臺灣博碩士論文加值系統

在新一代通訊系統中，接力資源分配方式將會對效能影響甚巨。接力資源分配是指在網路環境中，將接力點分配給傳輸效能不好的使用者，用以提升這些使用者的傳輸速度。目前為止，已經有許多研究文獻對此議題提出數個利用集中式演算法的解決方法，但這些方式讓基地台必須收集所有網路系統中的通道資訊，造成基地台嚴重負擔，並且未完全契合設計接力點的真正功能---提升原本傳輸效能較差的使用者。
這份研究提出一個基於離散式演算法的解決方案。透過隨機自動學習機制，使用者根據網路環境回傳的效能值，自行選擇適合的傳輸方式。實驗結果證明我們提出的分配演算法，有良好的收斂特性與負載平衡特性，效能方面也十分卓越。

Relay assignment is a crucial issue and it affects the performance
of cooperative communication networks, which means assigning the
proper relay nodes (RN) to cell-edge users (UE) in order to exploit
the spatial diversity through relay nodes and improve cell-edge performance.
Several assignment strategies have been proposed in literatures.
Nevertheless, the previous works solved this problem by centralized
way, where base station (eNB) will serve as a control node to
collect the channel conditions and location information and make the
final decision. Maximize aggregate performance is a typical objective
of centralized assignment strategy to improve system capacity in the
network using Hungarian algorithm. Another optimally centralized
algorithm, max-min feature, is to maximize the minimum data rate
among all users. It is shown to find the optimal objective regardless
the initial relay node. Although centralized methods could have better
performance for system, it takes high operation and maintenance
tasks in eNB, where it infringes the operational requirements of smallcell
enhancements. This work is motivated to propose a distributed
algorithm, which means that each UE would choose its own appropriate
RN individually. To achieve this goal, we proposed a strategy
called ”Distributed Relay Assignment by Learning” (RAL) based on
stochastic learning automata. Under the new assignment algorithm,
not only the local performance is preserved, but also the benefited
probability of UEs is raised obviously.

1 Introduction 1
2 BackgroundInformation 5
2.1 Layer 1 Relay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Layer 2 Relay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 RelatedWorks 11
3.1 Maximize the Aggregate Performance (OPRA) . . . . . . . . . . . 12
3.2 Max-min the performance among all UEs (ORA) . . . . . . . . . 13
3.3 Greedy Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.4 Observation of previous works . . . . . . . . . . . . . . . . . . . . 13
4 System Model and Proposed Algorithm 15
4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 Stochastic Learning Automata . . . . . . . . . . . . . . . . . . . . 16
4.3 Proposed Algorithm - Distributed Relay Assignment by Learning
(RAL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3.1 Algorithm Description . . . . . . . . . . . . . . . . . . . . 18
4.3.2 Learning algorithm . . . . . . . . . . . . . . . . . . . . . . 19
5 Parameter Settings and Simulation Results 21
5.1 Parameter settings . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.2 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6 Conclusions 33
Bibliography 35

[1] 3GPP, “Report of 3GPP TSG RAN IMT-Advanced Workshop,” REV-080060, Apr. 2008.
[2] T. Cover and A. Gamal, “Capacity theorems for the relay channel,” in IEEE Trans. Inf. Theory, vol. 25, Sep. 1979, pp. 572–584.
[3] E. Lang, S. Redana, and B. Raaf, “Business Impact of Relay Deployment for Coverage Extension in 3GPP LTE-Advanced,” in IEEE ICC, Jun. 2009, pp. 1 –5.
[4] A. Bou Saleh, S. Redana, B. Raaf, T. Riihonen, J. Hamalainen, and R.Wichman, “Performance of Amplify-and-Forward and Decode-and-Forward Relays in LTE-Advanced,” in IEEE 70th VTC 2009-Fall, Sep. 2009, pp. 1 –5.
[5] X. Fang, Dejun Yang and G. Xue, “OPRA: Optimal Relay Assignment for
Capacity Maximization in Cooperative Networks,” in IEEE ICC, Jun. 2011, pp. 1–6.
[6] J. Cai, X. Shen, J. Mark, and A. Alfa, “Semi-Distributed User Relaying Algorithm for Amplify-and-ForwardWireless Relay Networks,” IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1348 –1357, Apr. 2008.
[7] S. Sharma, Y. Shi, Y. Hou, and S. Kompella, “An Optimal Algorithm for Relay Node Assignment in Cooperative Ad Hoc Networks,” IEEE/ACM Trans. Netw., vol. 19, no. 3, pp. 879 –892, Jun. 2011.
[8] T. Nakamura, S. Nagata, A. Benjebbour, Y. Kishiyama, T. Hai, S. Xiaodong, Y. Ning, and L. Nan, “Trends in small cell enhancements in lte advanced,” IEEE Commun. Mag., vol. 51, no. 2, pp. 98–105, 2013.
[9] Y. Yang, H. Hu, J. Xu, and G. Mao, “Relay technologies for WiMax and LTE-advanced mobile systems,” IEEE Commun. Mag., vol. 47, no. 10, pp. 100 105, Oct. 2009.
[10] A. Ghosh, R. Ratasuk, B. Mondal, N. Mangalvedhe, and T. Thomas, “LTEadvanced: next-generation wireless broadband technology,” IEEE Wireless
Commun., vol. 17, no. 3, pp. 10 –22, Jun. 2010.
[11] K. Loa, C.-C. Wu, S.-T. Sheu, Y. Yuan, M. Chion, D. Huo, and L. Xu, “IMT-advanced relay standards [WiMAX/LTE Update],” IEEE Commun. Mag., vol. 48, no. 8, pp. 40 –48, Aug. 2010.
[12] K. T. T. Steven W. Peters, Ali Y. Panah and J. Robert W. Heath, “Relay Architectures for 3GPP LTE-Advanced,” EURASIP Journal on Wireless Communications and Networking, vol. 2009, p. 14, 2009.
[13] R. Yi Zhao, Adve and T. J. Lim, “Improving Amplify-and-Forward Relay Networks: Optimal Power Allocation versus Selection,” in IEEE Int. Symp. Inf. Theory, Jul. 2006, pp. 1234 –1238.
[14] Y. Jing and H. Jafarkhani, “Single and multiple relay selection schemes and
their achievable diversity orders,” IEEE Trans. Wireless Commun., vol. 8, no. 3, pp. 1414 –1423, Mar. 2009.
[15] H. Halabian, I. Lambadaris, C.-H. Lung, and A. Srinivasan, “Throughputoptimal relay selection in multiuser cooperative relaying networks,” in MILCOM, Nov. 2010, pp. 507 –512.
[16] S. Kadloor and R. Adve, “Optimal Relay Assignment and Power Allocation in Selection Based Cooperative Cellular Networks,” in IEEE ICC, Jun. 2009, pp. 1 –5.
[17] X. Zhang, A. Ghrayeb, and M. Hasna, “On Relay Assignment in Network-Coded Cooperative Systems,” IEEE Trans. Wireless Commun., vol. 10, no. 3, pp. 868 –876, Mar. 2011. 12
[18] P. Mogensen, W. Na, I. Kovacs, F. Frederiksen, A. Pokhariyal, K. Peder- sen, T. Kolding, K. Hugl, and M. Kuusela, “Lte capacity compared to the shannon bound,” in IEEE 65th VTC2007-Spring., 2007, pp. 1234–1238. 16, 21
[19] M. Thathachar and P. Sastry, “A new approach to the design of reinforce- ment schemes for learning automata,” IEEE Trans. Syst. Man Cybern., vol. SMC-15, no. 1, pp. 168–175, 1985. 16, 19
[20] P. S. Sastry, V. V. Phansalkar, and M. Thathachar, “Decentralized learning of nash equilibria in multi-person stochastic games with incomplete informa- tion,” IEEE Trans. Syst. Man Cybern., vol. 24, no. 5, pp. 769–777, 1994. 17, 19
[21] Y. Xu, J. Wang, Q. Wu, A. Anpalagan, and Y.-D. Yao, “Opportunistic spec- trum access in unknown dynamic environment: A game-theoretic stochastic learning solution,” IEEE Trans. Wireless Commun., vol. 11, no. 4, pp. 1380– 1391, 2012. 18
[22] Y. Xu, Q. Wu, and J. Wang, “Game theoretic channel selection for oppor- tunistic spectrum access with unknown prior information,” in IEEE ICC, 2011, pp. 1–5. 18
[23] Y. Chen, Q. Zhao, and A. Swami, “Joint design and separation principle for opportunistic spectrum access in the presence of sensing errors,” IEEE Trans. Inf. Theory, vol. 54, no. 5, pp. 2053–2071, 2008. 18
[24] 3GPP, “Further Advancements for E-UTRA Physical Layer Aspects,” TR 36.814, Feb. 2009. 21
[25] L. Rong, S. Elayoubi, and O. Haddada, “Impact of Relays on LTE-Advanced Performance,” in IEEE ICC, May 2010, pp. 1 –6. 21
[26] Z. Ma, Y. Zhang, K. Zheng, W. Wang, and M. Wu, “Performance of 3GPP LTE-Advanced networks with Type I relay nodes,” in 2010 5th Int. ICST Conf. CHINACOM, Aug. 2010, pp. 1 –5. 21