Trabajo fin de estudios
| Título | Estimacion Robusta de Canal para Sistemas 6G con MIMO Masivo |
| Tutor | David Morales Jiménez |
| Estado | Ofertado |
| Tipo | TFM_Máster Ing. Telecomunicación |
Descripción:
Massive multiple-input multiple-output (MIMO), where a base station equipped with many antennas (a few hundred) simultaneously serves many tens of users, has become one of the key technologies for fifth generation (5G) and future 6G wireless systems. This project will focus on the massive MIMO technology and, in particular, on robust channel estimation solutions suited to 6G scenarios of application.
In massive MIMO the effective channel vectors between the users and the base station are pairwisely (nearly) orthogonal. This property is key to improve transmission rates, as the uplink channel estimation can be performed in a semi-blind fashion from the received data, reducing the amount of training signals (overhead) to a great extent. As the massive MIMO technology continues to mature, new challenging scenarios of application need to be considered as, for example, high mobility scenarios where the users may travel at high speeds. This poses important challenges in the channel estimation, since large matrices will need to be estimated from a limited number of samples.
In this project, we will implement a semi-blind channel estimation scheme at the base station for single-cell massive MIMO. This scheme is based on a spectral decomposition approach, which requires accurate estimation of the eigenvectors of the channel covariance matrix. We will explore the use of a new approach, based on random matrix theory, to robustly estimate these eigenvectors, aiming to improve the channel estimation and, ultimately, the performance (e.g., data rates) of the revolutionary massive MIMO technology. We will evaluate the accuracy of the improved estimation scheme and compare it with previous and more conventional approaches.
This project is best suited to a student with an interest in signal processing for next generation wireless systems. Basic knowledge of statistics and probability, and fluency in MATLAB programming are highly recommended. The project will require the implementation of the covariance estimation and massive MIMO system simulation frameworks. The estimation and simulation frameworks will be developed in MATLAB.
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