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Spatially multiplexed OFDM – RoFSO transmission under different atmospheric conditions

  • Ankur Sood and Rahul Kaushik EMAIL logo
Published/Copyright: March 18, 2025
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Abstract

In this paper, a spatially multiplexed, OFDM-based radio over free space optical (RoFSO) communication system is proposed to enhance the data rate. The input data streams 1–4 are combined using space division multiplexing (SDM) to achieve 80 Gbps (4 × 20 Gbps) transmission rate. At SDM level, an optical carrier of 193.1 THz frequency is selected and various Laguerre Gaussian (LG) modes LG00, LG01, LG02, and LG03 are assigned to individual data streams 1–4. The RoFSO link is analyzed for different atmospheric attenuation and turbulence conditions while measuring system bit error rate (BER), Q-factor, received signal power, signal to noise ratio (SNR), etc. A comparative analysis for back to back (B2B), subcarrier multiplexed (SCM-MUX), wavelength division multiplexed (WDM-MUX), and space division multiplexed (SDM-MUX) configuration is carried out. In FSO environment, the SDM-MUX configuration is further analyzed for various haze, rain, fog, and dust conditions. Analysis shows that the extended link range is observed under low attenuation conditions. The impact of atmospheric turbulence under light rain and heavy rain conditions is also analyzed. The overall analysis estimates higher transmission rate for SDM-MUX configuration. Also, channel capacity can be enhanced by introducing additional optical modes for the selected design configuration.


Corresponding author: Rahul Kaushik, Electronics and Communication Engineering Department, Jaypee Institute of Information Technology, Noida, 201309, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Ankur Sood has developed the design concept, formulated, and performed simulation, and measurements are verified for design feasibility. Rahul Kaushik supervised the work reported in the manuscript. Both Ankur Sood and Rahul Kaushik have accepted responsibility for the entire content of this manuscript and approved the submission of the final version of the manuscript.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: Authors declare no conflicts of interest.

  6. Research funding: Authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

  7. Data availability: Not applicable.

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Received: 2025-01-01
Accepted: 2025-02-24
Published Online: 2025-03-18

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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