Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications

Kiran Khurshid; Nasir Saeed; Nazia Bibi; Muhammad Usman Hadi

doi:10.1109/icetecc65365.2025.11070289

Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications

Kiran Khurshid
, Nasir Saeed
, Nazia Bibi
, Muhammad Usman Hadi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

This work introduces a hybrid channel estimation method designed for Massive MIMO (M-MIMO) systems in IoT applications. The proposed approach focuses on minimizing complexity while ensuring competitive performance. The hybrid method combines Least Squares (LS) and Minimum Mean Square Error (MMSE). It employs switching-based approaches with adaptive weight functions that adjust to varying channel conditions. Through comprehensive simulations involving 40 IoT devices, we analyze the impact of the Rician K-factor on the Relative Channel Estimation Error (RCEE) and evaluate the Average Variance of Channel Estimation Error (VarCEE) against the number of base station antennas under fixed K-factor. Our results demonstrate that the proposed hybrid method maintains performance comparable to MMSE while achieving lower computational complexity. This work highlights the potential of hybrid estimation techniques in enhancing the efficiency of MMIMO systems for IoT deployments.

Original language	English
Title of host publication	2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Electronic)	979-8-3315-4338-9
ISBN (Print)	979-8-3315-4338-9, 979-8-3315-4339-6
DOIs	https://doi.org/10.1109/icetecc65365.2025.11070289
Publication status	Published online - 14 Jul 2025
Event	2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC) - Jamshoro, Pakistan Duration: 23 Apr 2025 → 25 Apr 2025

Publication series

Name	2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)
Publisher	IEEE Control Society

Conference

Conference	2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)
Country/Territory	Pakistan
City	Jamshoro
Period	23/04/25 → 25/04/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

IoT
Massive MIMMO
Channel Estimation Error
Least Squares
Minimum Mean Square Error
Massive MIMO

Access to Document

10.1109/icetecc65365.2025.11070289

Cite this

Khurshid, K., Saeed, N., Bibi, N., & Hadi, M. U. (2025). Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications. In 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC) (pp. 1-5). (2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)). IEEE. Advance online publication. https://doi.org/10.1109/icetecc65365.2025.11070289

Khurshid, Kiran ; Saeed, Nasir ; Bibi, Nazia et al. / Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications. 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC). IEEE, 2025. pp. 1-5 (2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)).

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abstract = "This work introduces a hybrid channel estimation method designed for Massive MIMO (M-MIMO) systems in IoT applications. The proposed approach focuses on minimizing complexity while ensuring competitive performance. The hybrid method combines Least Squares (LS) and Minimum Mean Square Error (MMSE). It employs switching-based approaches with adaptive weight functions that adjust to varying channel conditions. Through comprehensive simulations involving 40 IoT devices, we analyze the impact of the Rician K-factor on the Relative Channel Estimation Error (RCEE) and evaluate the Average Variance of Channel Estimation Error (VarCEE) against the number of base station antennas under fixed K-factor. Our results demonstrate that the proposed hybrid method maintains performance comparable to MMSE while achieving lower computational complexity. This work highlights the potential of hybrid estimation techniques in enhancing the efficiency of MMIMO systems for IoT deployments.",

keywords = "IoT, Massive MIMMO, Channel Estimation Error, Least Squares, Minimum Mean Square Error, Massive MIMO",

author = "Kiran Khurshid and Nasir Saeed and Nazia Bibi and Hadi, \{Muhammad Usman\}",

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Khurshid, K, Saeed, N, Bibi, N & Hadi, MU 2025, Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications. in 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC). 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC), IEEE, pp. 1-5, 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC), Jamshoro, Pakistan, 23/04/25. https://doi.org/10.1109/icetecc65365.2025.11070289

Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications. / Khurshid, Kiran; Saeed, Nasir; Bibi, Nazia et al.
2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC). IEEE, 2025. p. 1-5 (2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications

AU - Khurshid, Kiran

AU - Saeed, Nasir

AU - Bibi, Nazia

AU - Hadi, Muhammad Usman

PY - 2025/7/14

Y1 - 2025/7/14

N2 - This work introduces a hybrid channel estimation method designed for Massive MIMO (M-MIMO) systems in IoT applications. The proposed approach focuses on minimizing complexity while ensuring competitive performance. The hybrid method combines Least Squares (LS) and Minimum Mean Square Error (MMSE). It employs switching-based approaches with adaptive weight functions that adjust to varying channel conditions. Through comprehensive simulations involving 40 IoT devices, we analyze the impact of the Rician K-factor on the Relative Channel Estimation Error (RCEE) and evaluate the Average Variance of Channel Estimation Error (VarCEE) against the number of base station antennas under fixed K-factor. Our results demonstrate that the proposed hybrid method maintains performance comparable to MMSE while achieving lower computational complexity. This work highlights the potential of hybrid estimation techniques in enhancing the efficiency of MMIMO systems for IoT deployments.

AB - This work introduces a hybrid channel estimation method designed for Massive MIMO (M-MIMO) systems in IoT applications. The proposed approach focuses on minimizing complexity while ensuring competitive performance. The hybrid method combines Least Squares (LS) and Minimum Mean Square Error (MMSE). It employs switching-based approaches with adaptive weight functions that adjust to varying channel conditions. Through comprehensive simulations involving 40 IoT devices, we analyze the impact of the Rician K-factor on the Relative Channel Estimation Error (RCEE) and evaluate the Average Variance of Channel Estimation Error (VarCEE) against the number of base station antennas under fixed K-factor. Our results demonstrate that the proposed hybrid method maintains performance comparable to MMSE while achieving lower computational complexity. This work highlights the potential of hybrid estimation techniques in enhancing the efficiency of MMIMO systems for IoT deployments.

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KW - Massive MIMMO

KW - Channel Estimation Error

KW - Least Squares

KW - Minimum Mean Square Error

KW - Massive MIMO

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M3 - Conference contribution

SN - 979-8-3315-4338-9

SN - 979-8-3315-4339-6

T3 - 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)

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PB - IEEE

T2 - 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)

Y2 - 23 April 2025 through 25 April 2025

ER -

Khurshid K, Saeed N, Bibi N, Hadi MU. Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications. In 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC). IEEE. 2025. p. 1-5. (2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC)). Epub 2025 Jul 14. doi: 10.1109/icetecc65365.2025.11070289

Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications

Abstract

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Bibliographical note

Keywords

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