Συντάχθηκε 04-02-2022 14:35
Τόπος: Η παρουσίαση θα γίνει με τηλεδιάσκεψη
Σύνδεσμος τηλεδιάσκεψης
Έναρξη: 07/02/2022 10:00
Λήξη: 07/02/2022 11:00
ΠΟΛΥΤΕΧΝΕΙΟ ΚΡΗΤΗΣ
Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών
Πρόγραμμα Προπτυχιακών Σπουδών
ΠΑΡΟΥΣΙΑΣΗ ΔΙΠΛΩΜΑΤΙΚΗΣ ΕΡΓΑΣΙΑΣ
ΠΑΚΤΙΤΗΣ ΣΠΥΡΙΔΩΝ
θέμα
Εκτίμηση Αραιών Καναλιών σε Ασύρματα Συστήματα 5ης Γενιάς
Estimation of Sparse Channels in 5G Wireless Systems
Εξεταστική Επιτροπή
Καθηγητής Αθανάσιος Λιάβας (επιβλέπων)
Καθηγητής Γεώργιος Καρυστινός
Καθηγητής Κωνσταντίνος Μπερμπερίδης (Παν. Πατρών)
Abstract
In this Diploma Thesis, we study a novel Channel State Information on the Transmitter side (CSIT) algorithm for the estimation of the CSIT in a multiuser, Frequency Division Multiplexing (FDD), massive MIMO wireless system. The main characteristic of a massive MIMO system is the large number of antennas at the Base Station (BS). This fact puts significant difficulties at the channel estimation process but offers significant benefits regarding spectral and energy efficiency, reliability, and capacity.
First, we present the concept of Compressive Sensing and the important research results of J.A. Tropp and A.C. Gilbert, including an algorithm for sparse signal recovery from random measurements via Orthogonal Matching Pursuit. In their work, Tropp and Gilbert propose a remarkably simple algorithm for tackling signal estimation when dealing with sparse channel matrices.
Then, we familiarise ourselves with the Angular Domain representation of signals, mainly based on the book by D. Tse and P. Viswanath.
Last, we present an algorithm for efficient channel estimation in Massive MIMO FDD systems proposed in a paper by X. Rao and V. K. N. Lau. We also studied the work of J. C. Shen, J. Zhang, K. C. Chen and K. B. Lataief (see also the work of M. Massod, L.H. Afify and T.Y. Al-Naffouri).
We apply and test the algorithm in various scenarios of interest, revealing that efficient massive MIMO channel estimation is possible under the hypothesis of sparsity in the angular domain.
