Massive multiple-input multiple-output (MIMO) is the concept of deploying a very large number of antennas at the base stations (BS) of cellular networks. Frequency-division duplexing (FDD) massive MIMO systems in the downlink (DL) suffer significantly from the channel estimation overhead. In this thesis, we propose a minimum mean square error (MMSE)-based channel estimation framework that exploits the spatial correlation between the antennas at the BS to reduce the latter overhead. We investigate how the number of antennas at the BS affects the channel estimation error through analytical and asymptotic analysis. In addition, we derive a lower bound on the spectral efficiency of the communication system. Close form expressions of the asymptotic MSE and the spectral efficiency lower bound are obtained. Furthermore, perfect match between theoretical and simulation results is observed, and results show the feasibility of our proposed scheme.
","abstract_html":"<p>Massive multiple-input multiple-output (MIMO) is the concept of deploying a very large number of antennas at the base stations (BS) of cellular networks. Frequency-division duplexing (FDD) massive MIMO systems in the downlink (DL) suffer significantly from the channel estimation overhead. In this thesis, we propose a minimum mean square error (MMSE)-based channel estimation framework that exploits the spatial correlation between the antennas at the BS to reduce the latter overhead. We investigate how the number of antennas at the BS affects the channel estimation error through analytical and asymptotic analysis. In addition, we derive a lower bound on the spectral efficiency of the communication system. Close form expressions of the asymptotic MSE and the spectral efficiency lower bound are obtained. Furthermore, perfect match between theoretical and simulation results is observed, and results show the feasibility of our proposed scheme.</p>","abstract_has_math":false,"creators":["Mayouche, Abderrahmane"],"institution":null,"degree_name":"Master of Science in Electrical Engineering (MSEE)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wu, Jingxian","McCann, Roy A."],"advisors":["Yang, Jing"],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-08-01T07:00:00Z","date_published":"2016-08-01T07:00:00Z","updated_at":"2026-07-24T01:00:19Z","subjects":["Applied sciences","Asymptotic analysis","Channel estimation overhead","FDD","MSE","Massive MIMO","Spectral efficiency","Electrical and Electronics","Systems and Communications"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/1688","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wu, Jingxian","McCann, Roy A."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Yang, Jing"]},{"key":"dc:creator","label":"Author","values":["Mayouche, Abderrahmane"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-09-29T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Electrical Engineering (MSEE)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Applied sciences","Asymptotic analysis","Channel estimation overhead","FDD","MSE","Massive MIMO","Spectral efficiency","Electrical and Electronics","Systems and Communications"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/1688"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Massive multiple-input multiple-output (MIMO) is the concept of deploying a very large number of antennas at the base stations (BS) of cellular networks. Frequency-division duplexing (FDD) massive MIMO systems in the downlink (DL) suffer significantly from the channel estimation overhead. In this thesis, we propose a minimum mean square error (MMSE)-based channel estimation framework that exploits the spatial correlation between the antennas at the BS to reduce the latter overhead. We investigate how the number of antennas at the BS affects the channel estimation error through analytical and asymptotic analysis. In addition, we derive a lower bound on the spectral efficiency of the communication system. Close form expressions of the asymptotic MSE and the spectral efficiency lower bound are obtained. Furthermore, perfect match between theoretical and simulation results is observed, and results show the feasibility of our proposed scheme.
"]},{"key":"dc:title","label":"Title","values":["Channel Estimation Overhead Reduction for Downlink FDD Massive MIMO Systems"]}]}],"canonical_facts":{"dc:contributor":["Wu, Jingxian","McCann, Roy A."],"dc:contributor.advisor":["Yang, Jing"],"dc:creator":["Mayouche, Abderrahmane"],"dc:date":["2016"],"dc:date.available":["2017-09-29T07:00:00Z"],"dc:description.abstract":["Massive multiple-input multiple-output (MIMO) is the concept of deploying a very large number of antennas at the base stations (BS) of cellular networks. Frequency-division duplexing (FDD) massive MIMO systems in the downlink (DL) suffer significantly from the channel estimation overhead. In this thesis, we propose a minimum mean square error (MMSE)-based channel estimation framework that exploits the spatial correlation between the antennas at the BS to reduce the latter overhead. We investigate how the number of antennas at the BS affects the channel estimation error through analytical and asymptotic analysis. In addition, we derive a lower bound on the spectral efficiency of the communication system. Close form expressions of the asymptotic MSE and the spectral efficiency lower bound are obtained. Furthermore, perfect match between theoretical and simulation results is observed, and results show the feasibility of our proposed scheme.
"],"dc:identifier":["https://scholarworks.uark.edu/etd/1688"],"dc:subject":["Applied sciences","Asymptotic analysis","Channel estimation overhead","FDD","MSE","Massive MIMO","Spectral efficiency","Electrical and Electronics","Systems and Communications"],"dc:title":["Channel Estimation Overhead Reduction for Downlink FDD Massive MIMO Systems"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Electrical Engineering (MSEE)"]},"updated_at":"2026-07-24T01:00:19Z"}