臺灣博碩士論文加值系統

異質移動性蜂巢網路已經充斥在我們的生活中,提供行動用戶各種語音和數據服務。該異質性蜂巢網路是由兩層的蜂巢網路所組成,其中各層的基地台位置皆具隨機性,且擁有特定的傳輸功率、路徑損耗指數、密度及偏異值。舉例來說,相較於大型基地台,小型基地台擁有較低的傳輸功率、較高的密度及正偏異值,這些特性都會讓原本連結大型基地台的用戶更積極地選擇輕負載的小型基地台。而行動裝置的通訊可能在發起通訊或切換通訊 (我們稱為換手) 的過程中因為基地台資源不足被迫中斷。在過去幾年,已有許多文獻在傳統六角形蜂巢網路中以時間的角度發展並分析換手的特性。然而,對於現今異質移動性蜂巢網路,基地台規律的分配已無法確實呈現實際位置的不確定性。本篇論文將至今尚無被探討過的基地台資源考慮進換手方法,並利用隨機幾何的技術建置網路模型,來研究分析在兩層異質移動性蜂巢網路中的通訊阻擋機率。通訊阻擋機率代表通訊因為資源不足而被強制終止的機率。我們根據從 OpenCellID中的大型基地台地理位置數據進行模擬實驗,來比較並評估提出的隨機幾何模型與現實環境的差異。

Mobile heterogeneous cellular networks (HCNs) have been integrated with human life to provide various kinds of voice and data services to mobile users (MUs). The HCNs is modeled as a two-tier cellular network where each tier’s base stations (BSs) are randomly located and have a particular transmit power, path loss exponent, spatial density, and bias towards admitting MUs. For instance, when compared with macro BSs (mBSs), small BSs (sBSs) would usually have lower transmit power, higher spatial density, and a positive bias so that users in mBS are actively encouraged to use the more lightly loaded sBSs. Due to resource insufficiency, the services might not be completed during the call initiation or the handover process, and the key to maintaining service continuation depends on how to handle the handover process. Over the past few years, many literatures developed and analyzed the models for understanding the characteristics of handovers are investigated from time perspective with the assumption of homogeneous hexagonal cells. However, BSs are not deployed regularly in recent mobile HCNs such as LTE-Advanced, the analytical model including uncertainty of location is emerging. This paper considers different BSs’ cell size, which has not been considered in handover method yet, and adopts stochastic geometry perspective model to analyze a study of call blocking probability for two-tier HCNs where the performance of handover process can be evaluated. The call blocking probability, we present, is derived by resource insufficiency. We also conduct simulation experiments according to mBSs’ data from OpenCellID to comprehensively evaluate the proposed stochastic geometry model in the realistic environment.

Chinese Abstract...................................... 1
Abstract................................................... 2
TableofContents....................................... 3
ListofTables.............................................. 5
List of Illustrations ................................... 6
1 Introduction.......................................... 8
2 RelatedWork........................................ 13
2.1 Time-basedModel ............................ 13
2.2 Space-basedModel ........................... 14
2.2.1 SojournTime................................... 15
2.2.2 HandoverRates................................ 16
2.2.3 Pointprocess ................................... 16
2.2.4 Booleanmodel ................................. 17
3 SystemModel.......................................... 18
3.1 NetworkModel .................................... 18
3.2 ResourceAllocationModel..................... 19
3.3 ChannelModel ..................................... 20
3.4 BiasedReceivedPower............................ 20
3.5 MobilityModel ...................................... 21
3.6 PerformanceMetric................................ 21
4 AnalyticalClosed-formExpressions ........... 23
4.1 Notation................................................ 23
4.2 CallBlocking ......................................... 25
4.3 CallArrivalFailureProbability .................. 26
4.3.1 Validation........................................... 29
4.4 HandoverRate ....................................... 30
4.5 CallBlockingProbability ......................... 34
4.5.1 Validation........................................... 35
5 PerformanceEvaluation ............................ 39
5.1 SimulationScenario................................ 39
5.2 Results.................................................. 40
6 Conclusion............................................... 42
References................................................... 43

[1] H.-S. Jo, Y. J. Sang, P. Xia, and J. G. Andrews, “Heterogeneous cellular net- works with flexible cell association: A comprehensive downlink SINR analysis,” IEEE Trans. Wireless Commun., vol. 11, no. 10, pp. 3484–3495, Oct. 2012.
[2] S. Sadr and R. S. Adve, “Handoff rate and coverage analysis in multi-tier het- erogeneous networks,” IEEE Trans. Wireless Commun., vol. 14, no. 5, pp. 2626–2638, Jan. 2015.
[3] W. Bao and B. Liang, “Stochastic geometric analysis of user mobility in het- erogeneous wireless networks,” IEEE J. Sel. Areas Commun., vol. 33, no. 10, pp. 2212 – 2225, May 2015.
[4] B. Fang and W. Zhou, “An effective handover analysis for the randomly dis- tributed heterogeneous cellular networks,” arXiv preprint arXiv:1501.01450, Jan. 2015.
[5] C. B. Rodriguez-Estrello, G. Hernandez-Valdez, and F. A. Cruz-Perez, “System- level analysis of mobile cellular networks considering link unreliability,” IEEE Trans. Veh. Technol., vol. 58, no. 2, pp. 926–940, Feb. 2009.
[6] Y. B. Lin, “Performance modeling for mobile telephone networks,” IEEE Netw., vol. 1, no. 6, pp. 63–68, Nov.-Dec. 1997.
[7] Y. Fang, I. Chlamtac, and Y. B. Lin, “Call performance for a PCS network,” IEEE J. Sel. Areas Commun., vol. 15, no. 8, pp. 1568–1581, Oct. 1997.
[8] ——, “Channel occupancy times and handoff rate for mobile computing and PCS networks,” IEEE Trans. Commun., vol. 47, no. 6, pp. 679–692, June 1998.
[9] C.-H. Wu, H.-P. Lin, and L.-S. Lan, “A new analytic framework for dynamic mobility management of PCS networks,” IEEE Trans. Mobile Comput., vol. 1, no. 3, pp. 208–220, July 2002.
[10] J. Wang, Q.-A. Zeng, and D. P. Agrawal, “Performance analysis of a preemptive and priority reservation handoff scheme for integrated service-based wireless mobile networks,” IEEE Trans. Mobile Comput., vol. 2, no. 1, pp. 65–75, Jan. 2003.
[11] V. A. Aalo and G. P. Efthymoglou, “Evaluation of call dropping probability for a heterogeneous wireless network with uniformly distributed handoff failure rates,” in Proc. IEEE GLOBECOM 2010, Dec. 2010.
[12] I. F. Akyildiz, Y. B. Lin, W. R. Lai, and R. J. Chen, “A new random walk model for PCS networks,” IEEE J. Sel. Areas Commun., vol. 18, no. 7, pp. 1254–1260, July 2000.
[13] D. Stoyan, W. S. Kendall, and J. Mecke, Stochastic geometry and its applica-
tions. John Wiley and Son, 1995.
[14] M. Haenggi, J. G. Andrews, F. Baccelli, O. Dousse, and M. Franceschetti, “Stochastic geometry and random graphs for the analysis and design of wireless networks,” IEEE J. Sel. Areas Commun., vol. 27, no. 7, pp. 1029–1045, Sept. 2009.
[15] M. Haenggi, “Mean interference in hard-core wireless networks,” IEEE Com- mun. Lett., vol. 15, no. 8, pp. 792–794, Aug. 2011.
[16] E. Zola and F. Barcelo-Arroyo, “Probability of handoff for users moving with the random Waypoint mobility model,” in Proc. IEEE LCN 2011, Oct. 2011, pp. 187–190.
[17] R. K. Ganti, F. Baccelli, and J. G. Andrews, “Series expansion for interference in wireless networks,” IEEE Trans. Inf. Theory, vol. 58, no. 4, pp. 2194–2205, Apr. 2012.
[18] J. G. Andrews, F. Baccelli, and R. K. Ganti, “A tractable approach to coverage and rate in cellular networks,” IEEE Trans. Commun., vol. 59, no. 11, pp. 3122– 3134, Nov. 2011.
[19] X. Lin, R. K. Ganti, P. J. Fleming, and J. G. Andrews, “Towards understand- ing the fundamentals of mobility in cellular metworks,” IEEE Trans. Wireless Commun., vol. 12, no. 4, pp. 1686–1698, Apr. 2013.
[20] Y. Hong, X. Xu, M. Tao, J. Li, and T. Svensson, “Cross-tier handover analyses in small cell networks: A stochastic geometry approach,” in Proc. IEEE ICC 2015, June 2015, pp. 3429–3434.
[21] A. Merwaday, I. Giivenc, W. Saad, A. Mehbodniya, and F. Adachi, “Sojourn time-based velocity estimation in small cell poisson networks,” IEEE Commun. Lett., vol. 20, no. 2, Nov. 2015.
[22] R. Arshad, H. ElSawy, S. Sorour, T. Y. Al-Naffouri, and M.-S. Alouini, “Han- dover management in dense cellular networks: A stochastic geometry ap- proach,” arXiv preprint arXiv:1604.08552, Apr. 2016.
[23] K. Vasudeva, M. Simsek, D. Lopez-Perez, and I. Guvenc, “Analysis of han- dover failures in heterogeneous networks with fading,” arXiv preprint arXiv: 1507.01586, July 2015.
[24] A. Sgora and D. D. Vergados, “Handoff prioritization and decision schemes in wireless cellular networks: a survey,” IEEE Commun. Surveys Tuts., vol. 11, no. 4, pp. 57–77, 2009.
[25] S. Buyukcorak, G. K. Kurt, and O. Cengaver, “A probabilistic framework for estimating call holding time distributions,” IEEE Trans. Veh. Technol., vol. 63, no. 2, pp. 811–821, Feb. 2014.
[26] S. Shin, U. Lee, F. Dressler, and H. Yoon, “Analysis of cell sojourn time in heterogeneous networks with small cells,” IEEE Commun. Lett., vol. 20, no. 4, Nov. 2016.
[27] Y. J. Sang and K. S. Kim, “Load distribution in heterogeneous cellular net- works,” IEEE Commun. Lett., vol. 18, no. 2, Feb. 2014.
[28] “OpenCellID.” [Online]. Available: http://www.opencellid.org/
[29] C.-H. Lee, C.-Y. Shih, and Y.-S. Chen, “Stochastic geometry based models for modeling cellular networks in urban areas,” Wireless networks, vol. 19, no. 6, pp. 1063–1072, Aug. 2013.
[30] 3GPP, “E-UTRA: Further Advancements for E-UTRA Physical layer aspects,” 3GPP TR 36.814 v9.0.0, Mar. 2010.