隨著智慧型行動裝置數量的迅速成長，帶動了行動通訊網路多媒體應用服務的興起，快速增長了行動通訊網路的存取需求，導致基地台 (evolved NodeB; eNB) 的負載也越來越高，使得新世代的行動通訊網路必須要以良好的通訊品質、頻譜利用率、網路覆蓋率、裝置節能與應用服務等面向進行改善與發展，提供用戶在存取行動通訊網路時的服務品質。

裝置間 (device-to-device; D2D) 通訊技術，呼應了目前的改善需求與發展趨勢，提供用戶複用頻譜直接進行通訊，不需經由基地台轉送資料，且為了解決用戶通道品質不佳的問題，又提出了裝置間中繼 (relay) 通訊。

本論文主要是改善通道品質不佳之用戶的通訊，透過多對一的裝置間中繼通訊，以我們提出的投票式分群機制先過濾需要中繼通訊之用戶，並運用改良式K-Means演算法，檢視用戶特徵並結合成一群，接著對用戶調整發射功率，降低訊號干擾與耗電量等問題。結束分群後，再根據我們修改的比例式公平 (proportional fair; PF) 排程演算法，作為分配資源的策略以利後續的傳輸。

由模擬結果顯示，本論文使得83% 需要透過中繼通訊之用戶，能被其最佳的中繼用戶服務，也因為我們提出的資源分配策略，除了重複利用可用的頻譜資源，同時也維持了系統內用戶的傳輸公平性，相較於其他的方法，在傳輸量與封包傳送的成功率都有著更好的表現，尤其是與一般的裝置間中繼通訊相比，可提升最多約31% 的傳輸量。

Along with the rapid development of smart device, the network traffic flow increase flourishing due to the mobile application and multimedia services. It cause more and more traffic loads on a base station. Therefore, the new generation of cellular network must increase spectrum utilization, network coverage, energy efficiency and application services for performance to provide a good quality of communication service.

In order to alleviate the load of base station, the device-to-device (D2D) communication technology is proposed to an efficient way to offload the data traffic. D2D enable users directly communications between each other without through base stations. However, in order to improve the issue of users which have poor channel quality, there is a kind of relay-assisted D2D communication proposed.

In this paper, the scenario is based on the relay-assisted D2D communication. We propose a novel voting-based grouping scheme to improve the performance of users who have poor channel quality. First, we find the users who need to use relay-assisted D2D communication. And then, we modify the k-means clustering to check the feature between each users, and make the users who have similar feature to each other in the same group. Last, we use power control scheme to reduce the signal interference and the energy consumption of devices. After the grouping scheme, we modify the proportional fair scheduling algorithm to become our resource allocation strategy before users transmit data.

Simulation results show that our scheme can make about 83% users who need to use relay-assisted D2D communication are service by the best relay user. According to our resource allocation strategy, it can exactly improve not only spectrum efficiency but also the user’s transmission fairness in the system. In comparison with the other three schemes, our scheme has excellent performance in throughput and reduces user’s packet drop ratio in the system. Especially compared to the throughput of traditional relay-assisted D2D communication, our scheme can increase about 31% at most.

[1] Cisco, “The Cisco Visual Networking Index (VNI) Global Mobile Data Traffic Forecast Update,” White Paper, Feb. 2017. Available: http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html
[2] C. H. Yu et al, “Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks” in IEEE Trans. Wireless Commun., vol. 10, no. 8, pp. 2752-2763, Aug. 2011.
[3] 3GPP, “Overall description (Release 9),” TS 36.300 V9.10.0, Jun. 2013.
[4] A. Ghosh et al, “LTE-Advanced: Next-Generation Wireless Broadband Technology,” in IEEE Wireless Commun., vol. 17, no. 3, pp. 10-22, Jun. 2010.
[5] H. Holma and A. Toskala, “LTE for UMTS：OFDMA and SC-FDMA Based Radio Access,” Apr. 2009.
[6] M.I. Salman et al, “Radio resource management (RRM) for green 3GPP long term evolution (LTE) cellular networks: review, and trade-offs,” in The Institution of Electronics and Telecommunication Engineers (IETE) Technical Review, vol. 30, no. 3, pp. 257-269, 2013.
[7] 3GPP, “Further advancements for E-UTRA physical layer aspects (Release 9),” TR 36.814 V9.2.0, Mar. 2017.
[8] 3GPP, “Physical Channels and Modulation (Release 14),” TS 36.211 V14.2.0, Mar. 2017.
[9] N. Abu-Ali et al, “Uplink scheduling in lte and lte-advanced: Tutorial, survey and evaluation framework,” in IEEE Communications Surveys & Tutorials, vol. 16, no. 3, pp. 1239-1265, 2014.
[10] H. Kuribayashi et al, “A Mobility-Based Mode Selection Technique for Fair spatial Dissemination of Data in Multi-Channel Device-to-Device Communication,” in IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, 2016.
[11] S. Lee et al, “Proportional fair frequency-domain packet scheduling for 3GPP LTE uplink,” in IEEE INFOCOM, Rio de Janeiro, Brazil, 2009, pp. 2611-2651.
[12] S. Zhenqi et al, “Research on uplink scheduling algorithm of massive m2m and h2h services in lte,” in IET International Conference on Information and Communications Technologies (IETICT 2013), Beijing, China, 2013, pp. 365-369.
[13] M. Salah et al, “Evaluating uplink schedulers in lte in mixed traffic environments,” in IEEE International Conference on Communications (ICC), Kyoto, Japan, 2011.
[14] S. AlQahtani and M. Alhassany, “Comparing Different LTE Scheduling Schemes,” in IEEE 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, Italy, 2013, pp. 264-269.
[15] Y. Wang and C. Chuang, “Efficient eNB Deployment Strategy for Heterogeneous Cells in 4G LTE Systems,” Computer Networks, vol. 79, no. 14, pp. 297-312, Jan. 2015.
[16] H. Zarrinkoub, “Understanding LTE with MATLAB: from mathematical modeling to simulation and prototyping,” in John Wiley & Sons, 2014.
[17] A. Damnjanovic et al, “A survey on 3GPP heterogeneous networks,” in IEEE Wireless Commun., vol. 18, no. 3, Jun. 2011.
[18] X. Lin et al, “An overview of 3GPP device-to-device proximity services,” in IEEE Communications Magazine, vol. 52, no. 4, pp. 40 - 48, Apr. 2014.
[19] 3GPP, “Proximity-based services (ProSe) (Release 12),” TS 23.303 V12.8.0, Mar. 2016.
[20] Y. Cao, T. Jiang, and C. Wang, “Cooperative Device-to-Device Communications In Cellular Networks,” in IEEE Wireless Commun., vol. 22, no. 3, pp. 124-129, Jun. 2015.
[21] L. Song et al, “Game-theoretic resource allocation methods for device-to-device communication,” in IEEE Wireless Commun., vol. 21, no. 3, pp. 136-144, Jun. 2014.
[22] Z. Chen et al, “Load balancing for D2D-based relay communications in heterogeneous network,” in IEEE 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Mumbai, Indian, 2015, pp. 23-29.
[23] F. Chiti et al, “Interference aware approach for D2D communications,” in IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, 2016.
[24] C. Yeh et al, “LTE-D broadcast with distributed interference-aware D2D resource allocation,” in IEEE 7th International Conference on Ubiquitous and Future Networks (ICUFN), Sapporo, Japan, 2015, pp. 165-170.
[25] M. Zhao et al, “A Two-Stages Relay Selection And Resource Allocation Joint Method for D2D Communication System,” in IEEE Wireless Communications and Networking Conference (WCNC), Doha, Qatar, Sep. 2016.
[26] S. Mumtaz et al, "Energy efficient interference-aware resource allocation in LTE-D2D communication", in IEEE International Conference on Communications (ICC), Sydney, Australia, 2014, pp. 282-287.
[27] Lili Wei et al, “System-level simulations for multi-hop D2D communications overlay LTE networks,” in IEEE International Conference on Computing, Networking and Communications (ICNC), Kauai, USA, 2016.
[28] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, vol. 1, 1967, pp. 281-297.
[29] K. Thilina et al, “Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks,” in IEEE Journal on Selected Areas in Commun., vol. 31, no. 11, Nov. 2013.
[30] C. Piech, “K Means,” Artificial Intelligence: Principles and Techniques, Stanford University, Available: http://stanford.edu/~cpiech/cs221/handouts/kmeans.html