Abstract
Modern receivers apply smooth demodulation and demapping processes to received symbols, using bit log-likelihood ratios (LLRs). Known as “LLRnet" demodulator architecture is offered that is a global educatable neural network attributed in this paper. The calculation of the optimal LLR algorithm includes the calculation of each bit in the LLR value and requires the assessment of whole lattice points as high dimensional which is unpractical for the QAM modulation. Known the most in the literature the Maximum Likelihood (ML) detector shows very high computational complexity that is used in the QAM scheme. Besides LLRnet developed achievement importantly, all calculational complexity is also reduced. Via estimating exact log-likelihood, how to create symbols, and channel corruptions for training a neural network LLRNet is shown in this paper. New and contemporary radio communication systems, such as 5G- NR (New Radio) and DVB (Digital Video Broadcasting) for satellite, DVB (S.2 Second Generation) utilize the LLR approach that calculates soft bit values with FEC (Forward Error Correction) algorithms and utilizes demodulated smooth bit values. This article aims a link-level simulation study to implement of LLRnet to DVB S2 and 5G-NR. The motivation of this study is seen that performing machine learning techniques on physical layer scheme, makes LLRNet a powerful example for practicability. This paper offers to compare Max-Log Approximate LLR, Exact LLR, and LLRNet methods for 16, 32 and 128 QAM.
Keywords
References
- Erfanian, J., Pasupathy, S., & Gulak, G. (1994). Reduced complexity symbol detectors with parallel structure for ISI channels. IEEE Transactions on Communications, 42(234) 1661–1671.
- Robertson, P., Villebrun, E., & Hoeher, P. (1995, June). A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain. Proceedings IEEE International Conference on Communications ICC 95, ( pp. 1009–1013).
- Tosato, F., & Bisaglia, P. (2002, April). Simplified soft-output demapper for binary interleaved COFDM with application to HIPERLAN/2. IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333), (vol. 2, pp. 664–668).
- Wang, Q., Xie, Q., Wang, Z., Chen, S., & Hanzo, L. (2014). A universal low-complexity symbol-to-bit soft demapper. IEEE Transactions on Vehicular Technology, 63(1), 119–130.
- Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2 (4), 303–314.
- Yao, Y., Su, Y., Shi, J., & Lin, J. (2015, August). A low-complexity soft QAM demapper based on first-order linear approximation. 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), (pp. 446–450).
- Luong, T.V., Ko, Y., Vien, N. A., Nguyen D. H. N., & Matthaiou, M. (2019). Deep Learning-Based Detector for OFDM-IM. IEEE Wireless Communications Letters, 8(4).
- Xie, Y., The, K.C., & Kot, A. C. (2021). Deep Learning-Based Joint Detection for OFDM-NOMA Scheme. IEEE Communications Letters, 25(8).
- Yi, X., & Zhong, C. (2020). Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems. IEEE Communications Letters, 24(12).
- Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989–993.
- Shental, O., & Hoydis, J. (2019, December). Machine LLRning": Learning to Softly Demodulate. 2019 IEEE Globecom Workshops (GC Wkshps).
- Hu, Z., Chen, F., Wen, M., Ji, F., & Yu H. (2018). Low-Complexity LLR Calculation for OFDM with Index Modulation. IEEE Wireless Communications Letters, Vol. 7, No. 4.
- Sabud, S., & Kumar, P. (2022). A Low Complexity Two-Stage LLR Detector for Downlink OFDM-IM NOMA. IEEE Communications Letters, Vol. 26, No. 10.
- Espluga, L. O., Aubault-Roudier, M., Poulliat, C. Boucheret, M. L., Al-Bitar, H., & Closas, P. (2020). LLR Approximation for Fading Channels Using a Bayesian Approach. IEEE Communications Letters, Vol. 24, No. 6.
- Pang, P., Chang, H., Zhang, Q., Xin, X., Gao, R., Tian, F., Tian, Q., Wang, Y., & Guo, D. (2021). The research of probabilistic shaping signal transmission scheme based on neural network LLR calculation. 2021 19th International Conference on Optical Communications and Networks (ICOCN).
Abstract
Modern alıcılar, bit log-olasılık oranlarını (LLR'ler) kullanarak alınan sembollere yumuşak demodülasyon ve de-maping işlemleri uygular. Optimal LLR algoritmasının hesaplanması, LLR değerindeki her bir bitin hesaplanmasını içerir ve QAM modülasyonu için pratik olmayan, tüm kafes noktalarının yüksek boyutlu olarak değerlendirilmesini gerektirir. Literatürde en çok bilinen Maksimum Olabilirlik (ML) dedektörü, QAM şemasında kullanılan çok yüksek hesaplama karmaşıklığı gösterir. Bu makalede atfedilen, küresel bir eğitilebilir sinir ağı olan “LLRnet” demodülatör mimarisi sunulmaktadır. LLRnet başarıyı önemli ölçüde geliştirmesinin yanı sıra, tüm hesaplama karmaşıklığını da azaltır. Bu yazı, bir sinir ağının LLRNet'i eğitmek için tam log-olasılığını tahmin etmesini, sembollerin nasıl oluşturulacağını ve kanal bozulmalarının nasıl olacağını göstermektedir. Uydu için 5G-NR (Yeni Radyo) ve DVB (Dijital Video Yayını), DVB (S.2 İkinci Nesil) gibi yeni ve çağdaş radyo iletişim sistemleri, FEC (İleri Hata Düzeltme) ile yumuşak bit değerlerini hesaplayan algoritmalar ve demodüle edilmiş düzgün bit değerlerini kullanan LLR yaklaşımını kullanır. Bu makale, LLRnet'in DVB S2 ve 5G-NR'ye uygulanması için bağlantı düzeyinde bir simülasyon çalışması sunmaktadır. Bu çalışmada, makine öğrenmesi tekniklerinin fiziksel katman şeması üzerinde gerçekleştirilmesinin LLRnet'i uygulanabilirlik açısından güçlü bir örnek haline getirdiği görülmektedir. Yine bu makale, 16, 32 ve 128 QAM için Max-Log Approximate LLR, Tam LLR ve LLRNet yöntemlerini karşılaştırmayı önerir.
Keywords
References
- Erfanian, J., Pasupathy, S., & Gulak, G. (1994). Reduced complexity symbol detectors with parallel structure for ISI channels. IEEE Transactions on Communications, 42(234) 1661–1671.
- Robertson, P., Villebrun, E., & Hoeher, P. (1995, June). A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain. Proceedings IEEE International Conference on Communications ICC 95, ( pp. 1009–1013).
- Tosato, F., & Bisaglia, P. (2002, April). Simplified soft-output demapper for binary interleaved COFDM with application to HIPERLAN/2. IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333), (vol. 2, pp. 664–668).
- Wang, Q., Xie, Q., Wang, Z., Chen, S., & Hanzo, L. (2014). A universal low-complexity symbol-to-bit soft demapper. IEEE Transactions on Vehicular Technology, 63(1), 119–130.
- Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2 (4), 303–314.
- Yao, Y., Su, Y., Shi, J., & Lin, J. (2015, August). A low-complexity soft QAM demapper based on first-order linear approximation. 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), (pp. 446–450).
- Luong, T.V., Ko, Y., Vien, N. A., Nguyen D. H. N., & Matthaiou, M. (2019). Deep Learning-Based Detector for OFDM-IM. IEEE Wireless Communications Letters, 8(4).
- Xie, Y., The, K.C., & Kot, A. C. (2021). Deep Learning-Based Joint Detection for OFDM-NOMA Scheme. IEEE Communications Letters, 25(8).
- Yi, X., & Zhong, C. (2020). Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems. IEEE Communications Letters, 24(12).
- Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989–993.
- Shental, O., & Hoydis, J. (2019, December). Machine LLRning": Learning to Softly Demodulate. 2019 IEEE Globecom Workshops (GC Wkshps).
- Hu, Z., Chen, F., Wen, M., Ji, F., & Yu H. (2018). Low-Complexity LLR Calculation for OFDM with Index Modulation. IEEE Wireless Communications Letters, Vol. 7, No. 4.
- Sabud, S., & Kumar, P. (2022). A Low Complexity Two-Stage LLR Detector for Downlink OFDM-IM NOMA. IEEE Communications Letters, Vol. 26, No. 10.
- Espluga, L. O., Aubault-Roudier, M., Poulliat, C. Boucheret, M. L., Al-Bitar, H., & Closas, P. (2020). LLR Approximation for Fading Channels Using a Bayesian Approach. IEEE Communications Letters, Vol. 24, No. 6.
- Pang, P., Chang, H., Zhang, Q., Xin, X., Gao, R., Tian, F., Tian, Q., Wang, Y., & Guo, D. (2021). The research of probabilistic shaping signal transmission scheme based on neural network LLR calculation. 2021 19th International Conference on Optical Communications and Networks (ICOCN).
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 29, 2023 |
| Publication Date | December 31, 2023 |
| Submission Date | August 4, 2022 |
| Published in Issue | Year 2023 Volume: 7 Issue: 2 |
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