Resource optimization for massive MIMO systems

The thesis delves into the intricacies of resource optimization in both massive Multiple-Input Multiple-Output (MIMO) and cell-free massive MIMO (CFmMIMO) systems, which are pivotal for the advancement of 5G and beyond wireless networks. The research primarily addresses the challenges of pilot contamination and power allocation, which significantly impact the spectral efficiency (SE) and overall performance of these systems. Initially, the thesis explores the massive MIMO systems, focusing on the impact of pilot overhead and the accuracy of channel estimation on the SE. Closed-form expressions for the uplink (UL) and downlink (DL) SEs under conditions of imperfect channel state information (CSI) are derived. These expressions are crucial in understanding the trade-offs involved in pilot resource allocation, emphasizing that efficient pilot management is essential for maintaining high system performance. The analysis provides the closed-form expressions as vital tools for selecting optimal pilot overhead parameters. In the latter part, the thesis shifts focus to CFmMIMO systems, which distribute antennas across a large area to provide uniform coverage and enhance the performance of cell-edge users. Here, the primary challenge addressed is the downlink power control. Traditional methods for power control are computationally intensive and often inadequate for the centralized nature of CFmMIMO systems. To overcome these limitations, the research introduces advanced deep learning techniques, specifically Attention Neural Networks (ANN) and Pilot contamination-Aware Power Control (PAPC) transformer neural network, for power control. These models leverage the capabilities of masked multi-head attention networks, enabling efficient power allocation even in the presence of pilot contamination. The ANN-based approach initially transforms the constrained optimization problem into an unconstrained one, optimized through unsupervised learning. Subsequently, PAPC further refines this approach by incorporating additional architectural enhancements, such as pre-processing and post-processing stages, which improve performance and scalability while reducing computational complexity. Extensive simulations validate the effectiveness of these proposed solutions, demonstrating their potential to significantly reduce the computational complexity while providing state-of-the-art performance in CFmMIMO systems. In conclusion, this thesis makes significant contributions to the field of wireless communications by providing innovative solutions and comprehensive analytical tools for resource optimization in massive MIMO and CFmMIMO systems. The findings and methodologies presented are expected to pave the way for more efficient and reliable next-generation wireless communication technologies, addressing critical challenges in pilot resource allocation and power control.