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

Department of Electrical and Communication Engineering

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

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	2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331543389
DOIs	https://doi.org/10.1109/ICETECC65365.2025.11070289
Publication status	Published - 2025
Event	2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025 - Jamshoro, Pakistan Duration: Apr 23 2025 → Apr 25 2025

Publication series

Name	2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025

Conference

Conference	2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025
Country/Territory	Pakistan
City	Jamshoro
Period	4/23/25 → 4/25/25

Keywords

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

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence
Computer Science Applications
Electrical and Electronic Engineering

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 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025 (2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025). Institute of Electrical and Electronics Engineers Inc.. 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.
2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025. Institute of Electrical and Electronics Engineers Inc., 2025. (2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025).

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

Khurshid, K, Saeed, N, Bibi, N & Hadi, MU 2025, Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications. in 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025. 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025, Jamshoro, Pakistan, 4/23/25. https://doi.org/10.1109/ICETECC65365.2025.11070289

Khurshid K, Saeed N, Bibi N, Hadi MU. Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications. In 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025. Institute of Electrical and Electronics Engineers Inc. 2025. (2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025). doi: 10.1109/ICETECC65365.2025.11070289

Khurshid, Kiran ; Saeed, Nasir ; Bibi, Nazia et al. / Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications. 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025. Institute of Electrical and Electronics Engineers Inc., 2025. (2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025).

@inproceedings{f413d8b156544d9c8bca4154e51dcf90,

title = "Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications",

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

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

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025 ; Conference date: 23-04-2025 Through 25-04-2025",

year = "2025",

doi = "10.1109/ICETECC65365.2025.11070289",

language = "English",

series = "2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025",

}

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

Y1 - 2025

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.

KW - Channel Estimation Error

KW - IoT

KW - Least Squares

KW - Massive MIMO

KW - Minimum Mean Square Error

UR - https://www.scopus.com/pages/publications/105012121830

UR - https://www.scopus.com/pages/publications/105012121830#tab=citedBy

U2 - 10.1109/ICETECC65365.2025.11070289

DO - 10.1109/ICETECC65365.2025.11070289

M3 - Conference contribution

AN - SCOPUS:105012121830

T3 - 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025

BT - 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025

Y2 - 23 April 2025 through 25 April 2025

ER -

Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this