Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
This paper proposes a tree-based regression model for hybrid channel estimation in wireless sensor networks (WSNs) in generalized frequency division multiplexing (GFDM) over both visible light communication (VLC) and radio frequency (RF) links. The hybrid channel incorporates both additive white Gau...
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MDPI AG
2025-06-01
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| author | Azam Isam Aladwani Tarik Adnan Almohamad Abdullah Talha Sözer İsmail Rakıp Karaş |
| author_facet | Azam Isam Aladwani Tarik Adnan Almohamad Abdullah Talha Sözer İsmail Rakıp Karaş |
| author_sort | Azam Isam Aladwani |
| collection | DOAJ |
| description | This paper proposes a tree-based regression model for hybrid channel estimation in wireless sensor networks (WSNs) in generalized frequency division multiplexing (GFDM) over both visible light communication (VLC) and radio frequency (RF) links. The hybrid channel incorporates both additive white Gaussian noise (AWGN) and Rayleigh fading to mimic realistic environments. Traditional estimators, such as MMSE and LMMSE, often underperform in such heterogeneous and nonlinear conditions due to their analytical rigidity. To overcome these limitations, we introduce a data-driven approach using a decision tree regressor trained on 18,000 signal samples across 36 SNR levels. Simulation results show that support vector machine (SVM) achieved 91.34% accuracy and a BER of 0.0866 at 10 dB, as well as 96.77% accuracy with a BER of 0.0323 at 30 dB. Random forest achieved 91.01% accuracy and a BER of 0.0899 at 10 dB, as well as 97.88% accuracy with a BER of 0.0212 at 30 dB. The proposed tree model attained 90.83% and 97.63% accuracy with BERs of 0.0917 and 0.0237, respectively, at the corresponding SNR values. The distinguishing advantage of the tree model lies in its inference efficiency. It completes predictions on the test dataset in just 45.53 s, making it over three times faster than random forest (140.09 s) and more than four times faster than SVM (189.35 s). This significant reduction in inference time makes the proposed tree model particularly well suited for real-time and resource-constrained WSN scenarios, where fast and efficient estimation is often more critical than marginal gains in accuracy. The results also highlight a trade-off, where the tree model provides sub-optimal predictive performance while significantly reducing computational overhead, making it an attractive choice for low-power and latency-sensitive wireless systems. |
| format | Article |
| id | doaj-art-41bcae5d33e54bc2b696bccf1f6edc8b |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-41bcae5d33e54bc2b696bccf1f6edc8b2025-08-20T03:28:58ZengMDPI AGSensors1424-82202025-06-012513390610.3390/s25133906Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based RegressorAzam Isam Aladwani0Tarik Adnan Almohamad1Abdullah Talha Sözer2İsmail Rakıp Karaş3Electrical and Electronics Engineering Department, Faculty of Engineering, Karabuk University, Karabuk 78050, TürkiyeElectrical and Electronics Engineering Department, Faculty of Engineering, Karabuk University, Karabuk 78050, TürkiyeElectrical and Electronics Engineering Department, Faculty of Engineering, Karabuk University, Karabuk 78050, TürkiyeComputer Engineering Department, Faculty of Engineering, Karabuk University, Karabuk 78050, TürkiyeThis paper proposes a tree-based regression model for hybrid channel estimation in wireless sensor networks (WSNs) in generalized frequency division multiplexing (GFDM) over both visible light communication (VLC) and radio frequency (RF) links. The hybrid channel incorporates both additive white Gaussian noise (AWGN) and Rayleigh fading to mimic realistic environments. Traditional estimators, such as MMSE and LMMSE, often underperform in such heterogeneous and nonlinear conditions due to their analytical rigidity. To overcome these limitations, we introduce a data-driven approach using a decision tree regressor trained on 18,000 signal samples across 36 SNR levels. Simulation results show that support vector machine (SVM) achieved 91.34% accuracy and a BER of 0.0866 at 10 dB, as well as 96.77% accuracy with a BER of 0.0323 at 30 dB. Random forest achieved 91.01% accuracy and a BER of 0.0899 at 10 dB, as well as 97.88% accuracy with a BER of 0.0212 at 30 dB. The proposed tree model attained 90.83% and 97.63% accuracy with BERs of 0.0917 and 0.0237, respectively, at the corresponding SNR values. The distinguishing advantage of the tree model lies in its inference efficiency. It completes predictions on the test dataset in just 45.53 s, making it over three times faster than random forest (140.09 s) and more than four times faster than SVM (189.35 s). This significant reduction in inference time makes the proposed tree model particularly well suited for real-time and resource-constrained WSN scenarios, where fast and efficient estimation is often more critical than marginal gains in accuracy. The results also highlight a trade-off, where the tree model provides sub-optimal predictive performance while significantly reducing computational overhead, making it an attractive choice for low-power and latency-sensitive wireless systems.https://www.mdpi.com/1424-8220/25/13/3906wireless sensor networks (WSNs)generalized frequency division multiplexing (GFDM)tree-based machine learningchannel estimationvisible light communication (VLC)radio frequency (RF) |
| spellingShingle | Azam Isam Aladwani Tarik Adnan Almohamad Abdullah Talha Sözer İsmail Rakıp Karaş Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor Sensors wireless sensor networks (WSNs) generalized frequency division multiplexing (GFDM) tree-based machine learning channel estimation visible light communication (VLC) radio frequency (RF) |
| title | Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor |
| title_full | Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor |
| title_fullStr | Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor |
| title_full_unstemmed | Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor |
| title_short | Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor |
| title_sort | hybrid vlc rf channel estimation for gfdm wireless sensor networks using tree based regressor |
| topic | wireless sensor networks (WSNs) generalized frequency division multiplexing (GFDM) tree-based machine learning channel estimation visible light communication (VLC) radio frequency (RF) |
| url | https://www.mdpi.com/1424-8220/25/13/3906 |
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