducing latency, and increasing capacity to support technologies like AR, VR, and IoT. By employing large antenna arrays to serve multiple users simultaneously, massive MIMO addresses challenges like channel estimation errors and resource allocation complexities. Solutions like hybrid beamforming, machine learning, and mobile edge computing (MEC) integration aim to optimize performance and minimize latency.

Objectives: This thesis explores optimization techniques for massive MIMO systems through a literature review to identify limitations and proposes novel techniques. Effectiveness is tested via simulations . It evaluates the capacity and energy efficiency of massive MIMO in 5G frequency bands and provides practical deployment recommendations.

Methods: A detailed literature review identifies limitations in current massive MIMO designs, focusing on challenges like energy and spectrum efficiency. The study integrates deep learning and genetic algorithm to enhance beamforming strategies, addressing key trade-offs. Only recent and relevant studies were analyzed to ensure a robust foundation for the proposed methods.

Results: The proposed hybrid neural network model, enhanced by a genetic algorithm, significantly improves beamforming accuracy, computational efficiency, and robustness under challenging conditions like noise and interference. The model outperforms traditional methods, validating its potential for next-generation communication systems.

Conclusions: This thesis introduces a hybrid approach combining deep learning and genetic algorithms to tackle challenges in massive MIMO systems. The model reduces training time , enhances beamforming accuracy, and maintains performance under noise and interference, making it ideal for real-time applications. It optimizes sum rate and energy efficiency, offering a sustainable solution for 5G deployment and setting a new benchmark for modern wireless communication systems.