隨著物聯網應用的普及。現有的網路架構需要更新以因應此趨勢。第三代合作夥伴計畫在機器類型通訊 (Machine-Type Communication; MTC) 成為網路應用發展的關鍵技術。本論文提出利用裝置對裝置 (Device-to-Device; D2D) 通訊技術改善既有架構內隨機存取 (Random Access; RA) 程序中常見的壅塞問題。另外，為了使裝置對裝置通訊更加有效，本論文更進一步提出基於階層式分群演算法 (Hierarchical Clustering) 的分群演算法。藉由改變階層式演算法中的距離 (linkage) 計算方式，使分群更平均，避免過適 (overfitting) 現象。首先，基地台會使用改良後的分群演算法，將大量機器類型裝置 (MTC devices; MTCDs) 分為數個 D2D 通訊群組。有必要的話，基地台會更進一步將 D2D 群組劃分為數個存取層級 (Access Classes; ACs)。在正常負載下，每次隨機存取機會中，每個 D2D 群組依據 D2D 通訊嘗試次數，選擇一個成員發起非競爭隨機存取程序。藉由 D2D 通訊收集負載資訊後，該成員將資訊在隨機存取程序中將此資訊傳遞給基地台。藉此，基地台得以評估系統內各個 D2D 群組的負載狀況。並依據狀況安排競爭式隨機存取程序的訊框，所謂分時競爭解決訊框 (Time Division Contention Resolution; TDCR)。本論文提出的 TDCR 架構，旨在提升通訊的公平性，以及解決通訊壅塞的效率。根據 10,000 到 50,000 機器類型裝置的模擬結果顯示，本論文提出的 TDCR 架構之後，不但可以有效將 D2D 通訊的平均嘗試次數保持在 1.5 以下，也有效提升隨機存取成功機率達 95\% 以上。此外，系統的表現也不易受到裝置數量影響。

With the prevalence implementation of Internet of Things (IoT) applications. The existing cellular network architecture requires massive updates to adapt to the trend. The Third Generation Partnership Project proposed Machine-type Communication (MTC) as the crucial technique to expand the development of the cellular network. This thesis proposes a method utilizing device-to-device (D2D) communication techniques to resolve the common congestion issue of Random Access (RA) procedures. Also, the thesis proposed a modified clustering method based on Hierarchical Clustering. By modifying the calculation method of linkage, the modified clustering method can achieve more evenly distribution and avoid over-fitting issues. At first, the Base Station (BS) applies the modified clustering method to massive MTC devices (MTCDs) into several D2D communication groups. BS may arrange D2D groups into several Access Classes if necessary. At every RA opportunity, each D2D group selects a single device based on the number of D2D communication attempts. The selected members then initiate for a contention-free RA procedure with assigned radio resources. They also transmit intra-group information to BS during the procedure. By evaluating this information, the BS is able to schedule system frames of contention-based RA procedure to alleviate congestion. These frames are so-called Time Division Congestion Resolution (TDCR) frames. The proposed TDCR scheme aims to improve the fairness of communications and resolve congestion issues more efficiently. The simulation result shows that the proposed TDCR scheme is able to control the number of D2D attempts under 1.5 and to retain the successful probability of RA procedure above 95\%. Additionally, the performance of the system is not affected by the number of MTCDs.

[1] A. Osseiran, V. Braun, T. Hidekazu, P. Marsch, H. Schotten, H. Tullberg, M. A. Uusitalo, and
M. Schellmann, “The foundation of the mobile and wireless communications system for 2020 and
beyond: Challenges, Enablers and technology solutions,” IEEE Vehicular Technology Conference,
pp. 1–5, 2013.
[2] 3GPP, “Digital cellular telecommunications system (Phase 2+) (GSM); Universal Mobile Telecommunications
System (UMTS); LTE; Service requirements for Machine-Type Communications (MTC);
Stage 1,” Technical Specification (TS) 22.368, 3rd Generation Partnership Project (3GPP), 2020. Version
15.0.0.
[3] C. Bockelmann, N. K. Pratas, G. Wunder, S. Saur, M. Navarro, D. Gregoratti, G. Vivier, E. De Carvalho,
Y. Ji, C. Stefanovic, P. Popovski, Q. Wang, M. Schellmann, E. Kosmatos, P. Demestichas,
M. Raceala-Motoc, P. Jung, S. Stanczak, and A. Dekorsy, “Towards Massive Connectivity Support
for Scalable mMTC Communications in 5G Networks,” IEEE Access, vol. 6, pp. 28969–28992, 2018.
[4] 3GPP, “Technical Specification Group Radio Access Network; Study on RAN Improvements for
Machine-type Communications;,” Technical Report (TR) 37.868, 3rd Generation Partnership Project
(3GPP), 2011. Version 1.0.0 Release 10.
[5] I. Leyva-Mayorga, L. Tello-Oquendo, V. Pla, J. Martinez-Bauset, and V. Casares-Giner, “Performance
analysis of access class barring for handling massive M2M traffic in LTE-A networks,” 2016 IEEE
International Conference on Communications, pp. 1–6, 2016.
[6] S. Duan, V. Shah-Mansouri, Z. Wang, and V. W. Wong, “D-ACB: Adaptive Congestion Control Algorithm
for Bursty M2M Traffic in LTE Networks,” IEEE Transactions on Vehicular Technology,
vol. 65, no. 12, pp. 9847–9861, 2016.
[7] M. Y. Cheng, G. Y. Lin, H. Y. Wei, and A. C. C. Hsu, “Overload control for machine-typecommunications
in LTE-advanced system,” IEEE Communications Magazine, vol. 50, no. 6, pp. 38–
45, 2012.
[8] Y. H. Hsu, K. Wang, and Y. C. Tseng, “Enhanced cooperative access class barring and traffic adaptive
radio resource management for M2M communications over LTE-A,” 2013 Asia-Pacific Signal and
Information Processing Association Annual Summit and Conference, APSIPA 2013, pp. 2–7, 2013.
[9] A. Laya, L. Alonso, and J. Alonso-Zarate, “Contention resolution queues for massive machine type
communications in LTE,” IEEE International Symposium on Personal, Indoor and Mobile Radio Communications,
PIMRC, vol. 2015-Decem, pp. 2314–2318, 2015.
[10] A. Samir, M. M. Elmesalawy, A. S. Ali, and I. Ali, “An Improved LTE RACH Protocol for M2M
Applications,” Mobile Information Systems, vol. 2016, 2016.
[11] L. Tello-Oquendo, I. Leyva-Mayorga, V. Pla, J. Martinez-Bauset, J. R. Vidal, V. Casares-Giner, and
L. Guijarro, “Performance Analysis and Optimal Access Class Barring Parameter Configuration in
LTE-A Networks with Massive M2M Traffic,” IEEE Transactions on Vehicular Technology, vol. 67,
no. 4, pp. 3505–3520, 2018.
[12] J. P. Cheng, C. H. Lee, and T. M. Lin, “Prioritized Random Access with dynamic access barring for
RAN overload in 3GPP LTE-A networks,” 2011 IEEE GLOBECOM Workshops, GC Wkshps 2011,
pp. 368–372, 2011.
[13] 3GPP, “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation,”
Technical Specification (TS) 36.211, 3rd Generation Partnership Project (3GPP), 2020. Version
15.8.1.
[14] H. H. Hussein, H. A. Elsayed, and S. M. Abd El-kader, “Intensive Benchmarking of D2D communication
over 5G cellular networks: prototype, integrated features, challenges, and main applications,”
Wireless Networks, vol. 2, 2019.
[15] X.-H. Lin, “Solution to Congestion Problem for Massive MTC Devices Random Access in LTE-A
Network,” National Taiwan University of Science and Technology, 2019.
[16] 3GPP, “LTE;Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial
Radio Access Network (E-UTRAN); Overall description; Stage 2,” Technical Specification (TS)
36.300, 3rd Generation Partnership Project (3GPP), 2020. Version 15.9.0.
[17] 3GPP, “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC);
Protocol specification,” Technical Specification (TS) 36.331, 3rd Generation Partnership Project
(3GPP), 2020. Version 15.8.0.
[18] 3GPP, “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Medium Access Control (MAC)
protocol specification,” Technical Specification (TS) 36.321, 3rd Generation Partnership Project
(3GPP), 2018. Version 15.2.0.
[19] R. Ratasuk, A. Prasad, Z. Li, A. Ghosh, and M. Uusitalo, “Recent advancements in M2M communications
in 4G networks and evolution towards 5G,” 2015 18th International Conference on Intelligence
in Next Generation Networks, ICIN 2015, pp. 52–57, 2015.
[20] R. Ratasuk, N. Mangalvedhe, D. Bhatoolaul, and A. Ghosh, “LTE-M Evolution Towards 5G Massive
MTC,” 2017 IEEE Globecom Workshops, GC Wkshps 2017 - Proceedings, vol. 2018-January, pp. 1–6,
2018.
[21] C. Bockelmann, N. Pratas, H. Nikopour, K. Au, T. Svensson, C. Stefanovic, P. Popovski, and A. Dekorsy,
“Massive machine-type communications in 5g: Physical and MAC-layer solutions,” IEEE Communications
Magazine, vol. 54, no. 9, pp. 59–65, 2016.
[22] N. E. Tarik Taleb, Andreas Kunz, “Machine type communications in 3GPP networks: Potential, challenges,
and solutions,” IEEE Communications Magazine, vol. 50, no. 3, pp. 178–184, 2012.
[23] M. K. Pedhadiya, R. K. Jha, and H. G. Bhatt, “Device to device communication: A survey,” Journal
of Network and Computer Applications, vol. 129, pp. 71 – 89, 2019.
[24] P. Gandotra, R. K. Jha], and S. Jain, “A survey on device-to-device (d2d) communication: Architecture
and security issues,” Journal of Network and Computer Applications, vol. 78, pp. 9 – 29, 2017.
[25] 3GPP, “Study on architecture enhancements to support Proximity-based Services (ProSe),” Technical
Report (TR) 22.703, 3rd Generation Partnership Project (3GPP), 2014. Version 12.0.0 Release 12.
[26] 3GPP, “Feasibility study for Proximity Services (ProSe),” Technical Report (TR) 22.803, 3rd Generation
Partnership Project (3GPP), 2015. Version 12.2.0 Release 12.
[27] F. Jameel, Z. Hamid, F. Jabeen, S. Zeadally, and M. A. Javed, “A survey of device-to-device communications:
Research issues and challenges,” IEEE Communications Surveys Tutorials, vol. 20, no. 3,
pp. 2133–2168, 2018.
[28] E. Dahlman, S. Parkvall, and J. Skold, 4G, LTE-advanced Pro and the Road to 5G. Academic Press,
2016.
[29] 3GPP, “Proximity-based services (ProSe); Stage 2,” Technical specification (TS) 22.703, 3rd Generation
Partnership Project (3GPP), 2018. Version 15.1.0 Release 15.
[30] 3GPP, “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio
transmission and reception,” Technical Specification (TS) 36.101, 3rd Generation Partnership Project
(3GPP), 2020. Version 15.9.0.
[31] A. Biral, M. Centenaro, A. Zanella, L. Vangelista, and M. Zorzi, “The challenges of m2m massive
access in wireless cellular networks,” Digital Communications and Networks, vol. 1, no. 1, pp. 1–19,
2015.
[32] L. M. Bello, P. Mitchell, and D. Grace, “Application of q-learning for rach access to support m2m
traffic over a cellular network,” in European Wireless 2014, pp. 1–6, VDE, 2014.
[33] R. Suzuki and H. Shimodaira, “Pvclust: an r package for assessing the uncertainty in hierarchical
clustering,” Bioinformatics, vol. 22, no. 12, pp. 1540–1542, 2006.
[34] A. K. Jain, “Data clustering: 50 years beyond k-means,” Pattern recognition letters, vol. 31, no. 8,
pp. 651–666, 2010.