Çöz Aktar İşbirlikli Çeşitlemeli Çok Röleli Sistemlerde Derin Öğrenme Yardımlı En İyi Röle Seçimi Ve Güç Optimizasyonu

Year 2022, Volume: 9 Issue: 1, 144 - 158, 31.01.2022

Ahmet Emir , Ferdi Kara , Hakan Kaya

https://doi.org/10.31202/ecjse.950073

Abstract

İşbirlikli Haberleşme sistemi kaynak, röleler ve hedef düğümlerden oluşmaktadır. Kaynak, işaretleri rölelere ve hedefe, röleler ise çözdüğü işareti hedef düğüme aktarmaktadır. İşbirlikli haberleşme sistemlerinde spektral verimliliğin korunması açısından en iyi röle seçimi önemli bir husustur. Ayrıca güvenli bir iletişim için kaynak röle ve röle hedef düğümleri arasındaki işaret gürültü oranlarının da maksimum yapılması gereklidir. Derin öğrenme (deep learning-DL) tekniği fiziksel seviye haberleşme tekniklerinde yaygın olarak kullanılmaya başlanan bir tekniktir. DL var olan haberleşme tekniklerine alternatif çözümler sunmaktadır. Bu çalışmada DL tekniği ile en iyi röle seçilmiştir. En iyi röle seçimi, kaynaktan rölelere ve röleler hedef arasında iletim yapılırken güç optimizasyonu da göz önünde bulundurularak yapılmıştır. Evrişimli sinir ağı (Convolutional Neural Network-CNN) tekniği ile bulunan sonuçlar geleneksel maxmin yöntemi ile tespit edilen en iyi röle ile bulunan sonuçlardan hata performansı açısından başarılıdır. Ayrıca DL ile hata başarımlarına güç optimizasyonu da önemli bir etki sağlamaktadır.

Keywords

CNN, BPSK, DF, İşbirlikli Haberleşme, Derin Öğrenme

Supporting Institution

Zonguldak Bülent Ecevit Üniversitesi Üniversitesi Bilimsel Araştırma Projeleri Birimi

Project Number

2021-75737790-02

Best Relay Selection And Power Optimization In Deep Learning Aided Multi-Relay System With Decode And Forward Cooperative Diversity

Abstract

Cooperative Communication system consists of source, relays and destination nodes Source transmits signals to relays and target node, and relays transmit the decoded signals to the target node. The best relay selection is an important issue in terms of maintaining spectral efficiency in cooperative communication systems. In addition, for a secure communication, it is necessary to maximize the signal-to-noise ratios between the source relay and relay destination nodes. Deep learning (DL) technique has been a technique that have been widely used among physical level communication techniques. DL proposes alternative solutions to existing communication techniques. In this study, the best relay has been selected with DL technique. Best relay selection has been implemented by considering power optimization while transmitting from source to the best relay and transmitting between the best relay and target node. The results obtained with the Convolutional Neural Network (CNN) technique are more successful in terms of error performance than the results found with the best relay detected by the traditional maxmin method. In addition, power optimization also has a significant effect on error performance with DL.

Keywords

CNN, BPSK, DF, Cooperative Communication, Deep Learning

Project Number

2021-75737790-02

References

• Xiao, Y., Jin, X., Shen, Y.,Guan, Q., "Joint Relay Selection and Adaptive Modulation and Coding for Wireless Cooperative Communications," in IEEE Sensors Journal, doi: 10.1109/JSEN.2021.3079331
• Ikki, S., Ahmed, M. H., "On the Performance of Adaptive Decode-and-Forward Cooperative Diversity with the Nth Best-Relay Selection Scheme," GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009, pp. 1-6, doi: 10.1109/GLOCOM.2009.5425901.
• Muenthetrakoon,W.,Khutwiang, K., Kotchasarn, C., "SER of Multi-hop Decode and Forward Cooperative Communications under Rayleigh Fading Channel," 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, 2011, pp. 318-323, doi: 10.1109/ISMS.2011.83.
• Wang, Y., Zhang, X., Liu, D., "Performance of dual-hop multi-relay cooperative communication system in Rician fading," 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2015, pp. 573-577, doi: 10.1109/ICSESS.2015.7339123.
• Ikki,S., Ahmed, M., H., ”Performance Analysis of Cooperative Diversity Wireless Networks over Nakagami-m Fading Channel,” IEEE Commun. letter, vol. 11, no. 4, pp. 334-336, April, 2007.
• Al-Mistarihi,M.,F., Magableh, A. M., Al-Khasawneh,M. M., "Closed form expression of outage probability in DCSK cooperative communication systems over Nakagami-m fading channels," 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015, pp. 512-515, doi: 10.1109/MIPRO.2015.7160325.
• Lu,H., Hong,P.,Xue, K., "High-Throughput Cooperative Communication with Interference Cancellation for Two-Path Relay in Multi-Source System," in IEEE Transactions on Wireless Communications, vol. 12, no. 10, pp. 4840-4851, October 2013, doi: 10.1109/TWC.2013.090313.121076.
• Bletsas,A., Shin, H.,Win,M. Z., Lippman, A., ”A simple Cooperativediversity method based on network path selection,” IEEE JSAC, vol. 24,no. 3, pp. 659-672 Mar. 2006.
• Ikki S., Ahmed, M. H., ”Performance of Multiple-Relay Cooperative Diversity Systems with Best Relay Selection over Rayleigh FadingChannels,” EURASIP Journal on Advances in Signal Processing, vol.2008.
• Ikki,S.,Ahmed, M. H., "On the Performance of Adaptive Decode-and-Forward Cooperative Diversity with the Nth Best-Relay Selection Scheme," GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009, pp. 1-6, doi: 10.1109/GLOCOM.2009.5425901.
• Ikki,S., Ahmed, M. H., "On the Performance of Amplify-and-Forward Cooperative Diversity with the Nth Best-Relay Selection Scheme," 2009 IEEE International Conference on Communications, 2009, pp. 1-6, doi: 10.1109/ICC.2009.5199262.
• Sanli,E., Kara, F.,Kaya, H., " Hata Yayılımının En ˙İyi Röle Seçimli İşbirlikli Haberleşme Sistemlerinin Hata Başarımına Etkisi," 2018 26th Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU), 2018, pp. 1-4, doi: 10.1109/SIU.2018.8404742.
• Lu,L., He,D.,Xie, Q.,Li,G.Y.,Yu, X., "Graph-Based Path Selection and Power Allocation for DF Relay-Aided Transmission," in IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 138-141, Feb. 2018, doi: 10.1109/LWC.2017.2760878.
• Ho, C. D., Ngo, H. Q., Matthaiou, M., "Pilot Assignment and Power Allocation for Multipair Massive MIMO DF Relaying Networks," in IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 7379-7388, July 2020, doi: 10.1109/TVT.2020.2991018.
• Saini, R., Mishra, D., Kotha, V., "Power Allocation and Relay Placement for Secrecy Outage Minimization over DF Relayed System," 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), 2021, pp. 1-4, doi: 10.1109/CCNC49032.2021.9369642.
• Kara, F., Kaya, H., "On the Error Performance of Cooperative-NOMA With Statistical CSIT," in IEEE Communications Letters, vol. 23, no. 1, pp. 128-131, Jan. 2019, doi: 10.1109/LCOMM.2018.2878729
• Kara, F., Kaya, H., "Threshold-Based Selective Cooperative-NOMA," in IEEE Communications Letters, vol. 23, no. 7, pp. 1263-1266, July 2019, doi: 10.1109/LCOMM.2019.2914918.
• Yang, Y., Bai, Z., Pang, K., Sun, S., Han, T., Kwak, K., "Performance Analysis of SM-Index Modulation Based Cooperative Wireless Communication System," 2018 IEEE 18th International Conference on Communication Technology (ICCT), 2018, pp. 286-290, doi: 10.1109/ICCT.2018.8600193.
• Dimas, A., Kalogerias, D. S., Petropulu, A. P., "Cooperative Beamforming With Predictive Relay Selection for Urban mmWave Communications," in IEEE Access, vol. 7, pp. 157057-157071, 2019, doi: 10.1109/ACCESS.2019.2950274.
• Ye, H., Liang, L., Li, G. Y., & Juang, B. H., 2020. Deep Learning-Based End-to-End Wireless Communication Systems with Conditional GANs as Unknown Channels. IEEE Transactions on Wireless Communications, 19(5), 3133–3143. https://doi.org/10.1109/TWC.2020.2970707.
• Ye, H., Li, G. Y., Juang, B. H., 2018. Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems. IEEE Wireless Communications Letters, 7(1), 114–117. https://doi.org/10.1109/LWC.2017.2757490.
• Luo, B., Peng, Q., Cosman, P. C., Milstein, L. B.,2019. Robustness of Deep Modulation Recognition under AWGN and Rician Fading. In Conference Record - Asilomar Conference on Signals, Systems and Computers (Vol. 2018-October, pp. 447–450). https://doi.org/10.1109/ACSSC.2018.8645089.
• Wang,X., Zhang,Y., Shen, R., Xu, Y., Zheng, F.C., "DRL-Based Energy-Efficient Resource Allocation Frameworks for Uplink NOMA Systems," in IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7279-7294, Aug. 2020, doi: 10.1109/JIOT.2020.2982699.
• Wu, N., Wang, X., Lin, B., Zhang, K.,2019. A CNN-Based End-to-End Learning Framework Toward Intelligent Communication Systems. IEEE Access, 7, 110197–110204. https://doi.org/10.1109/access.2019.2926843.
• Gui, G., Huang, H., Song, Y., Sari, H., 2018. Deep Learning for an Effective Nonorthogonal Multiple Access Scheme. IEEE Transactions on Vehicular Technology, 67(9), 8440–8450. https://doi.org/10.1109/TVT.2018.2848294.
• Zhang, J., Tao, X., Wu, H., Zhang, N., Zhang, X., 2020. Deep Reinforcement Learning for Throughput Improvement of the Uplink Grant-Free NOMA System. IEEE Internet of Things Journal, 7(7), 6369–6379. https://doi.org/10.1109/JIOT.2020.2972274.
• Emir, A., Kara, F., Kaya, H., Yanikomeroglu, H., "DeepMuD: Multi-User Detection for Uplink Grant-Free NOMA IoT Networks via Deep Learning," in IEEE Wireless Communications Letters, vol. 10, no. 5, pp. 1133-1137, May 2021, doi: 10.1109/LWC.2021.3060772.
• Luong, T. Van, 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), 1159–1162. https://doi.org/10.1109/LWC.2019.2909893.
• Alrabeiah, M.,Alkhateeb, A., 2020, Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels. IEEE Transactions on Communications, 68(9), 5504–5518. https://doi.org/10.1109/TCOMM.2020.3003670
• Lu,Y., Cheng,P., Chen, Z., Li, Y., Mow, W.H., Vucetic, B., "Deep Autoencoder Learning for Relay-Assisted Cooperative Communication Systems," in IEEE Transactions on Communications, vol. 68, no. 9, pp. 5471-5488, Sept. 2020, doi: 10.1109/TCOMM.2020.2998538.
• Su,Y., Lu, X., Zhao, Y., Huang, L., Du, X., "Cooperative Communications With Relay Selection Based on Deep Reinforcement Learning in Wireless Sensor Networks," in IEEE Sensors Journal, vol. 19, no. 20, pp. 9561-9569, 15 Oct.15, 2019, doi: 10.1109/JSEN.2019.2925719.
• Emir, A., Kara, F., Kaya, H., Yanikomeroglu, H., "Deep Learning Empowered Semi-Blind Joint Detection in Cooperative NOMA", Access IEEE, vol. 9, pp. 61832-61852, 2021.
• Li,T., Liu,W., Zeng,Z., Xiong N. N. 2021.DRLR: A Deep Reinforcement Learning based Recruitment Scheme for Massive Data Collections in 6G-based IoT networks, IEEE Internet of Things Journal(Early Acces). doi: 10.1109/JIOT.2021.3067904.
• Yang H,. Xiong, Z., Zhao, J., Niyato, D., Xiao, L.,. Wu, Q. 2021, “Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications”, IEEE Transactions on Wireless Communications 20, 1, 375-388.
• Yang H., Alphones,A., Xiong,Z., Niyato,D., Zhao,J. and. Wu,K. 2020.Artificial-Intelligence-Enabled Intelligent 6G Networks, IEEE Network, vol. 34, no. 6, pp. 272-280. doi: 10.1109/MNET.011.2000195.
• Cun Y. Le Cun et al., "Handwritten digit recognition: applications of neural network chips and automatic learning," IEEE Communications Magazine, vol. 27, no. 11, pp. 41-46, Nov. 1989, doi: 10.1109/35.41400