OFDM Variants and FPGA Implementation: 
A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization

Fatai Ahmed-Ade; Vincent Andrew Akpan; Emmanuel Omonigho Ogolo

doi:doi:10.11648/j.ajesa.20251101.13

Research Article |

| Peer-Reviewed

OFDM Variants and FPGA Implementation: A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization

Fatai Ahmed-Ade

, Vincent Andrew Akpan^*

, Emmanuel Omonigho Ogolo

Published in American Journal of Embedded Systems and Applications (Volume 11, Issue 1)

Received: 1 October 2025 Accepted: 16 October 2025 Published: 9 December 2025

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Abstract

This paper presents a comprehensive comparative analysis of Orthogonal Frequency Division Multiplexing (OFDM) variants and their practical implementations across multiple domains to guide future communication system design decisions. This paper investigates algorithm comparison and methodologies for OFDM variants, explore optical wireless communication integration, examine neural network-based OFDM mapping techniques, and demonstrate field-programmable gate array (FPGA) realizations. The primary objectives of this research are threefold, namely: 1). to establish comprehensive performance benchmarks for major OFDM variants across key metrics including spectral efficiency, computational complexity, and implementation feasibility; 2). to investigate the synergistic benefits of integrating OFDM with optical wireless communication systems; and 3). to evaluate the effectiveness of neural network-based signal processing in OFDM applications while demonstrating practical FPGA realizations. Through systematic comparison of conventional OFDM, Filtered-OFDM (F-OFDM), Universal Filtered Multi-Carrier (UFMC), and Generalized Frequency Division Multiplexing (GFDM), we establish performance benchmarks across spectral efficiency, power consumption, and computational complexity metrics. The optical wireless integration study reveals significant improvements in data transmission rates and energy efficiency. Neural network mapping demonstrates enhanced channel estimation and equalization capabilities, while FPGA implementations provide real-time processing solutions with optimized resource utilization. Experimental results show up to 25% improvement in spectral efficiency and 40% reduction in computational complexity compared to traditional implementations. The findings contribute to the advancement of next-generation wireless communication systems and provide practical implementation guidelines for researchers and engineers.

Published in	American Journal of Embedded Systems and Applications (Volume 11, Issue 1)
DOI	10.11648/j.ajesa.20251101.13
Page(s)	16-38
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Channel Estimation, Convolutional Neural Network (CNN), Filtered-OFDM, UFMC, GFDM, Field Programmable Gate Array (FPGA), Optical Wireless Communication (OWC)

References

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Ahmed-Ade, F., Akpan, V. A., Ogolo, E. O. (2025). OFDM Variants and FPGA Implementation: A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization. American Journal of Embedded Systems and Applications, 11(1), 16-38. https://doi.org/10.11648/j.ajesa.20251101.13

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Ahmed-Ade, F.; Akpan, V. A.; Ogolo, E. O. OFDM Variants and FPGA Implementation: A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization. Am. J. Embed. Syst. Appl. 2025, 11(1), 16-38. doi: 10.11648/j.ajesa.20251101.13

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AMA Style

Ahmed-Ade F, Akpan VA, Ogolo EO. OFDM Variants and FPGA Implementation: A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization. Am J Embed Syst Appl. 2025;11(1):16-38. doi: 10.11648/j.ajesa.20251101.13

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@article{10.11648/j.ajesa.20251101.13,
  author = {Fatai Ahmed-Ade and Vincent Andrew Akpan and Emmanuel Omonigho Ogolo},
  title = {OFDM Variants and FPGA Implementation: 
A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {11},
  number = {1},
  pages = {16-38},
  doi = {10.11648/j.ajesa.20251101.13},
  url = {https://doi.org/10.11648/j.ajesa.20251101.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20251101.13},
  abstract = {This paper presents a comprehensive comparative analysis of Orthogonal Frequency Division Multiplexing (OFDM) variants and their practical implementations across multiple domains to guide future communication system design decisions. This paper investigates algorithm comparison and methodologies for OFDM variants, explore optical wireless communication integration, examine neural network-based OFDM mapping techniques, and demonstrate field-programmable gate array (FPGA) realizations. The primary objectives of this research are threefold, namely: 1). to establish comprehensive performance benchmarks for major OFDM variants across key metrics including spectral efficiency, computational complexity, and implementation feasibility; 2). to investigate the synergistic benefits of integrating OFDM with optical wireless communication systems; and 3). to evaluate the effectiveness of neural network-based signal processing in OFDM applications while demonstrating practical FPGA realizations. Through systematic comparison of conventional OFDM, Filtered-OFDM (F-OFDM), Universal Filtered Multi-Carrier (UFMC), and Generalized Frequency Division Multiplexing (GFDM), we establish performance benchmarks across spectral efficiency, power consumption, and computational complexity metrics. The optical wireless integration study reveals significant improvements in data transmission rates and energy efficiency. Neural network mapping demonstrates enhanced channel estimation and equalization capabilities, while FPGA implementations provide real-time processing solutions with optimized resource utilization. Experimental results show up to 25% improvement in spectral efficiency and 40% reduction in computational complexity compared to traditional implementations. The findings contribute to the advancement of next-generation wireless communication systems and provide practical implementation guidelines for researchers and engineers.},
 year = {2025}
}

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A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization
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DO  - 10.11648/j.ajesa.20251101.13
T2  - American Journal of Embedded Systems and Applications
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JO  - American Journal of Embedded Systems and Applications
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AB  - This paper presents a comprehensive comparative analysis of Orthogonal Frequency Division Multiplexing (OFDM) variants and their practical implementations across multiple domains to guide future communication system design decisions. This paper investigates algorithm comparison and methodologies for OFDM variants, explore optical wireless communication integration, examine neural network-based OFDM mapping techniques, and demonstrate field-programmable gate array (FPGA) realizations. The primary objectives of this research are threefold, namely: 1). to establish comprehensive performance benchmarks for major OFDM variants across key metrics including spectral efficiency, computational complexity, and implementation feasibility; 2). to investigate the synergistic benefits of integrating OFDM with optical wireless communication systems; and 3). to evaluate the effectiveness of neural network-based signal processing in OFDM applications while demonstrating practical FPGA realizations. Through systematic comparison of conventional OFDM, Filtered-OFDM (F-OFDM), Universal Filtered Multi-Carrier (UFMC), and Generalized Frequency Division Multiplexing (GFDM), we establish performance benchmarks across spectral efficiency, power consumption, and computational complexity metrics. The optical wireless integration study reveals significant improvements in data transmission rates and energy efficiency. Neural network mapping demonstrates enhanced channel estimation and equalization capabilities, while FPGA implementations provide real-time processing solutions with optimized resource utilization. Experimental results show up to 25% improvement in spectral efficiency and 40% reduction in computational complexity compared to traditional implementations. The findings contribute to the advancement of next-generation wireless communication systems and provide practical implementation guidelines for researchers and engineers.
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Fatai Ahmed-Ade

Department of Physics, Federal University Lokoja, Lokoja, Nigeria

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http://orcid.org/0009-0003-4123-7338
Vincent Andrew Akpan

Department of Biomedical Engineering, The Federal University of Technology, Akure, Nigeria

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http://orcid.org/0000-0002-5155-2863
Emmanuel Omonigho Ogolo

Department of Physics Electronics, The Federal University of Technology, Akure, Nigeria

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http://orcid.org/0000-0002-9444-298X

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Ahmed-Ade, F., Akpan, V. A., Ogolo, E. O. (2025). OFDM Variants and FPGA Implementation: A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization. American Journal of Embedded Systems and Applications, 11(1), 16-38. https://doi.org/10.11648/j.ajesa.20251101.13

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Ahmed-Ade, F.; Akpan, V. A.; Ogolo, E. O. OFDM Variants and FPGA Implementation: A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization. Am. J. Embed. Syst. Appl. 2025, 11(1), 16-38. doi: 10.11648/j.ajesa.20251101.13

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Ahmed-Ade F, Akpan VA, Ogolo EO. OFDM Variants and FPGA Implementation: A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization. Am J Embed Syst Appl. 2025;11(1):16-38. doi: 10.11648/j.ajesa.20251101.13

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@article{10.11648/j.ajesa.20251101.13,
  author = {Fatai Ahmed-Ade and Vincent Andrew Akpan and Emmanuel Omonigho Ogolo},
  title = {OFDM Variants and FPGA Implementation: 
A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {11},
  number = {1},
  pages = {16-38},
  doi = {10.11648/j.ajesa.20251101.13},
  url = {https://doi.org/10.11648/j.ajesa.20251101.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20251101.13},
  abstract = {This paper presents a comprehensive comparative analysis of Orthogonal Frequency Division Multiplexing (OFDM) variants and their practical implementations across multiple domains to guide future communication system design decisions. This paper investigates algorithm comparison and methodologies for OFDM variants, explore optical wireless communication integration, examine neural network-based OFDM mapping techniques, and demonstrate field-programmable gate array (FPGA) realizations. The primary objectives of this research are threefold, namely: 1). to establish comprehensive performance benchmarks for major OFDM variants across key metrics including spectral efficiency, computational complexity, and implementation feasibility; 2). to investigate the synergistic benefits of integrating OFDM with optical wireless communication systems; and 3). to evaluate the effectiveness of neural network-based signal processing in OFDM applications while demonstrating practical FPGA realizations. Through systematic comparison of conventional OFDM, Filtered-OFDM (F-OFDM), Universal Filtered Multi-Carrier (UFMC), and Generalized Frequency Division Multiplexing (GFDM), we establish performance benchmarks across spectral efficiency, power consumption, and computational complexity metrics. The optical wireless integration study reveals significant improvements in data transmission rates and energy efficiency. Neural network mapping demonstrates enhanced channel estimation and equalization capabilities, while FPGA implementations provide real-time processing solutions with optimized resource utilization. Experimental results show up to 25% improvement in spectral efficiency and 40% reduction in computational complexity compared to traditional implementations. The findings contribute to the advancement of next-generation wireless communication systems and provide practical implementation guidelines for researchers and engineers.},
 year = {2025}
}

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TY  - JOUR
T1  - OFDM Variants and FPGA Implementation: 
A Comprehensive Analysis of Algorithm Comparison, Optical Wireless Integration, Neural Network Mapping, and FPGA Realization
AU  - Fatai Ahmed-Ade
AU  - Vincent Andrew Akpan
AU  - Emmanuel Omonigho Ogolo
Y1  - 2025/12/09
PY  - 2025
N1  - https://doi.org/10.11648/j.ajesa.20251101.13
DO  - 10.11648/j.ajesa.20251101.13
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 16
EP  - 38
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20251101.13
AB  - This paper presents a comprehensive comparative analysis of Orthogonal Frequency Division Multiplexing (OFDM) variants and their practical implementations across multiple domains to guide future communication system design decisions. This paper investigates algorithm comparison and methodologies for OFDM variants, explore optical wireless communication integration, examine neural network-based OFDM mapping techniques, and demonstrate field-programmable gate array (FPGA) realizations. The primary objectives of this research are threefold, namely: 1). to establish comprehensive performance benchmarks for major OFDM variants across key metrics including spectral efficiency, computational complexity, and implementation feasibility; 2). to investigate the synergistic benefits of integrating OFDM with optical wireless communication systems; and 3). to evaluate the effectiveness of neural network-based signal processing in OFDM applications while demonstrating practical FPGA realizations. Through systematic comparison of conventional OFDM, Filtered-OFDM (F-OFDM), Universal Filtered Multi-Carrier (UFMC), and Generalized Frequency Division Multiplexing (GFDM), we establish performance benchmarks across spectral efficiency, power consumption, and computational complexity metrics. The optical wireless integration study reveals significant improvements in data transmission rates and energy efficiency. Neural network mapping demonstrates enhanced channel estimation and equalization capabilities, while FPGA implementations provide real-time processing solutions with optimized resource utilization. Experimental results show up to 25% improvement in spectral efficiency and 40% reduction in computational complexity compared to traditional implementations. The findings contribute to the advancement of next-generation wireless communication systems and provide practical implementation guidelines for researchers and engineers.
VL  - 11
IS  - 1
ER  -

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