Deep Learning for Direction of Arrival Estimation in Massive MIMO Systems

Student thesis: Master thesis (including HD thesis)

  • Kasper Steensig Jensen
4. term, Signal Processing and Computing, Master (Master Programme)
With the dawn of fifth generation (5G) cellular networks massive MIMO systems have become more applicable. One problem in massive MIMO systems is pilot contamination caused by the dense placement of 5G cellular towers. Therefore it is of interest to use blind source separation instead to determine channel parameters. Traditional methods for blind source separation can be slow so alternatives are of interest. The three methods examined are deep neural networks, sparse estimation methods (ISTA based) and a fusion between the two (Learned-ISTA based). For direction of arrival estimation the methods provide comparable NMSE at the low end of 10e-1 to high end of 10e-2. The Learned-ISTA based methods show promise and further investigation of training it, model parameters and peak-finding algorithms for selecting the directions of arrival is relevant.
Publication date3 Jun 2021
Number of pages53
ID: 413609367