Title:	Iterative data-aided algorithms and hardware impairments in Massive MIMO systems.
DNr:	LiU-2019-1
Project Type:	LiU Compute
Principal Investigator:	Daniel Verenzuela <daniel.verenzuela@liu.se>
Affiliation:	Linköpings universitet
Duration:	2019-01-24 – 2020-08-01
Classification:	20203
Keywords:

Abstract

Next generation wireless networks aim at providing substantial improvements in spectral efficiency (SE) and energy efficiency (EE). Massive MIMO has been proved to be a viable technology to achieve these goals by spatially multiplexing several users using many base station (BS) antennas. A potential limitation of Massive MIMO in multicell systems is pilot contamination, which arises in the channel estimation process from the interference caused by reusing pilots in neighboring cells. A standard method to reduce pilot contamination, known as regular pilot (RP), is to adjust the length of pilot sequences while transmitting data and pilot symbols disjointly. An alternative method, called superimposed pilot (SP), sends a superposition of pilot and data symbols. This allows to use longer pilots which, in turn, reduces pilot contamination. In this project we study the use iterative data-aided channel estimation algorithms with RP and SP to improve the SE and EE of Massive MIMO systems. Due to the large number of BS antennas, the implementation of Massive MIMO requires low-complexity hardware at each antenna branch that, in turn, increases distortions. This work also studies the optimal selection of per-antenna hardware quality in terms of analog-to-digital converters (ADCs) resolution in order to maximize SE and EE. For this project a large number of Monte Carlo simulations are needed to simulate all scenarios, including solving optimization problems, coding and decoding algortihms.