Evaluation of Joint Technique Iterative Clipping Filtering (ICF) and Neural Network Predistortion on SDR-based MIMO-OFDM System

Melki Gulo - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
I Astawa - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Amang Sudarsono - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Yoedy Moegiharto - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Naufal Priambodo - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
M. Gunawan - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia

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DOI: http://dx.doi.org/10.62527/joiv.8.2.1981


Multiple-input, multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a communications technology that powers numerous modern communication systems, including 5G and WiFi-6. This technology is utilized in current communication systems due to its high performance and extensive channel capacity. MIMO-OFDM does have disadvantages, such as large Peak-to-Average Power Ratio (PAPR) values. If the signal is processed by a nonlinear Power Amplifier (PA) device, a high PAPR value signal can result in both in-band and out-of-band signal distortion. To combat high PAPR values, PAPR reduction strategies such as Iterative Clipping Filtering (ICF) are utilized. From this study, using ICF with iteration 2 and Clipping Ratios (CR) 3 and 4 can improve the system's minimum Bit Error Rate (BER) by about 22.8% and 91.1%, respectively. Choosing the correct CR will improve the system, but using the lower CR will make it worse than a system without ICF. This occurs in systems using ICF with iterations two and CR 2 and at the same SNR conditions as systems without ICF; using ICF with iterations two and CR 2 results in higher BER values. The use of Predistortion Neural Network (PDNN) can overcome this problem. By using PDNN, there is an improvement in the system where the minimum BER value can reach 0.1 × 10-5. The percentage decrease in BER from using PDNN for ICF with iterations two and CR 2, 3, and 4 is 99.88%, 99.86%, and 98.807%, respectively. Thus, the joint techniques of ICF and PDNN can significantly enhance the performance of MIMO-OFDM systems with nonlinear PA. Importantly, the experiment was conducted on an SDR device, ensuring the real-world applicability of the results.


ICF;Predistortion;Neural Networks;MIMO-OFDM;SDR

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S. R. Pokhrel, H. L. Vu, and A. L. Cricenti, “Adaptive Admission Control for IoT Applications in Home WiFi Networks,” IEEE Trans. on Mobile Comput., vol. 19, no. 12, pp. 2731–2742, Dec. 2020, doi: 10.1109/TMC.2019.2935719.

S. Li, L. D. Xu, and S. Zhao, “5G Internet of Things: A survey,” Journal of Industrial Information Integration, vol. 10, pp. 1–9, Jun. 2018, doi: 10.1016/j.jii.2018.01.005.

P. T and S. S. Nayak, “5G Technology for E-Health,” in 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India: IEEE, Oct. 2020, pp. 211–216. doi: 10.1109/I-SMAC49090.2020.9243403.

E. Selem, M. Fatehy, and S. M. A. El-Kader, “E-Health applications over 5G networks: challenges and state of the art,” in 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT), Hurgada, Egypt: IEEE, Nov. 2019, pp. 111–118. doi: 10.1109/ACCS-PEIT48329.2019.9062841.

L. Guevara and F. Auat Cheein, “The Role of 5G Technologies: Challenges in Smart Cities and Intelligent Transportation Systems,” Sustainability, vol. 12, no. 16, p. 6469, Aug. 2020, doi: 10.3390/su12166469.

A. Gohar and G. Nencioni, “The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System,” Sustainability, vol. 13, no. 9, p. 5188, May 2021, doi: 10.3390/su13095188.

T. Ahmed B., M. S. Krishnan, and A. K. Anil, “A Predictive Analysis on the Influence of WiFi 6 in Fog Computing with OFDMA and MU-MIMO,” in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India: IEEE, Mar. 2020, pp. 716–719. doi: 10.1109/ICCMC48092.2020.ICCMC-000133.

F. Jiang, Q. Li, and X. Chen, “Channel Smoothing for 802.11ax Beamformed MIMO-OFDM,” IEEE Commun. Lett., vol. 25, no. 10, pp. 3413–3417, Oct. 2021, doi: 10.1109/LCOMM.2021.3099167.

Y. A. Jawhar et al., “New low-complexity segmentation scheme for the partial transmit sequence technique for reducing the high PAPR value in OFDM systems,” ETRI Journal, vol. 40, no. 6, pp. 699–713, Dec. 2018, doi: 10.4218/etrij.2018-0070.

H. Bao, J. Fang, Q. Wan, Z. Chen, and T. Jiang, “An ADMM Approach for PAPR Reduction for Large-Scale MIMO-OFDM Systems,” IEEE Trans. Veh. Technol., vol. 67, no. 8, pp. 7407–7418, Aug. 2018, doi: 10.1109/TVT.2018.2837112.

A. Singal and D. Kedia, “Performance Analysis of MIMO-OFDM System Using SLM with Additive Mapping and U2 Phase Sequence for PAPR Reduction,” Wireless Pers Commun, vol. 111, no. 3, pp. 1377–1390, Apr. 2020, doi: 10.1007/s11277-019-06921-x.

L. Amhaimar, S. Ahyoud, A. Elyaakoubi, A. Kaabal, K. Attari, and A. Asselman, “PAPR Reduction Using Fireworks Search Optimization Algorithm in MIMO-OFDM Systems,” Journal of Electrical and Computer Engineering, vol. 2018, pp. 1–11, Sep. 2018, doi: 10.1155/2018/3075890.

T. Udomsripaiboon, “Adjustable dynamic range for PAPR clipping technique in large-scale MIMO-OFDM systems,” in 2018 International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON), Chiang Rai: IEEE, Feb. 2018, pp. 25–28. doi: 10.1109/ECTI-NCON.2018.8378275.

B. D. Timande and M. K. Nigam, “PAPR Reduction an effective approach for next frontier MIMO-OFDM systems,” JER is an international, peer-reviewed journal that publishes full-length original research papers, reviews, case studies in all areas of Engineering., vol. 9, Oct. 2021, doi: 10.36909/jer.11379.

Y. Moegiharto, A. M. Kautsar Bebyrahma, and I. Anisah, “BER Performance of Joint PTS PAPR Reduction Technique and Wiener HPA Predistortion in OFDM System,” in 2019 International Electronics Symposium (IES), Surabaya, Indonesia: IEEE, Sep. 2019, pp. 480–484. doi: 10.1109/ELECSYM.2019.8901566.

M. D. N. Habibah, G. S. Palupi, A. A. Puspitasari, U. A. Nadhiroh, M. Ridwan, and Y. Moegiharto, “Performance of a Joint PAPR Reduction Clipping and Filtering (CF) Scheme and Predistortion Techniques in Amplify and Forward (AF) Relaying System with Relay Selection Strategy,” in 2021 International Electronics Symposium (IES), Surabaya, Indonesia: IEEE, Sep. 2021, pp. 120–125. doi: 10.1109/IES53407.2021.9594005.

A. Syarif, Arifin, N. Sa’adah, I. G. P. Astawa, and Y. Moegiharto, “Performance of Joint PAPR Reduction Iterative Clipping and Filtering (ICF) and Predistortion in OFDM Systems Using Software Defined Radio,” in 2021 International Electronics Symposium (IES), Surabaya, Indonesia: IEEE, Sep. 2021, pp. 92–96. doi: 10.1109/IES53407.2021.9593971.

M. W. Gunawan, N. A. Priambodo, M. M. Gulo, A. Arifin, Y. Moegiharto, and H. Briantoro, “Evaluations of the predistortion technique by neural network algorithm in MIMO-OFDM system using USRP,” J.INFOTEL, vol. 14, no. 4, Nov. 2022, doi: 10.20895/infotel.v14i4.825.

M. M. Gulo, I. G. P. Astawa, and A. Sudarsono, “Performance Analysis of MIMO-OFDM System Using Predistortion Neural Network with Convolutional Coding Addition to Reduce SDR-Based HPA Nonlinearity,” EMITTER International Journal of Engineering Technology, vol. 11, no. 1, pp. 35–59, Jun. 2023, doi: https://doi.org/10.24003/emitter.v11i1.791.

M. M. Gulo, I. G. P. Astawa, A. Sudarsono, N. A. Priambodo, and M. W. Gunawan, “Implementation of Tiny Machine Learning (TinyML) as Pre-distorter for High Power Amplifier (HPA)Linearization of SDR-based MIMO-OFDM,” in 2023 International Electronics Symposium (IES), Denpasar, Indonesia: IEEE, Aug. 2023, pp. 204–210. doi: 10.1109/IES59143.2023.10242459.

M. H. Lee, M. B. Shahab, M. F. Kader, and S. Y. Shin, “Spatial multiplexing using walsh-hadamard transform,” in 2016 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS), Denpasar, Indonesia: IEEE, Oct. 2016, pp. 43–46. doi: 10.1109/ICSGTEIS.2016.7885764.

L. Kansal, V. Sharma, and J. Singh, “Multiuser Massive MIMO-OFDM System Incorporated with Diverse Transformation for 5G Applications,” Wireless Pers Commun, vol. 109, no. 4, pp. 2741–2756, Dec. 2019, doi: 10.1007/s11277-019-06707-1.

Y. Tian and M. E. Magaña, “Pilot-Aided Channel Estimation for Massive MIMO Systems in TDD-mode Using Walsh-Hadamard Transformed Subsampled Data at the Base Station,” Wireless Pers Commun, vol. 119, no. 1, pp. 423–440, Jul. 2021, doi: 10.1007/s11277-021-08218-4.

S. Bharati and P. Podder, “Adaptive PAPR Reduction Scheme for OFDM Using SLM with the Fusion of Proposed Clipping and Filtering Technique in Order to Diminish PAPR and Signal Distortion,” Wireless Pers Commun, vol. 113, no. 4, pp. 2271–2288, Aug. 2020, doi: 10.1007/s11277-020-07323-0.

K. Anoh, C. Tanriover, and B. Adebisi, “On the Optimization of Iterative Clipping and Filtering for PAPR Reduction in OFDM Systems,” IEEE Access, vol. 5, pp. 12004–12013, 2017, doi: 10.1109/ACCESS.2017.2711533.

H. Al-Kanan and F. Li, “A Simplified Accuracy Enhancement to the Saleh AM/AM Modeling and Linearization of Solid-State RF Power Amplifiers,” Electronics, vol. 9, no. 11, p. 1806, Oct. 2020, doi: 10.3390/electronics9111806.

A. E. Jayati, Wirawan, T. Suryani, and Endroyono, “Characteristic of HPA Nonlinear Distortion Effects in MIMO-GFDM Systems,” in 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju: IEEE, Oct. 2018, pp. 379–384. doi: 10.1109/ICTC.2018.8539527.

C. An and H.-G. Ryu, “Design and Performance Comparison of W-OFDM Under the Nonlinear HPA Environment,” Wireless Pers Commun, vol. 98, no. 1, pp. 983–999, Jan. 2018, doi: 10.1007/s11277-017-4904-x.

A. E. Jayati and M. Sipan, “Impact of Nonlinear Distortion with the Rapp Model on the GFDM System,” in 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE), Surabaya, Indonesia: IEEE, Oct. 2020, pp. 1–5. doi: 10.1109/ICVEE50212.2020.9243295.

L. Xu, F. Gao, W. Zhang, and S. Ma, “Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers,” in 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China: IEEE, Mar. 2021, pp. 1–6. doi: 10.1109/WCNC49053.2021.9417512.

Z. Li, S. Perera, Y. Zhang, G. Zhang, and R. Doviak, “Phased-Array Radar System Simulator (PASIM): Development and Simulation Result Assessment,” Remote Sensing, vol. 11, no. 4, p. 422, Feb. 2019, doi: 10.3390/rs11040422.

A. Jayati, W. Wirawan, T. Suryani, and E. Endroyono, “Partial Transmit Sequence and Selected Mapping Schemes for PAPR Reduction in GFDM Systems,” IJIES, vol. 12, no. 6, pp. 114–122, Dec. 2019, doi: 10.22266/ijies2019.1231.11.

N. Ishtiaq and S. A. Sheikh, “Maximum Likelihood SNR Estimation for QAM Signals Over Slow Flat Fading Rayleigh Channel,” KSII TIIS, Nov. 2016, doi: 10.3837/tiis.2016.11.009.