如今網際網路蓬勃發展，行動通訊技術的演進功不可沒。隨著即時通訊和行動支付的普及與智慧型手機和平板電腦等裝置的銷量暴增，更意味著通訊網路乘載的數據流量將日益增加。為此，國際電信聯盟 (International Telecommunication Union，ITU) 訂定了第五代行動通訊技術 (5G) 發展框架與總體目標，該技術稱為 IMT-2020 (International Mobile Telecommunications-2020)。
使用授權頻段 (Licensed Band) 之窄頻物聯網 (Narrow Band Internet of Thing，NB-IoT) 屬於低功耗廣域網路 (Low Power Wide Area Network，LPWAN) 之相關技術，由於無需佈建新網路，因而受到各大電信設備商與營運商青睞。窄頻物聯網由第三代合作夥伴計畫 (3rd Generation Partnership Project, 3GPP) 標準組織於第十三版 (Release 13) 所提出，計劃用於支援廣域物聯網的標準，甚至作為 IMT-2020 三大應用場景之大規模機器型通訊 (Massive Machine Type Communications，mMTC) 項目的基礎。
然而，目前窄頻物聯網系統規範基地台 (Evolved Node B，eNB) 僅針對已完成 Radio Resource Control (RRC) 連線之使用者裝置 (User Equipment，UE) 進行上下行資源分配。每當處於閒置狀態之使用者裝置欲發送數據到基地台，須先執行隨機存取程序 (Random Access Procedure，RAP) 以建立 RRC 連線。隨機存取程序使用多通道時槽阿羅哈協定 (multi-channel slotted ALOHA)，其系統吞吐量伴隨連網裝置數量增加而下降，潛在的碰撞機率進而導致資源浪費與數據傳輸延遲。換言之，於大規模機器型通訊應用場景中，如何降低大量設備進行隨機存取程序時發生碰撞的機率是相當值得探討的議題。為了克服這個問題，本篇論文嘗試導入 Wi-Fi 中的隨機後退 (Random Backoff，RB) 演算法於隨機存取程序中，預期透過此設計降低使用者裝置之間的相互干擾。特別強調的是，此機制的設計與現有規範完全相容，為一項具專利價值之技術。

Nowadays, the growing internet services rely on fast development of mobile communication technologies. With the popularity of instant messaging and mobile payments as well as the increase of smart mobile devices, surfing the Internet anytime, anywhere has become a kind of necessity for modern life. It also means that traffic on the communications network will be increased. Based on this trend, the International Telecommunication Union (ITU) has established a framework which goal is the 5G development called International Mobile Telecommunications-2020 (IMT-2020).
Without the overhead of building a new network, the Narrow Band Internet of Thing (NB-IoT), which is one of technology for Low Power Wide Area Network (LPWAN), attracts major vendors and operators. The NB-IoT operates on the licensed band and it is a new 3GPP radio access technology aiming to support the standards of wide-area IoT, even as the basis for the Massive Machine Type Communications (mMTC) that is one of three major application scenarios of IMT-2020.
However, in NB-IoT network, the base station (eNB) only schedules the channel resource for the devices (UEs) which have established the radio resource control (RRC) connections. For a UE staying in IDLE mode, it has to perform the random access procedure (RAP) in order to establish RRC connection with the eNB. The RAP in NB-IoT is based on multi-channel slotted ALOHA protocol, which gets low throughput under high load. In other words, how to efficiently reduce the collision probability in mMTC scenario is an important and patentable technology. To overcome this problem, this thesis tries to integrate the RAP with the random backoff solution which has been adopted in Wi-Fi networks in order to minimize the interference among UEs. Moreover, this mechanism is full compliance with current specifications.

[1] Min Chen, Yiming Miao, Yixue Hao, Kai Hwang, “Narrow band Internet of things,” IEEE Access, Vol. 5, pp. 20557-20577, September 2017.
[2] Andreas Burg, Anupam Chattopadhyay and Kwok-Yan Lam “Wireless Communication and Security Issues for Cyber-Physical Systems and the Internet of Things,” Proceedings of the IEEE, Vol. 106, No. 1, pp.38-60, January 2018.
[3] Qualcomm Incorporated, et al., RP-151621 Narrowband IoT, 3GPP TSG RAN Meeting #60, 2015.
[4] Y.-P. Eric Wang, Xingqin Lin, Ansuman Adhikary, Asbjorn Grovlen, Yutao Sui, Yufei Blankenship, Johan Bergman and Hazhir S. Razaghi “A Primer on 3GPP Narrowband Internet of Things,” IEEE Communications Magazine, Vol. 55, No. 3, pp. 117-123, March 2017.
[5] Luca Feltrin, Galini Tsoukaneri, Massimo Condoluci, Chiara Buratti, Toktam Mahmoodi, Mischa Dohler, and Roberto Verdone, “Narrowband IoT: A Survey on Downlink and Uplink Perspectives,” IEEE Wireless Communications, Vol.26, No. 1, pp.78-86, February 2019.
[6] Rapeepat Ratasuk, Benny Vejlgaard, Nitin Mangalvedhe, Amitava Ghosh, "NB-IoT System for M2M Communication," in Wireless Communications and Networking Conference Workshops (WCNCW), pp. 428-432, August 2016.
[7] 3GPP TS 36.321, “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Medium Access Control (MAC) Protocol Specification,” V13.2.0, August 2016.
[8] 3GPP TS 36.331, “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Radio resource control (RRC) protocol specification,” V13.2.0, August 2016.
[9] Nan Jiang, Yansha Deng, Massimo Condoluci, Weisi Guo, Arumugam Nallanathan and Mischa Dohler, “RACH Preamble Repetition in NB-IoT Network,” IEEE Communications Letters, Vol. 22, No. 6, pp.1244-1247, January 2018.
[10] 3GPP TS 36.211, “Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation,” V13.3.0, November 2016.
[11] Wha Sook Jeon, Seung Beom Seo and Dong Geun Jeong, “Effective Frequency Hopping Pattern for ToA Estimation in NB-IoT Random Access,” IEEE Transactions on Vehicular Technology, Vol.67, No.10, pp.10150-10154, July 2018.
[12] 3GPP TS 36.213, “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures,” V13.2.0, August 2016.
[13] Ruki Harwahyu, Ray-Guang Cheng, Wan-Jung Tsai, Jeng-Kuang Hwang and Giuseppe Bianchi, “Repetitions v.s. Retransmissions: Trade-off in Configuring NB-IoT Random Access Channels,” IEEE Internet of Things Journal (Early Access), January 2019.
[14] Ziqi Chen, David B. Smith, “Heterogeneous Machine-Type Communications in Cellular Networks: Random Access Optimization by Deep Reinforcement Learning,” IEEE International Conference on Communications (ICC), July 2018.
[15] Luis Tello-Oquendo, Diego Pacheco-Paramo, Vicent Pla, Jorge Martinez-Bauset, “Reinforcement Learning-Based ACB in LTE-A Networks for Handling Massive M2M and H2H Communications,” IEEE International Conference on Communications (ICC), July 2018.
[16] Giuseppe Bianchi, “Performance analysis of the IEEE 802.11 distributed coordination function”, IEEE Journal on Selected Areas in Communications, Vol. 18, No. 3, pp.535-547, March 2000.
[17] Ruki Harwahyu, Ray-Guang Cheng Chia-Hung Wei, and Riri Fitri Sari, “Optimization of Random Access Channel in NB-IoT,” IEEE Internet of Things Journal, Vol.5, No.1, pp.391-402, 2018.
[18] Yifeng Zhao, Kai Liu, Huayu Yan and Lianfen Huang, “A classification back-off method for capacity optimization in NB-IOT random access,” IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID), February 2018.
[19] Ray-Guang Cheng, Chia-Hung Wei, Shiao-Li Tsao and Fang-Ching Ren, “RACH collision probability for machine-type communications,” IEEE 75th Vehicular Technology Conference (VTC Spring), July 2012.
[20] Chia-Hung Wei, Ray-Guang Cheng and Shiao-Li Tsao, “Modeling and Estimation of One-Shot Random Access for Finite-User Multichannel Slotted ALOHA Systems,” IEEE Communications Letters, Vol. 16, No. 8, pp. 1196-1199, August 2012.
[21] Yuyi Sun, Fei Tong, Zhikun Zhang and Shibo He, “Throughput Modeling and Analysis of Random Access in Narrowband Internet of Things,” IEEE Internet of Things Journal, Vol. 5, No. 3, pp. 1485-1493, June 2018.
[22] Thorndike, E. L., Animal intelligence: An experimental study of the associate processes in animals. American Psychologist, 1898.
[23] Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi, “Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications,” December 2018.
[24] Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction. Cambridge, Massachusetts: The MIT Press, 1998.
[25] David Silver, Lecture 1: Introduction to Reinforcement Learning. Google DeepMind, 2015.
[26] Shao-Yu Lien, Shao-Chou Hung, Der-Jiunn Deng, Yueh Jir Wang, “Efficient Ultra-Reliable and Low Latency Communications and Massive Machine-Type Communications in 5G New Radio,” IEEE Global Communications Conference (GLOBECOM), January 2018.
[27] Huikang Li, Gonglong Chen, Yihui Wang, Yi Gao, Wei Dong, “Accurate Performance Modeling of Uplink Transmission in NB-IoT,” IEEE International Conference on Parallel and Distributed Systems (ICPADS), February 2019.