Compressive sensing applied to raw channel estimation

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Perustieteiden korkeakoulu | Master's thesis
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Date

2021-08-24

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Major/Subject

Applied Mathematics

Mcode

SCI3053

Degree programme

Master’s Programme in Mathematics and Operations Research

Language

en

Pages

43

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Abstract

Accurate and efficient channel estimation plays a key role in wireless communication. Signals that are designed to measure the channel quality between a transmitter and a receiver are called reference signals. One such signal used in 5G New Radio (NR) is called Sounding Reference Signal (SRS). SRS is a signal transmitted from the user equipment (UE) and received by the gNodeB base station (gNb BS). The signal is processed in the 5G Physical Layer (L1) in order to estimate the channel response of each frequency subcarrier by means of beamforming and symbol decoding. Due to large number of antennas in a 5G base station, channel estimation can be computationally expensive. This master’s thesis investigates how the number of receiving antennas used for SRS channel estimation can be reduced by sampling and the data can be approximately recovered using compressive sensing (CS), which is a class of optimization methods for finding sparse solutions...

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Supervisor

Oliveira, Fabricio

Thesis advisor

Medeiros, Luiz

Keywords

compressive sensing, sparse recovery, channel estimation, SRS, 5G, antenna sampling

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https://urn.fi/URN:NBN:fi:aalto-202108298506

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[dipl] Perustieteiden korkeakoulu / SCI