Distributed Massive MIMO with Low-Resolution ADCs: Enhancing Efficiency through Deep Learning
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Information and communication technology (MPICT), MSc
Publicerad
2024
Författare
Amani, Elina
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Abstract
Distributed massive MIMO, including a central unit (CU) and a large number of spatially distributed antennas, provides more uniform quality of service (QoS) than co-located massive MIMO systems. One of the components used in distributed massive MIMO is the analog-to-digital converters (ADC). However, high-resolution ADCs consume a considerable power. Having a simple structure and a very low power consumption, the low-resolution ADCs, can be used to decrease both the complexity and power consumption. However, using such ADCs, introduces non-linear distortions in the received signals, thus, complicating channel estimation and data detection at the receiver. In this study, a distributed massive MIMO case with one-bit radio-over-fiber fronthaul has been studied where model-driven deep learning structures are used to compensate for the non-linear distortion caused by the low-resolution ADCs used in the communication system. The aim is to improve both channel estimation and data detection in the uplink phase.
Beskrivning
Ämne/nyckelord
Keywords: Distributed Massive MIMO, one-bit ADC, Deep Neural Network, Channel Estimation, Data Detection