Channel Estimation on MIMO F-OFDM Using Deep Learning Algorithm

Abstract

This paper outlines the Analysis of techniques for estimating channels in MIMO-OFDM systems, which combine Multiple Input Multiple Output (MIMO) with Orthogonal Frequency Division Multiplexing(OFDM)to enhance spectral efficiency and robustness in modern telecommunication systems

Introduction

I. INTRODUCTION

The rapid evolution of wireless communication systems has driven the demand for higher data rates, better reliability, and more efficient use of the radio spectrum. In this context, technologies like Multiple Input Multiple-Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) have emerged as fundamental solutions to these challenges. The combination of these two technologies, known as MIMO-OFDM, is at the heart of modern wireless standards such as LTE, 5G, Wi-Fi, and WiMAX. MIMO-OFDM systems are recognized for their ability to provide both robustness against multipath interference and significant improvements in spectral efficiency, making them indispensable for high speed wireless communication.

II. METHODOLOGY

A. Design Frame Work

The Best of Both Worlds By combining MIMO and OFDM, MIMO-OFDM systems bring together the benefits of both spatial diversity and frequency diversity. The MIMO aspect of the system allows for simultaneous transmission of multiple data streams,which increases the capacity of the system, while the OFDM aspect mitigates the effects of frequency-selective fading and simplifies the equalization process at the receiver.

B. Feedback Mechanism

(F-OFDM) is used to improve the performance of traditional OFDM by reducing out-of-band emissions and enhancing spectral efficiency. It applies filters to each sub-band, which minimizes adjacent channel interference, allowing for better coexistence of different services or users in neighboring frequency bands. F-OFDM also enables flexible spectrum usage, making it ideal for dynamic spectrum allocation in systems like 5G.

C. Data Processing and Analysis

Deep Learning

Better Accuracy: It handles complex channel effects like fading and interference more effectively than traditional methods.

Adaptability: Deep learning can adapt to changing environments, such as different noise levels and mobility patterns.

Handling Time-Varying Channels: Techniques like Recurrent Neural Networks (RNNs) track rapid changes in high-mobility scenarios.

D. Software Implementation

The BPSK, QPSK and QAM modulation techniques are analyzed with MATLAB. Further, the QAM modulation technique is simulated by using Python Programming using Background and Related Work

There search titled "Channel Estimation on MIMO- OFDM Systems" was authored by André Antônio dos Anjos, Ricardo Antonio Dias, and Luciano Leonel Mendes from the Instituto Nacional de telecommunicators in Brazil

The research paper titled "Channel Estimation for MIMO-OFDM Systems" was authored by Shahid Manzoor, Adnan Salem Bamuhaisoon, and Ahmed Nor Alifa, and published in 2015

The paper titled "Implementation of Channel Estimation for MIMO-OFDM Systems" was written by Chih-Hung Lin, Robert Chen-Hao Chang,Kuang-Hao Lin, and Yang-Yu Lin, and published in 2010.

In this project, a Deep Neural Network (DNN) is utilized for channel estimation in the MIMO F- OFDM system, leveraging its ability to learn complex, non-linear relationships between transmitted and received signals. The DNN is trained using datasets of pilot symbols and received signals, enabling it to accurately estimate Channel State Information (CSI). Its architecture includes multiple layers to extract intricate features, providing higher accuracy than traditional methods like LS and MMSE. The DNN’s robustness to noise, adaptability to dynamic environments, and scalability to large antenna systems make it ideal for modern wireless networks. Additionally, its real-time inference capability ensures efficiency, aligning with the demands of next-generation networks like 5G and6G.

The use of a Deep Neural Network (DNN) in this project ensures accurate channel estimation by learning complex non-linear relationships between transmitted and received signals. DNNs outperform traditional methods like LS and MMSE, offering robustness to noise and adaptability to dynamic environments. Their scalability to large antenna system and real-time efficiency make them ideal for next-generation networks like 5G and 6G.

Transmitter Side

Receiver Side

Fig.1.Blockdiagram.

Conclusion

This project successfully explores the integration of deep learning techniques into channel estimation for MIMO F-OFDM systems, addressing the critical challenge of accurate channel state information (CSI) acquisition in modern wireless communication networks. The combination of MIMO and F-OFDM provides enhanced spectral efficiency and robustness against interference, making it a cornerstone fornext- generation wireless standards like 5G and beyond. By employing deep learning algorithms, particularly techniques like DNN , this work demonstrates significant improvements in channel estimation accuracy under varying channel conditions. The MATLAB and Python-based simulations confirm the effectiveness of the proposed methodology, highlighting its capability to minimize the Bit Error Rate (BER) and ensure reliable communication in dynamic environments. Comparative analysis with traditional estimation methods such as LS, ML, and MMSE further underscores the advantages of deep learning in handling complex channel effects.

References

[1] The research paper titled \"Channel Estimation for MIMO-OFDM Systems\" was authored by Shahid Manzoor, Adnan Salem Bamuhaisoon, and Ahmed Nor Alifa, and published in 2015. [2] The paper titled \"Implementation of Channel Estimation for MIMO-OFDM Systems\" was written by Chih-Hung Lin, Robert Chen-Hao Chang,Kuang-Hao Lin, and Yang-Yu Lin, and published in 2010. [3] The paper titled \"Efficient Channel Estimation of MIMO-OFDM System using Pilot Tones\"waswritten by Owais Baig, Muhammad Kamran Asif,and Mohammed SalmanBaig,andpublishedin 2015. [4] The research paper titled \"Deep Learning Based Channel Estimation for MIMO-OFDM System with Modified ResNet Model\" was authored by C. Silpa, A. Vani, and K. Rama Naidu, and was published in 2023 in the Indian Journal of Science and Technology. [5] The research titled \"Channel Estimation on MIMO-OFDM Systems\" was authored by André Antônio dos Anjos, Ricardo Antonio Dias, and Luciano Leonel Mendes from the Instituto Nacional de telecommunicators in Brazil. [6] The research paper titled \"Channel Estimation for MIMO-OFDM Systems\" was authored by D.B. Bhoyar and Vaishali B. Niranjane, and published in 2012. [7] The research paper titled \"Channel Estimation Techniques in MIMO-OFDM LTE Systems\" was authored by P. Venkateswarlu and R. Nagendra. It was published in July 2014 in the International Journal of Engineering Research and Applications. [8] The research paper is titled \"3D MIMO-OFDM Channel Estimation,\" authored by Gunther Auer, a member of IEEE, and was published in the IEEE TransactionsonCommunications,Volume60,Issue 4, in April 2012. [9] Theresearchtitled\"A Survey onMIMO-OFDM Systems: Review of Recent Trends\" was authored by Houda Harkat, Paulo Monteiro, Atilio Gameiro, Fernando Guiomar, and Hasmath Farhana Thariq Ahmed.Itwaspublishedin2022inthejournal Signals. [10] Qiwei Zheng, Fanggang Wang, Xia Chen, Yinsheng Liu, Deshan Miao, and Zhuyan Zhao, \"Comparison of 5G Waveform Candidates in High- Speed Scenario,\" in 32nd URSI General Assembly and Scientific Symposium (GASS), Montreal, Canada, Aug. 2017, pp. 1-5. [11] S. Sarker, L. A. Ara, T. Alam, and T. Debnath, \"Design and Analysis of MIMO F-OFDM Systems for 5G and Beyond Wireless Communications,\" International Journal of Recent Technology and Engineering (IJRTE), vol. 10, no. 2, pp. 203–210, July2021,doi:10.35940/ijrte.B6274.0710221. [12] A.J.Ramadhan,\"OverviewandComparisonof Candidate 5G Waveforms: FBMC, UFMC, and F- OFDM,\" International Journal of Computer Network and Information Security (IJCNIS), vol. 14,no. 2

Copyright

Copyright © 2025 Abdullah Furqaan Ahmed, Charan. H, Gandla Venga Naveen, Hruthik. U, Mrs. Manasa S, Dr. Swetha Rani L. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.