請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20487完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭斯彥(Sy-Yen Kuo) | |
| dc.contributor.author | Li-Sheng Chen | en |
| dc.contributor.author | 陳立勝 | zh_TW |
| dc.date.accessioned | 2021-06-08T02:50:25Z | - |
| dc.date.copyright | 2020-09-03 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-28 | |
| dc.identifier.citation | Ericsson Mobility Report. [Online]. Available: https://www.ericsson.com/assets/local/mobility-report/documents/2018/ericssonmobility-report-november-2018.pdf Feasibility Study on New Services and Markets Technology Enablers (Release 14), document 3GPP TR 22.891 V14.2.0, Sep. 2016. IMT Vision Framework and Overall Objectives of the Future Development of IMT for 2020 and Beyond, document ITU-R M.2083-0, Sept. 2015. Study on Scenarios and Requirements for Next Generation Access Technologies (Release 14), document 3GPP TR 38.913 V14.3.0, June. 2017. A. Sampath, P. S. Kumar, and J. M. Holtzman, “On setting reverse link target SIR in a CDMA system,” in Proc. IEEE Veh. Technol. Conf., Phoenix, AZ, USA, May 1997, vol. 2, pp. 929–933. F. Blanquez-Casado, G. Gomez, M. C. A. Torres, and J. T. Entrambasaguas, “eOLLA: An enhanced outer loop link adaptation for cellular networks,” EURASIP J. Wireless Commun. Netw., no. 1, p. 20, Dec. 2016. M. G. Sarret, D. Catania, F. Frederiksen, A. F. Cattoni, G. Berardinelli, and P. Mogensen, “Dynamic Outer Loop Link Adaptation for the 5G Centimeter-Wave Concept,” in Proc. 21th European Wireless Conference, Budapest, Hungary, May 2015, pp. 1-6. E. Dahlman, S. Parkvall, and J. Sköld, 5G NR: The Next Generation Wireless Access Technology, New York, NY, USA: Academic, 2018. K. I. Pedersen, G. Monghal, I. Z. Kovács, T. E. Kolding, A. Pokhariyal, F. Frederiksen, and P. Mogensen, “Frequency domain scheduling for OFDMA with limited and noisy channel feedback,” in Proc. VTC (Fall), Oct. 2007, pp. 1792–1796. R. Fantacci, D. Marabissi, D. Tarchi, and I. Habib, “Adaptive Modulation and Coding Techniques for OFDMA Systems,” IEEE Transactions on Wireless Communications, vol. 8, no. 9, pp. 4876–4883, 2009. P. V. Klaine, M. A. Imran, O. Onireti, and R. D. Souza, “A survey of machine learning techniques applied to self-organizing cellular networks,” IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2392–2431, 4th Quart., 2017. M. Jaber, M. A. Imran, R. Tafazolli, and A. Tukmanov, “An Adaptive Backhaul-aware Cell Range Extension Approach,” in Proc. IEEE International Conference on Communication Workshop (ICCW), London, UK, June 2015, pp. 74–79. S. Fan, H. Tian, and C. Sengul, “Self-optimization of Coverage and Capacity based on a Fuzzy Neural Network with Cooperative Reinforcement Learning,” EURASIP Journal on Wireless Communications and Networking, vol. 2014, no. 1, p. 57, Apr. 2014. M. Miozzo, L. Giupponi, M. Rossi, and P. Dini, “Switch On/Off Policies for Energy Harvesting Small Cells through Distributed Q-Learning,” in Proc. IEEE Wireless Communications and Networking Conference Workshops (WCNCW), San Francisco, CA, USA, March 2017, pp. 1–6. P. H. P. de Carvalho, R. Vieira, and J. Leite, “A Continuous State Reinforcement Learning Strategy for Link Adaptation in OFDM Wireless Systems,” Journal of Communication and Information Systems, vol. 30, no. 1, Jun. 2015, DOI. 10.14209/JCIS.2015.6. R. Bruno, A. Masaracchia, and A. Passarella, “Robust adaptive modulation and coding (AMC) selection in LTE systems using reinforcement learning,” in Proc. IEEE 80th Vehicular Technology Conference (VTC2014-Fall), Vancouver, BC, Canada, Sept. 2014, pp. 1–6. L. Zhang, J. Tan, Y. Liang, G. Feng, and D. Niyato, “Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks,” IEEE Transactions on Wireless Communications, vol. 18, no. 6, pp. 3281–3294, Jun. 2019. NR; User Equipment (UE) conformance specification; Radio Resource Management (RRM) (Release 16), document 3GPP TS 38.533 V16.1.0, 2019. NR; User Equipment (UE) radio access capabilities (Release 15), document 3GPP TS 38.306 V15.9.0, 2020. Y. Hou, L. Zhao, and H. Lu, “Fuzzy neural network optimization and network traffic forecasting based on improved differential evolution,” Future Generation Computer Systems, vol. 81, pp. 425-432, 2018. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. R. Jozefowicz, W. Zaremba and I. Sutskever, 'An empirical exploration of recurrent network architectures', in Proc. 32nd Int. Conf. Mach. Learn. (ICML), 2015, pp. 2342-2350. N. M. Nasrabadi, “Pattern recognition and machine learning,” J. Electron. Imag., vol. 16, no. 4, 2007. J. Han and C. Moraga, ‘‘The influence of the sigmoid function parameters on the speed of backpropagation learning,’’ in Proc. Int. Workshop Artif. Neura, 1995, pp. 195–201. R. Hahnloser, R. Sarpeshkar, M. Mahowald, R. Douglas, and H. Seung,“Digital selection and analogue amplification coexist in a cortexinspired silicon circuit,” Nature, vol. 405, no. 6789, pp. 947–951, Jun. 2000. N. M. Nasrabadi, “Pattern recognition and machine learning,” J. Electron. Imag., vol. 16, no. 4, 2007. V. Kůrková,“Kolmogorov’s theorem and multilayer neural networks,” Neural Networks, vol. 5, pp. 501–506, 1992. NR; Physical channels and modulation (Release 15), document 3GPP TS 38.211 V15.7.0, Sept 2019. NR; Physical layer procedures for data (Release 15), document 3GPP TS 38.214 V15.7.0, Sept 2019. S. Ahmadi, 5G NR: Architecture, Technology, Implementation, and Operation of 3GPP New Radio Standards. Cambridge, MA, USA: Academic, 2019. H. Elgendi , M. Mäenpää, T. Levanen , T. Ihalainen , S. Nielsen and M. Valkama, 'Interference Measurement Methods in 5G NR: Principles and Performance,' in Proc. 16th International Symposium on Wireless Communication Systems, Oulu, Finland, Aug. 2019. C. Yu, W. Xiangming, L. Xinqi, and Z. Wei, “Research on the modulation and coding scheme in LTE TDD wireless network,” in Proc. International Conference on Industrial Mechatronics and Automation, Chengdu, China, May 2009. M. C. Aguayo‐Torres, G. Gomez‐Paredes, J. Ramiro, J. T. Entrambasaguas, 5G Ref: The Essential 5G Reference: Adaptive Modulation and Coding, Wiley, 2020. M. C. Aguayo‐Torres, G. Gomez‐Paredes, J. Ramiro, J. T. Entrambasaguas, Adaptive Modulation and Coding, Wiley, 2020. Sami H. O. Salih and Mamoun M. A. Suliman, “Implementation of Adaptive Modulation and Coding Technique Using Matlab,” International Journal of Scientific Engineering Research, vol. 2, no. 5, May 2011. NR; Physical channels and modulation (Release 15), document 3GPP TS 38.211 V15.2.0, December 2018. NR; Multiplexing and channel coding (Release 16), document 3GPP TS 38.212 V15.6.0, June 2019. I. Kachalsky, I. Zakirzyanov and V. Ulyantsev, “Applying Reinforcement Learning and Supervised Learning Techniques to Play Hearthstone,” in Proc. 16th IEEE International Conference on Machine Learning and Applications, Cancun, Mexico, Dec. 2017. G. E. Oien, H. Holm, and K. J. Hole, “Impact of channel prediction on adaptive coded modulation performance in Rayleigh fading,” IEEE Trans. Veh. Technol., vol. 53, pp. 758–769, May 2004. L. Jianggang, T. Wanbin, L. Shaoqian, “A simple and effective SNR estimation algorithm for OFDM in Rayleigh Fading Channels,” in Proc. IEEE International Conference on Information, Communications and Signal Processing (ICICS), 2010, pp. 1-4. H. Chen and C. Jian, “Application Research of Technology Combining AMC and OFDM in HF Communication Systems,” in Proc. IEEE International Conference on Wireless, Chengdu, China, Sept. 2010. Y. Yu, X. Tan, Y. Chi, L. Ma and X. Li, “A Novel AMC Algorithm Based on Improved Channel Estimation for SC-FDE Systems,” in Proc. International Conference on Instrumentation Measurement Computer Communication and Control, 2012. X. Yuan ; Y. Liu ; X. Jing ; B. Han, “New LTE downlink CQI correction algorithm,” in Proc. IEEE International Conference on Communication Technology, Guilin, China, Nov. 2013. C. H. Yu, A. Hellsten and O. Tırkkonen, “Rate adaptation of AMC/HARQ systems with CQI errors,” in Proc. IEEE Veh. Technol. Conf., May 2010, pp. 1-5. M. Taki, R. Zaeem, and M. Heshmati, “Throughput optimized error-free transmission using optimum combination of AMC and ARQ based on imperfect CSI,” in Proc. Int. Conf. Inf. Commun. Technol. Res. (ICTRC), 2015, pp. 182–185. Z. H. Z. He and F. Z. F. Zhao, “Performance of HARQ with AMC Schemes in LTE Downlink,” in Proc. International Conference on Communications and Mobile Computing (CMC), Shenzhen, China, April 2010. Y. Sano, Y. Ohwatari, Y. Sagae, A. Morimoto, Y. Okumura, “Investigation on Feedback Channel State Information for Interference Rejection Combining Receiver in LTE-Advanced Downlink,” in Proc. IEEE 79th Vehicular Technology Conference (VTC Spring) , Seoul, South Korea, May 2014. G. Xu and Y. Lu, “Channel and modulation selection based on support vector machines for cognitive radio,” in Proc. International Conference on Wireless Communications, Networking and Mobile Computing, Sep. 2006, pp. 1 –4. R. C. Daniels, C. M. Caramanis, and R. W. Heath, “Adaption in convolutionally coded MIMO-OFDM wireless systems through supervised learning and SNR ordering,” IEEE Trans. Veh. Technol., vol. 59, no. 1, pp. 114–126, Jan. 2010. J. P. Leite, P. H. P. de Carvalho, and R. D. Vieira, “A flexible framework based on reinforcement learning for adaptive modulation and coding in OFDM wireless systems,” in Proc. IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, Apr. 2012, pp. 809–814. R. Bruno, A. Masaracchia, and A. Passarella, “Robust Adaptive Modulation and Coding (AMC) selection in LTE systems using reinforcement learning,” in Proc. IEEE 80th Veh. Technol. Conf., Sep. 2014, pp. 1–6. I. Ahmad, K. H. Chang, “Effective SNR Approach of Link to System Level Mapping in Underwater Acoustic Communications Network,” http://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE07125661, 2017. NR; User Equipment (UE) radio transmission and reception (Release 15), document 3GPP TS 38.101 V15.5.0, May 2019. NR; Physical layer procedures for control (Release 15), document 3GPP TS 38.213 V15.3.0, October 2018. T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inform. Theor., vol. IT-13, no. 1, pp. 21–27, Jan. 1967. L. Kuncheva, “Editing for the k-Nearest neighbors rule by a genetic algorithm,” Pattern Recognit. Lett., vol. 16, pp. 809–814, 1995. R. C. Daniels, C. M. Caramanis, and R. W. Heath, Jr., “A supervised learning approach to adaptation in practical MIMO-OFDM wireless systems,” in Proc. IEEE Global Commun. Conf., 2008, pp. 1–5. R. O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification. John Wiley Sons, 2001. X. Q. Sun, Y. J. Chen, Y. H. Shao, C. N. Li, and C. H. Wang, ‘‘Robust nonparallel proximal support vector machine with Lp-norm regularization,” IEEE Access, vol. 6, pp. 20334–20347, 2018. I.H. Witten and E. Frank, Data Mining-Pracitcal Machine Learning Tools and Techniques with JAVA Implementations. Morgan Kaufmann, 2000. Service requirements for machine-type communications(Release 15), document 3GPP TS 22.368 V13.1.0, March 2016. R. Verma, A. Prakash, A. Agrawal, K. Naik, R. Tripathi, T. Khalifa, M. Alsabaan, T. Abdelkader, A. Abogharaf, “Machine-to-machine (M2M) communications: A survey,” J. Netw. Comput. Appl., vol. 66, pp. 83–105, 2016. S. Qipeng, L. Nuaymi, X. Lagrange, 'Survey of radio resource management issues and proposals for energy-efficient cellular networks that will cover billions of machines', EURASIP journal on wireless communications and networking, vol. 2016, no. 1, pp. 140, 2016. C. Bockelmann, N. K. Pratas, G. Wunder, S. Saur, M. Navarro, D. Gregoratti, G. Vivier, E. De Carvalho, Y. Ji, C. Stefanovic et al., 'Towards massive connectivity support for scalable mMTC communications in 5G networks', IEEE Access, vol. 6, pp. 28969-28992, 2018. A. E. Mahjoubi, T. Mazri, N. Hmina, 'NB-IoT and eMTC: Engineering results towards 5G/IoT mobile technologies', in Proc. Int. Symp. Adv. Electr. Commun. Technol., Rabat, Morocco, Nov. 2018, pp. 1-7. 5G; Study on scenarios and requirements for next generation access technologies (Release 14), document 3GPP TR 38.913 V14.2.0, May 2017. C. Bockelmann, N. K. Pratas, G. Wunder, S. Saur, M. Navarro, D. Gregoratti, G. Vivier, E. De Carvalho, Y. Ji, C. Stefanovic et al., 'Towards massive connectivity support for scalable mMTC communications in 5G networks', IEEE Access, vol. 6, pp. 28969-28992, 2018. Service accessibility (Release 13), document 3GPP TS 22.011 V13.6.0, Jul. 2016. Physical channels and modulation (Release 14), document 3GPP TS 36.211 V14.2.0, Apr. 2017. Radio resource control (RRC); Protocol specification (Release 15), document 3GPP TS 36.331 V15.3.0, Sep. 2018. A. Ali and W. Hamouda, “On the cell search and initial synchronization for NB-IoT LTE systems,” IEEE Commun. Lett., vol. 21, no. 8, pp. 1843–1846, Aug. 2017. H. Kroll, M. Korb, B. Weber, S. Willi, and Q. Huang, “Maximumlikelihood detection for energy-efficient timing acquisition in NB-IoT,” in Proc. IEEE Wireless Commun. Netw. Conf. Workshops (WCNCW), San Francisco, CA, USA, March 2017, pp. 1–5. G. Fortino, R. Gravina, W. Russo and C. Savaglio, “Modeling and Simulating Internet-of-Things Systems: A Hybrid Agent-Oriented Approach,” Computing in Science Engineering, vol. 19, no. 5, pp. 68−76, 2017. G. Fortino, “Agents meet the IoT: Toward ecosystems of networked smart objects,” IEEE Syst. Man Cybern. Mag., vol. 2, no. 2, pp. 43–47, Apr. 2016, doi: 10.1109/MSMC.2016.2557483. G. Fortino, R. Gravina, W. Russo, C. Savaglio, 'Modeling and simulating Internet-of-Things systems: A hybrid agent-oriented approach', Comput. Sci. Eng., vol. 19, no. 5, pp. 68-76, 2017. C. H. Liu, J. Fan, J. W. Branch, and K. K. Leung, “Toward QoI and energy-efficiency in Internet-of-Things sensory environments,” IEEE Trans. Emerg. Topics Comput., vol. 2, no. 4, pp. 473–487, Dec. 2014. C. H. Liu, J. Fan, P. Hui, J. Wu, and K. K. Leung, “Toward QoI and energy efficiency in participatory crowdsourcing,” IEEE Trans. Veh. Technol., vol. 64, no. 10, pp. 4684–4700, Oct. 2015. R.Ratasuk, D.Bhatoolaul ,and N.Mangalvedhe, “Performance Analysis of Voice over LTE Using Low-Complexity eMTC Devices,” in Proc. IEEE 85th Vehicular Technology Conference (VTC), Sydney, NSW, Australia, June 2017. S. Böcker, C. Arendt, P. Jörke and C. Wietfeld, “LPWAN in the Context of 5G: Capability of LoRa WAN to Contribute to mMTC,” in Proc. IEEE World Forum on Internet of Things, Limerick, Ireland, April 2019. M. E. Soussi, P. Zand, F. Pasveer, G. Dolmans, “Evaluating the performance of eMTC and NB-IoT for smart city applications,” in Proc. IEEE International Conference on Communications (ICC), pp. 1-7, May 2018. S. K. Sharma and X. Wang, “Towards massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions,” IEEE Communications Surveys Tutorials, 2019. F. Calabrese et al., “Learning Radio Resource Management in RANs: Framework Opportunities and Challenges,” IEEE Commun. Mag., vol. 56, no. 9, pp. 138-45, Sept. 2018. V. P. Kafle, Y. Fukushima, P. M. Julia and T. Miyazawa, “Consideration On Automation of 5G Network Slicing with Machine Learning,” in Proc. ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K), Santa Fe, Argentina, Nov. 2018. Z. Liu, Z. Dai, P. Yu, Q. Jin, H. Du, Z. Chu and D. Wu, “Intelligent station area recognition technology based on NB-IoT and SVM,” in Proc. IEEE 28th International Symposium on Industrial Electronics, Vancouver, BC, Canada, June 2019. I. S. Com¸sa, A. D. Domenico, and D. Ktenas, “QoS-driven scheduling in 5G radio access networks - a reinforcement learning approach,” in Proc. IEEE Glob. Commun. Conf. (GLOBECOM), Dec. 2017, pp. 1–7. L. Li, A. Ghasemi, 'IoT Enabled Machine Learning for an Algorithmic Spectrum Decision Process', IEEE IoT Journal, 2019. M. P. Reddy, G. Santosh, A. Kumar and K. Kuchi, “Improved Physical Downlink Control Channel for 3GPP Massive Machine Type Communications,” in Proc. International Conference on Communication Systems and Networks, December 2018, pp 1-25. R. C. Daniels, C. Caramanis, and R. W. Heath, “A supervised learning approach to adaptation in practical MIMO-OFDM wireless systems,” in Proc. IEEE Global Telecommun. Conf. (GLOBECOM), New Orleans, LA, USA, Nov. 2008, pp. 1–5 R. C. Daniels, C. Caramanis, and R. W. Heath, Jr., “Adaptation in convolutionally coded MIMO-OFDM wireless systems through supervised learning and SNR ordering,” IEEE Trans. Veh. Technol., vol. 59, no. 1, pp. 114–126, Jan. 2010. Z. Puljiz, M. Park, and R. W. Heath, Jr., “A machine learning approach to link adaptation for SC-FDE system,” in Proc. IEEE Global Telecommun. Conf. (GLOBECOM), Houston, TX, USA, Dec. 2011, pp. 1–5. Cellular system support for ultra-low complexity and low throughput Internet of Things (CIoT) (Release 13), document 3GPP TR 45.820 V13.0.0, Sept. 2015. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20487 | - |
| dc.description.abstract | 在第五代(5G)行動網路增強型行動寬頻 (enhanced mobile broadband, eMBB) 與新無線電 (new radio, NR) 中，自適應調變與編碼 (adaptive modulation and coding，AMC) 是一種重要的無線資源管理 (radio resource management, RRM) 技術。AMC 為依無線通道狀態與品質，能自動調整無線鏈結的調變、編碼方式與其相關參數，提昇無線鏈結傳輸品質，並增強頻譜利用效率。而在收發的傳輸過程中會有通道估計的誤差，無線通道的訊號對干擾加雜訊比 (signal to interference plus noise ratio，SINR) 量測也會有誤差。當發送端所收到與估算的通道狀態與其品質資訊可能不精確，會提高資料傳輸的錯誤率，也會降低頻譜利用效率。因此本研究探討這樣的問題，並提出解決與改進方案，針對5G eMBB與 NR 系統分別提出的基於機器學習的AMC 演算法，將通道狀態與訊號品質量測誤差、干擾、HARQ重傳次數..等作為學習模型的特徵，從而對5G eMBB與 NR 系統的通道狀態與訊號品質 進行較精確的預測，以機器學習式提出更優良的AMC演算法，從實驗模擬的結果來看，所提的演算法可大幅提高系統的傳輸效率和頻譜利用率。 另外，在 5G大規模機器類型通訊(massive machine type communications, mMTC) 應用情境中，訊號品質較差或覆蓋範圍要求較大的終端設備需要使用更多重傳 (Repetition) 來補償額外的訊號衰減。終端設備的Repetition次數過多會浪費寶貴的無線資源，而Repetition次數不足會導致接收端的資料接收失敗。根據訊號品質選擇合適的Repetition次數相當的重要。因此，本研究提出了在5G mMTC應用情境的增強型機器類型通訊 (enhanced machine type communication, eMTC）系統RRM技術機器學習式重傳演算法，以有效提高整體網路傳輸效率。模擬結果呈現本研究所提出的學習式Repetition演算法效率更高，能夠有效地提高成功傳輸率，資源利用率，並能降低非必要的Repetition次數和平均能源消耗。 | zh_TW |
| dc.description.abstract | Adaptive modulation and coding (AMC) is a link adaptation technique based on the physical layer of fifth-generation (5G) enhanced mobile broadband (eMBB) and new radio (NR) systems. The basic principle of AMC is that under constant transmission power, AMC ensures the quality of wireless link transmission and maximizes spectral efficiency through reasonable link transmission modulation and coding rate adaptation. Under poor channel conditions, low modulation and coding rates are selected; by contrast, under favorable channel conditions, high modulation and coding rates are employed. This maximizes the transmission efficiency and ensures communication quality. During the selection of a suitable modulation and coding rate, the transmission rate must be adjusted according to channel changes, thus maximizing the transmission capability of channels. Because of equipment limitations in the receiving end, channel estimation algorithms cannot be used to acquire ideal solutions, and estimation errors are inevitable. Simultaneously, the measurement of SINR of the channel also has some deviation. Consequently, parameter information returned from the receiving end may be inaccurate, which may impede the reliability of data transmission and reduce spectrum efficiency. Therefore, these problems concerning conventional AMC must be addressed. This study proposed the AMC with learning approaches for 5G eMBB and NR systems. The algorithms addresses problems regarding reduced efficiency in conventional AMC caused by channel and noise estimation errors. In particular, these errors and channel quality (interference and HARQ retransmissions etc.) are used as features for machine learning, which allows eMBB and NR systems to accurately estimate the SINR and ensure that the estimated value approximates the value determined through ideal channel estimation. Accordingly, the transmission reliability and spectrum efficiency of such systems can be enhanced. The simulation results indicated that the proposed algorithms could achieve ideal performance and a superior bit error rate compared with a scheme of AMC technology with a lookup table, and furthermore, a lower bit error rate could improve the transmission reliability and spectral efficiency in 5G eMBB systems. In 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive numbers of repetitions consume additional wireless resources, decreasing the transmission rate and increasing energy consumption. An insufficient number of repetitions prevents the successful deciphering of data by receivers, leading to a high bit error rate. The present study developed adaptive repetition algorithms with machine learning to substantially increase network transmission efficacy for the enhanced machine type communication (eMTC) system in 5G mMTC scenario. The simulation results showed that the proposed repetition with learning approach effectively improved the probability of successful transmission, resource utilization, the average number of repetitions, and average energy consumption. It is therefore more suitable than the common lookup table is for the eMTC system in 5G mMTC scenario. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T02:50:25Z (GMT). No. of bitstreams: 1 U0001-2608202018093200.pdf: 2463452 bytes, checksum: a297c2d40a042d9480f7f1882633acdb (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 論文口試委員審定書 i 謝 辭 ii 中文摘要 iii ABSTRACT v CONTENTS vii LIST OF FIGURES ix LIST OF TABLES ix Chapter 1 Introduction 1 Chapter 2 AMC with BP-ANN Learning Scheme for 5G Enhanced Mobile Broadband 6 2.1 Related Works of AMC for 5G Enhanced Mobile Broadband 8 2.2 eMBB System and SINR Estimation 8 2.3 AMC with BP-ANN Learning Scheme 13 2.4 Performance Evaluations 18 Chapter 3 AMC with KNN Learning Approach for 5G NR 24 3.1 Related Works of AMC for 5G NR 25 3.2 5G NR AMC and CQI Estimation 27 3.3 AMC with KNN Learning Approach 29 3.4 Performance Evaluation 34 Chapter 4 Repetition with Learning Approaches in 5G mMTC 41 4.1 Related Works of Repetition in 5G mMTC 43 4.2 eMTC System 47 4.3 Repetition with Learning Approaches 51 4.4 Performance Evaluation 54 Chapter 5 Conclusion and Future Work 64 REFERENCES 66 | |
| dc.language.iso | en | |
| dc.title | 5G行動網路結合機器學習之無線資源管理演算法 | zh_TW |
| dc.title | Radio Resource Management Algorithms with Machine Learning for 5G Mobile Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 顏嗣鈞(Hsu-chun Yen),雷欽隆(Chin-Laung Lei),鍾偉和(Wei-ho Chung),陳俊良(Jiann-Liang Chen),陳英一(Ing-Yi Chen) | |
| dc.subject.keyword | 第五代行動網路,增強型行動寬頻,新無線電,大規模機器類型通訊,無線資源管理,自適應調變與編碼,重傳,機器學習,反向傳播人工神經網路,k-最近鄰居法, | zh_TW |
| dc.subject.keyword | 5G,enhanced mobile broadband (eMBB),new radio (NR),massive machine type communications (mMTC),radio resource management (RRM),adaptive modulation and coding (AMC),repetition,machine learning,back propagation artificial neural network (BP-ANN),k nearest neighbor (KNN), | en |
| dc.relation.page | 71 | |
| dc.identifier.doi | 10.6342/NTU202004174 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2608202018093200.pdf 目前未授權公開取用 | 2.41 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。