Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation

Sparse Bayesian learning, beamforming techniques and asymptotic analysis for massive MIMO

Abstract : Multiple antennas at the base station side can be used to enhance the spectral efficiency and energy efficiency of the next generation wireless technologies. Indeed, massive multi-input multi-output (MIMO) is seen as one promising technology to bring the aforementioned benefits for fifth generation wireless standard, commonly known as 5G New Radio (5G NR). In this monograph, we will explore a wide range of potential topics in multi-userMIMO (MU-MIMO) relevant to 5G NR,• Sum rate maximizing beamforming (BF) design and robustness to partial channel stateinformation at the transmitter (CSIT)• Asymptotic analysis of the various BF techniques in massive MIMO and• Bayesian channel estimation methods using sparse Bayesian learning.One of the potential techniques proposed in the literature to circumvent the hardware complexity and power consumption in massive MIMO is hybrid beamforming. We propose a globally optimal analog phasor design using the technique of deterministic annealing, which won us the best student paper award. Further, in order to analyze the large system behaviour of the massive MIMO systems, we utilized techniques from random matrix theory and obtained simplified sum rate expressions. Finally, we also looked at Bayesian sparse signal recovery problem using the technique called sparse Bayesian learning (SBL). We proposed low complexity SBL algorithms using a combination of approximate inference techniques such as belief propagation (BP), expectation propagation and mean field (MF) variational Bayes. We proposed an optimal partitioning of the different parameters (in the factor graph) into either MF or BP nodes based on Fisher information matrix analysis.
Complete list of metadata
Contributor : ABES STAR :  Contact
Submitted on : Wednesday, November 3, 2021 - 4:22:34 PM
Last modification on : Tuesday, November 16, 2021 - 4:38:28 AM


Version validated by the jury (STAR)


  • HAL Id : tel-03140016, version 2


Christo Kurisummoottil Thomas. Sparse Bayesian learning, beamforming techniques and asymptotic analysis for massive MIMO. Engineering Sciences [physics]. Sorbonne Université, 2020. English. ⟨NNT : 2020SORUS231⟩. ⟨tel-03140016v2⟩



Record views


Files downloads