Turbulence-resilient adaptive modulation and diversity coding for DWDM-based hybrid MIMO-RF/FSO systems
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Ebrahim E. Elsayed
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Mohamed A. Yakout
Abstract
This research introduces an enhanced transmission framework for hybrid multiple-input multiple-output (MIMO) and dense wavelength-division multiplexing (DWDM) radio frequency/free-space optical (RF/FSO) communication systems, utilizing diversity coding techniques to combat turbulence-induced signal degradation. The proposed system achieves an aggregate data rate of 20 gigabits per second (Gbps) through eight channels operating at 2.5 Gbps each over a 1,500-meter (m) link, with comprehensive performance evaluation of bit error rate (BER), outage probability (OP), and signal-to-noise ratio (SNR) across three primary diversity combining methods: maximum ratio combining (MRC), selection combining (SC), and equal-gain combining (EGC). Analysis confirms the superior performance of Alamouti coding (AC), space-time coding (STC), space-time block coding (STBC), space-time trellis coding (STTC), orthogonal STBC (O-STBC), and quasi-orthogonal STBC (QO-STBC) in hybrid DWDM-MIMO-RF/FSO networks, particularly in reducing mean-square error while improving turbulence resilience. Simulation results demonstrate that QO-STBC and STTC implementations combined with MRC or SC significantly enhance BER, SNR, and outage characteristics in MIMO-DWDM FSO systems, with MRC showing better performance than both SC and EGC in minimizing BER and OP. Furthermore, numerical analysis reveals that DWDM-augmented QO-STBC/STTC in RF/FSO links reduces power penalties across BER thresholds under diverse turbulence conditions (weak, moderate, and strong), surpassing non-DWDM FSO configurations.
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Research ethics: No ethical approval was required for this study.
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Informed consent: Not applicable.
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Author contributions: Each co-author contributed equally to the conceptualization, drafting, and revision of this paper. All participants thoroughly reviewed the final manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: None declared. No artificial intelligence or large language models were employed in this work.
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Conflict of interest: The authors declare no competing interests.
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Research funding: This research received no external funding.
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Data availability: All findings are comprehensively presented within the manuscript.
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Patient consent statement: Not applicable.
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Permission to reproduce material from other sources: Not applicable.
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Clinical trial registration: Not applicable.
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