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Efficient Channel Estimation in Massive MIMO Partially Centralized Cloud-Radio Access Network Systems

Efficient Channel Estimation in Massive MIMO Partially Centralized Cloud-Radio Access Network Systems

Emmanuel Mukubwa, Oludare Sokoya
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 23
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781799861645|DOI: 10.4018/IJERTCS.20210101.oa4
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MLA

Mukubwa, Emmanuel, and Oludare Sokoya. "Efficient Channel Estimation in Massive MIMO Partially Centralized Cloud-Radio Access Network Systems." IJERTCS vol.12, no.1 2021: pp.64-86. http://doi.org/10.4018/IJERTCS.20210101.oa4

APA

Mukubwa, E. & Sokoya, O. (2021). Efficient Channel Estimation in Massive MIMO Partially Centralized Cloud-Radio Access Network Systems. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 12(1), 64-86. http://doi.org/10.4018/IJERTCS.20210101.oa4

Chicago

Mukubwa, Emmanuel, and Oludare Sokoya. "Efficient Channel Estimation in Massive MIMO Partially Centralized Cloud-Radio Access Network Systems," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 12, no.1: 64-86. http://doi.org/10.4018/IJERTCS.20210101.oa4

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Abstract

This article investigates channel estimation problem in massive MIMO partially centralized cloud-RAN (MPC-RAN). The channel estimation was realized through compressed data method to minimize the huge pilot overhead, then combined with parallel Givens data projection method (PGDPM) to form a semi-blind estimator. Comparison and analysis of improved minimum mean square error (MMSE), fast data projection method (FDPM), compressed data, and PGDPM techniques was evaluated for achievable normalized mean square error (NMSE) in MPC-RAN. The PGDPM-based estimator had the lowest normalized mean square error. The FDPM and PGDPM based methods are comparable in performance with PGDPM based estimator having a slight edge over FDPM-based estimator. This vindicates PGDPM-based estimator as a method to be utilized in channel estimation since it compresses the massive MIMO channel information, hence mitigating the fronthaul finite capacity problem, and at the same time, it is geared towards efficient parallelization for optimal BBU resource utilization.