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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蘇炫榮 | |
| dc.contributor.author | Po-Ju Tseng | en |
| dc.contributor.author | 曾柏儒 | zh_TW |
| dc.date.accessioned | 2021-06-08T02:11:06Z | - |
| dc.date.copyright | 2016-02-16 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-01-26 | |
| dc.identifier.citation | [1] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. N. Marzetta, O.
Edfors, and F. Tufvesson, 'Scaling up MIMO: opportunities and chal- lenges with very large arrays,' IEEE Signal Process. Mag., vol. 30, pp. 40-60, Jan. 2013. [2] H. Yang and T. N. Marzetta, 'Performance of conjugate and zero-forcing beamforming in large-scale antenna systems,' IEEE J. Sel. Areas Com- mun.,vol. 31, no. 2, pp. 172-179, Feb. 2013. [3] Xiongbin Rao; Lau, V.K.N. 'Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems', Signal Processing, IEEE Transactions on, On page(s): 3261 - 3271 Volume: 62, Issue: 12, June15, 2014. [4] Xiongbin Rao; Lau, V.K.N.; Xiangming Kong 'CSIT estimation and feedback for FDD multi-user massive MIMO systems', Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Con- ference on, On page(s): 3157 - 3161. [5] Y. Zhou,M. Herdin, A. M. Sayeed, and E. Bonek, Experimental study of MIMO channel statistics and capacity via the virtual channel representation, Univ.Wisconsin-Madison,Madison,WI, USA, Tech. Rep., Feb. 2007. [6] P. Kyritsi, D. C. Cox, R. A. Valenzuela, and P. W.Wolniansky, Cor- relation analysis based onMIMO channel measurements in an indoor environment, IEEE J. Sel. Areas Commun., vol. 21, no. 5, pp. 713720, 2003. [7] F. Kaltenberger, D. Gesbert, R. Knopp, and M. Kountouris, Correlation and capacity of measured multi-user MIMO channels, in Proc. IEEE Int. Symp. Personal, Indoor, Mobile Radio Commun. (PIMRC), 2008, pp. 15. [8] J. Hoydis, C. Hoek, T.Wild, and S. ten Brink, Channel measurements for large antenna arrays, in Proc. IEEE Int. Symp. Wireless Commun. Systems (ISWCS), 2012, pp. 811815. [9] X. Gao, O. Edfors, F. Rusek, and F. Tufvesson, Linear pre-coding per- formance in measured very-large MIMO channels, in Proc. IEEE Veh. Technol. Conf. (VTC), 2011, pp. 15. [10] Jin-Hao Li and Hsuan-Jung Su ,Opportunistic feedback reduction for multiuser MIMO broadcast channel with orthogonal beamforming, IEEE TRANSACTIONS on WIRELESS COMMUNICATIONS, 2014. [11] Hien Quoc Ngo; Ashikhmin, A.; Hong Yang; Larsson, E.G.; Marzetta, T.L. 'Cell-Free Massive MIMO: Uniformly great service for everyone',Signal Processing Advances in Wireless Communications(SPAWC), 2015 IEEE 16th International Workshop on,On page(s): 201- 205. [12] Zhao Li; PeifengLi; Shin, K.G. 'MU-MIMO downlink scheduling based on users' correlation and fairness',Personal, Indoor, and Mobile Radio Communication (PIMRC), 2014 IEEE 25th Annual International Sym- posium on,On page(s): 407-412. [13] Cacciapuoti, A.S.; Akyildiz, I.F.; Paura, L. 'Correlation-Aware User Selection for Cooperative Spectrum Sensing in Cognitive Radio Ad Hoc Networks',Selected Areas in Communications, IEEE Journal on,On page(s): 297 -306 Volume: 30, Issue: 2, February 2012. [14] Guoshen Yu; Sapiro, G. 'Statistical Compressed Sensing of Gaussian Mixture Models', Signal Processing, IEEE Transactions on, On page(s): 5842 - 5858 Volume: 59, Issue: 12, Dec. 2011. [15] D. Tse and P.Viswanath, Fundamentals ofWireless Communication. Cambridge, U.K.: Cambridge Univ. Press, 2005. [16] E. J. Candes, 'The Restricted Isometry Property and Its Implications for Compressed Sensing,' C. R. Acad. Sci. Paris, Ser. I, vol. 346, no. 12, pp. 589-592, Dec. 2008. [17] Bennett Eisenberg & Rosemary Sullivan, Why Is the Sum of Indepen- dent Normal Random Variables Normal?, Mathematics Magazine, Dec. 2008, 362-366 [18] Papoulis, Athanasios; Pillai, S. (2001) Probability, Random Variables and Stochastic Processe. [19] M. Shakil, B. M. Golam Kibria, Exact Distribution of the Ratio of Gamma and Rayleigh Random Variables, Pakistan Journal of Statistics and Operation Research 07/2006. [20] Laurenson, Dave (1994). 'Nakagami Distribution'. Indoor Radio Chan- nel Propagation Modelling by Ray Tracing Techniques. Retrieved 2007- 08-04. [21] S. Ghahramani, 'Fundamentals of probability with stochastic pro- cesses,' 3rd ed. Prentice Hall, 2005. [22] Yunxia Chen; Tellambura, C. 'Distribution functions of selection com- biner output in equally correlated Rayleigh, Rician, and Nakagami-m fading channels', Communications, IEEE Transactions on, On page(s): 1948 - 1956 Volume: 52, Issue: 11, Nov. 2004. [23] J. N. Pierce and S. Stein, Multiple diversity with nonindependent fading, Proc. IRE, vol. 48, pp. 89-104, Jan. 1960. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19645 | - |
| dc.description.abstract | 由於在不久後的將來，會有越來越多的裝置加入通訊的行列，如何在有限的頻寬終取得更高的傳輸速度(throughput) 以及更穩定的傳輸品質(QoS)，大規模多輸入多輸出(Massive-MIMO) 是目前被認為能夠在未來5G 無線通訊系統中達到此目標的關鍵技術。大規模多輸入多輸出是指在基地台配置數百根天線，以達到更高的多樣性(diversity gain) 與多工性(multiplexing gain)，能夠同時在同頻段服務數十個裝置。但要在避免裝置間干擾的前提下得到這些好處，基地台必須取得那些裝置的即時通到資訊(CSI)。
目前的研究大多傾向於在分時雙工模式(TDD) 中，利用通道互惠(channel reciprocity) 的性質，讓通道量測的問題簡化，只需要基地台量測各裝置傳送的已知訊號(pilots) 即可，然而在近期的許多研究中顯示，通道互惠的成立與否仍備受質疑。而在分頻雙工模式(FDD) 中，基地台的每根天線都須先傳送一段已知訊號，裝置再將量測結果回傳給基地台。然而在基地台具備數百根天線的情況下，傳統這樣傳送和回傳的方式會需要大量的頻寬與時間，以至於難以實現。 此研究是藉由利用基地台天線間距離相近與裝置間若距離靠近導致的相關性(correlation)，得以有壓縮的可行性，利用壓縮感知(compressive sensing) 技術將已知訊號傳送與回傳量大幅降低，使在分頻雙工模式中得以實現。並進一步將基地台挑選裝置的過程(sceduling stage) 加以考慮，提出一套在不太影響傳輸速度的前提下，大幅減少裝置回傳數量的方法。 | zh_TW |
| dc.description.abstract | The concept of massive MIMO is a system where a base station (BS) equipped with a large number of antennas, say 100 antennas or more, serves several users in the same frequency band simultaneously. However, to harvest the benefi ts, BS must acquire instantaneous channel state information (CSI) in order to suppress inter-user interference. By exploiting channel reciprocity under time division duplexing (TDD) operation, CSI acquisition at BS is feasible. Nonetheless, recent researches show that the assumption of channel reciprocity might not be practical. On the other hand, under frequency division duplexing (FDD) operation, channel estimation is much more challenging since resources required by pilot training and feedback overhead both grow linearly with the number of BS antennas.
In our work, we exploit the correlation between BS antennas and correlation between UEs due to their close distance. Such correlation means that we can reduce pilot training and feedback overhead signi cantly by using compressive sensing, make CSI acquisition at BS under FDD operation feasible. We further take the scheduling stage into consideration, by combining the order statistics, we can further reduce UEs feedback loads with very little total rate loss. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T02:11:06Z (GMT). No. of bitstreams: 1 ntu-105-R02942073-1.pdf: 3906182 bytes, checksum: 780ff60577ad43a9ab25d8222a19e38b (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . . 7 1.6 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . 9 1.7 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 System Model and Problem Formulation 11 2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.1 Multi-User Massive MIMO System . . . . . . . . . . . 12 2.1.2 Joint Channel Sparsity Model . . . . . . . . . . . . . . 14 2.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.1 CSIT Estimation and Feedback Reduction . . . . . . . 17 2.2.2 Feedback Load Reduction . . . . . . . . . . . . . . . . 18 3 CSIT Estimation and Feedback Reduction 20 3.1 CSIT Estimation and Feedback Reduction . . . . . . . . . . . 20 3.1.1 Pilot Sensing Matrix . . . . . . . . . . . . . . . . . . . 21 3.1.2 Optimal Decoder Design from Statistical Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2 Feedback Load Reduction . . . . . . . . . . . . . . . . . . . . 26 3.2.1 CQI Distribution . . . . . . . . . . . . . . . . . . . . . 26 3.2.2 Order Statistics of i.i.d. Random Variables . . . . . . . 29 3.2.3 Order Statistics of Correlated Random Variables . . . . 30 3.2.4 Compressive Sensing . . . . . . . . . . . . . . . . . . . 35 3.2.5 Sum Rate Loss Analysis and Feedback Criterion . . . . 39 3.2.6 Feedback Load Analysis . . . . . . . . . . . . . . . . . 41 4 Numerical Results 44 4.1 CSIT Estimation and Feedback Reduction . . . . . . . . . . . 45 4.2 Feedback Load Reduction . . . . . . . . . . . . . . . . . . . . 50 5 Conclusion 56 Bibliography 58 | |
| dc.language.iso | en | |
| dc.title | 在分頻雙工模式下多用戶之大規模多輸入多輸出系統中降低傳送端通道資訊估計與回傳量之技術 | zh_TW |
| dc.title | CSIT Estimation and Feedback Reduction for FDD Multi-user
Massive MIMO System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林士駿,李佳翰,徐福得 | |
| dc.subject.keyword | 大規模多輸入多輸出,壓縮感知,順序統計, | zh_TW |
| dc.subject.keyword | Massive MIMO,Compressive sensing,Order statistics, | en |
| dc.relation.page | 61 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2016-01-26 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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