Resource optimization for massive MIMO systems

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School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2024-12-20
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Authors

Date

2024

Department

Informaatio- ja tietoliikennetekniikan laitos
Department of Information and Communications Engineering

Major/Subject

Mcode

Degree programme

Language

en

Pages

66 + app. 50

Series

Aalto University publication series DOCTORAL THESES, 278/2024

Abstract

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.

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Supervising professor

Vorobyov, Sergiy., Prof., Aalto University, Department of Information and Communications Engineering, Finland

Thesis advisor

Vorobyov, Sergiy., Prof., Aalto University, Department of Information and Communications Engineering, Finland

Keywords

massive MIMO, cell-free massive MIMO, resource allocation, pilot contamination, power allocation, deep learning in wireless communication, GPT, BERT

Other note

Parts

  • [Publication 1]: A. K. Kocharlakota, K. Upadhya and S. A. Vorobyov. On Achievable Rates for Massive Mimo System with Imperfect Channel Covariance Information. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, pp. 4504-4508, 2019.
    DOI: 10.1109/ICASSP.2019.8682163 View at publisher
  • [Publication 2]: A. K. Kocharlakota, K. Upadhya and S. A. Vorobyov. Impact of Pilot Overhead and Channel Estimation on the Performance of Massive MIMO. IEEE Transactions on Communications, vol. 69, no. 12, pp. 8242-8255, Dec 2021.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202201121170
    DOI: 10.1109/TCOMM.2021.3112213 View at publisher
  • [Publication 3]: A. K. Kocharlakota, S. A. Vorobyov and R. W. Heath. Attention Neural Network for Downlink Cell-Free Massive MIMO Power Control. In Proceedings of the 56th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, pp. 738-743, 2022.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202405223518
    DOI: 10.1109/IEEECONF56349.2022.10051863 View at publisher
  • [Publication 4]: A. K. Kocharlakota, S. A. Vorobyov and R. W. Heath. Pilot Contamination Aware Transformer Neural Network for Downlink Power Control in Cell-Free Massive MIMO Networks. Submitted to IEEE Trans. Wireless Commun., Jun 2024.
    DOI: 10.48550/arXiv.2411.19020 View at publisher

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https://urn.fi/URN:ISBN:978-952-64-2199-5

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