Design and Analysis of Antenna Selection in Massive MIMO
(2024) EITM01 20232Department of Electrical and Information Technology
- Abstract
- Mobile communications have evolved with the commercial deployment of 5G and initial studies of 6G. New solutions and innovative technologies are being incorporated to reach the target requirements. One of the key technologies in mobile networks is massive multiple-input-multiple-output (MIMO). Increasing the number of antennas offers significant benefits in coverage, reliability, and spectral efficiency. However, the high cost, power consumption, and increased complexity of deploying large antenna arrays at the base stations (BS) could become a burden in real scenarios. Antenna selection (AS) could be a potential solution to alleviate the large number of radio frequency (RF) chains. This master thesis addresses the AS problem where a... (More)
- Mobile communications have evolved with the commercial deployment of 5G and initial studies of 6G. New solutions and innovative technologies are being incorporated to reach the target requirements. One of the key technologies in mobile networks is massive multiple-input-multiple-output (MIMO). Increasing the number of antennas offers significant benefits in coverage, reliability, and spectral efficiency. However, the high cost, power consumption, and increased complexity of deploying large antenna arrays at the base stations (BS) could become a burden in real scenarios. Antenna selection (AS) could be a potential solution to alleviate the large number of radio frequency (RF) chains. This master thesis addresses the AS problem where a high-quality subset of the available transmit antennas at the BS are active whereas the rest of the antennas are switched off. The AS criterion is to select the submatrix with the largest Minimum Singular Value (MSV) maximizing the Signal to Noise Ratio (SNR) so the Bit Error Rate (BER) is decreased. Fully flexible full-array switching (FAS) and sub-array switching (SAS) networks are considered. Exhaustive search becomes unfeasible even as the number of antennas grows therefore sub-optimal and optimal alternatives are needed. Greedy and Branch-and-Bound (BAB) searching algorithms for both switching networks are introduced and compared in terms of complexity and relative error for obtaining the largest MSV. Simulations carried out for scenarios with different antenna configurations show that all algorithms improve the BER compared to random antenna selection. However, the gain reduces as the percentage of selected antennas to the total available antennas increases. Regarding capacity, most of the algorithms obtained better ergodic capacity than random antenna selection. In addition, there is no significant difference in the BER performance between algorithms so when there are many antennas at the BS, sub-optimal approaches might be preferred to save computation time. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9171726
- author
- Jiménez Alonso, Guillermo LU
- supervisor
- organization
- course
- EITM01 20232
- year
- 2024
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- 5G, Massive MIMO, Antenna Selection, BER, Branch-and-Bound, Full-Array Switching, Sub-Array Switching, Minimum Singular Value.
- report number
- LU/LTH-EIT 2024-1013
- language
- English
- id
- 9171726
- date added to LUP
- 2024-09-05 13:49:32
- date last changed
- 2024-09-05 13:49:32
@misc{9171726,
abstract = {{Mobile communications have evolved with the commercial deployment of 5G and initial studies of 6G. New solutions and innovative technologies are being incorporated to reach the target requirements. One of the key technologies in mobile networks is massive multiple-input-multiple-output (MIMO). Increasing the number of antennas offers significant benefits in coverage, reliability, and spectral efficiency. However, the high cost, power consumption, and increased complexity of deploying large antenna arrays at the base stations (BS) could become a burden in real scenarios. Antenna selection (AS) could be a potential solution to alleviate the large number of radio frequency (RF) chains. This master thesis addresses the AS problem where a high-quality subset of the available transmit antennas at the BS are active whereas the rest of the antennas are switched off. The AS criterion is to select the submatrix with the largest Minimum Singular Value (MSV) maximizing the Signal to Noise Ratio (SNR) so the Bit Error Rate (BER) is decreased. Fully flexible full-array switching (FAS) and sub-array switching (SAS) networks are considered. Exhaustive search becomes unfeasible even as the number of antennas grows therefore sub-optimal and optimal alternatives are needed. Greedy and Branch-and-Bound (BAB) searching algorithms for both switching networks are introduced and compared in terms of complexity and relative error for obtaining the largest MSV. Simulations carried out for scenarios with different antenna configurations show that all algorithms improve the BER compared to random antenna selection. However, the gain reduces as the percentage of selected antennas to the total available antennas increases. Regarding capacity, most of the algorithms obtained better ergodic capacity than random antenna selection. In addition, there is no significant difference in the BER performance between algorithms so when there are many antennas at the BS, sub-optimal approaches might be preferred to save computation time.}},
author = {{Jiménez Alonso, Guillermo}},
language = {{eng}},
note = {{Student Paper}},
title = {{Design and Analysis of Antenna Selection in Massive MIMO}},
year = {{2024}},
}