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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電信工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49301
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dc.contributor.advisor王奕翔(I-Hsiang Wang)
dc.contributor.authorCheng-Yang Linen
dc.contributor.author林正洋zh_TW
dc.date.accessioned2021-06-15T11:22:47Z-
dc.date.available2017-08-26
dc.date.copyright2016-08-26
dc.date.issued2016
dc.date.submitted2016-08-19
dc.identifier.citation[1] “Realizing the full potential of government-held spectrum to spur economic growth,”President’s Council of Advisors on Science and Technology (PCAST) report, 2012.
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[5] V. Y. Tan, “Asymptotic estimates in information theory with non-vanishing error probabilities,” arXiv preprint arXiv:1504.02608, 2015.
[6] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE journal on selected areas in communications, vol. 23, no. 2, pp. 201–220, 2005.
[7] D. Natasha, P. Mitran, and V. Tarokh, “Achievable rates in cognitive radio channels,”Information Theory, IEEE Transactions on, vol. 52, no. 5, pp. 1813–1827, 2006.
[8] A. E. Gamal and Y.-H. Kim, Network information theory. Cambridge university press, 2011.
[9] M. H. Costa, “Writing on dirty paper (corresp.),” Information Theory, IEEE Transactions on, vol. 29, no. 3, pp. 439–441, 1983.
[10] W. Wu, S. Vishwanath, and A. Arapostathis, “Capacity of a class of cognitive radio channels: Interference channels with degraded message sets,” Information Theory, IEEE Transactions on, vol. 53, no. 11, pp. 4391–4399, 2007.
[11] A. Jovičić and P. Viswanath, “Cognitive radio: An information-theoretic perspective,”Information Theory, IEEE Transactions on, vol. 55, no. 9, pp. 3945–3958, 2009.
[12] G. Chung, S. Sridharan, S. Vishwanath, and C. S. Hwang, “On the capacity of overlay cognitive radios with partial cognition,” IEEE Transactions on Information Theory, vol. 58, no. 5, pp. 2935–2949, 2012.
[13] A. Goldsmith, S. A. Jafar, I. Marić, and S. Srinivasa, “Breaking spectrum gridlock with cognitive radios: An information theoretic perspective,” Proceedings of the IEEE, vol. 97, no. 5, pp. 894–914, 2009.
[14] I.-H. Wang and S. Diggavi, “Interference channels with bursty traffic and delayed feedback,” in Signal Processing Advances in Wireless Communications (SPAWC), 2013 IEEE 14th Workshop on. IEEE, 2013, pp. 205–209.
[15] C. E. Shannon, “Probability of error for optimal codes in a gaussian channel,” Bell System Technical Journal, vol. 38, no. 3, pp. 611–656, 1959.
[16] V. Strassen, “Asymptotische abschätzungen in shannons informationstheorie,” in Trans. Third Prague Conf. Inf. Theory, 1962, pp. 689–723.
[17] C. E. Shannon, “Certain results in coding theory for noisy channels’,” Information and control, vol. 1, no. 1, pp. 6–25, 1957.
[18] Y. Polyanskiy, Channel coding: non-asymptotic fundamental limits. Princeton University, 2010.
[19] T. M. Cover and J. A. Thomas, Elements of information theory. John Wiley & Sons, 2012.
[20] M. Hayashi, “Information spectrum approach to second-order coding rate in channel coding,” Information Theory, IEEE Transactions on, vol. 55, no. 11, pp. 4947–4966, 2009.
[21] P. N. Chen, “Berry-esseen theorem,” Lecture notes of Advanced Probability for Communications, 2016.
[22] Y. Polyanskiy, H. V. Poor, and S. Verdú, “Dispersion of gaussian channels,” in Information Theory, 2009. ISIT 2009. IEEE International Symposium on. IEEE, 2009, pp. 2204–2208.
[23] V. Y. Tan and M. Tomamichel, “The third-order term in the normal approximation for the awgn channel,”Information Theory, IEEE Transaction on, vol. 61, no. 5, pp. 2430–2438.
[24] E. MolavianJazi, “A unified approach to gaussian channels with finite blocklength,” Ph.D. dissertation, University of Notre Dame, 2014.
[25] E. MolavianJazi and J. N. Laneman,“A second-order achievable rate region for gaussian multi-access channels via a central limit theorem for functions,” Information Theory, IEEE Transactions on, vol. 61, no. 12, pp. 6719–6733, 2015.
[26] E. MolavianJazi and J. N. Laneman,“On the second-order cost of tdma for gaussian multiple access,” in 2014 IEEE International Symposium on Information Theory. IEEE, 2014, pp. 266–270. 95
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49301-
dc.description.abstract本論文考慮一個可實現的基於偵測頻譜分享系統,系統中包含不同
層級的次要使用者,在不同的次要使用者之間有偵測延遲,在訊息大
小有限的情形之下,偵測延遲會導致對較高層級使用者的干擾。此研
究目標為找到此系統中,不同層級的次要使用者傳輸率的極限。我們
提出一些傳輸方法,目的為解決偵測延遲所造成的干擾,並且比較採
用不同方法的表現。傳統上解決干擾問題,低階使用者利用避免干擾
或降低傳輸能量的方法,來減少對高階使用者的影響。採用這種方法
的收發端較容易實現,然而,若是偵測延遲的長度相較於訊息長度已
不可忽略,對高層級使用者就會造成不良的使用體驗。而另一種方法
為干擾消除,透過重傳干擾的機制,可以使較高層級使用者接收干擾
到自己的訊息,利用此訊息回復先前遭到干擾的訊息。若使用此方法,
收發端的複雜度勢必較高,且會造成解碼時間的延遲。理論上,減掉
干擾可以使系統達到較高傳送速率,且次級使用者如同無偵測延遲。
此研究採用有限長度的消息理論分析(Finite blocklength regime),如此較貼近實際上的傳輸情況。與其他避免干擾的傳輸方法作比較,使用
干擾消除的方法,在偵測延遲影響較大的情形之下,重傳干擾可以獲
得較好的效果。
zh_TW
dc.description.abstractThis thesis considers a sensing-based spectrum sharing among dynamic secondary users with different priorities. In the system, transmission of secondary users with lower priorities should not degrade performance of those with higher priorities. Our goal is to characterize undamental performance limits of the system which has sensing delay. Under our consideration, the delay period of low-priority is comparable to message size and it causes significant
interference to high-priority users, since the message size of high-priority is limited. This work is focused on finite blocklength results of different spectrum sharing techniques. Traditionally, low-priority user stops transmission or lower transmission power to avoid causing interference to high-priority users. It turns out that the performance of such interferen-ceavoidance schemes degrades as the sensing delay becomes comparable to the
blocklength of the high-priority users. Instead, we propose novel methods that harness simple retransmission from low-priority users to realize interference cancellation at the receivers of high-priority users. The finite blocklength performances of the proposed schemes outperform traditional ones when the sensing delay is comparable to the blocklength of high-priority users. The
additional cost lies in the complexity of the receivers and/or the induced decoding delay which is acceptable. To obtain these theoretical results, we first derive the equivalent channels under these spectrum sharing schemes, which are no longer i.i.d. or memoryless, and then extend the finite blocklength analysis for point-to-point channels to these equivalent channels.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T11:22:47Z (GMT). No. of bitstreams: 1
ntu-105-R03942070-1.pdf: 2518338 bytes, checksum: 889897bd454df92e94e2b0b365c5d862 (MD5)
Previous issue date: 2016
en
dc.description.tableofcontents誌謝iii
摘要v
Abstract vii
1 Introduction 1
1.1 New Spectrum Sharing Scope . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Chapters Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Channel Coding Bounds on Finite Blocklength Regime 9
2.1 Non-Asymptotic Achievability bounds . . . . . . . . . . . . . . . . . . . 10
2.2 Non-Asymptotic Converse bound . . . . . . . . . . . . . . . . . . . . . 13
2.3 Asymptotic Result of Second-order coding rate . . . . . . . . . . . . . . 13
3 Problem Formulation and Spectrum Sharing Schemes 17
3.1 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3 Conventional Schemes Mechanisms . . . . . . . . . . . . . . . . . . . . 19
3.3.1 Stop-and-Skip (SnS) Scheme . . . . . . . . . . . . . . . . . . . . 20
3.3.2 Sense-and-Lower Power (SnL) Scheme . . . . . . . . . . . . . . 20
3.4 Cancellation-based Schemes . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4.1 Stop-and-Cancel Scheme (SnC) . . . . . . . . . . . . . . . . . . 21
3.4.2 Consecutively Retransmit-and-Cancel Scheme (CRC) . . . . . . 21
3.4.3 Retransmit-and-Lower power Scheme (CRL) . . . . . . . . . . . 21
3.5 Retransmit-and-Decode Scheme (CRD) . . . . . . . . . . . . . . . . . . 22
4 Asymptotic Analysis up to Second-Order Coding Rate 23
4.1 Interference Cancellation Schemes . . . . . . . . . . . . . . . . . . . . . 24
4.1.1 Proposed Cancellation Scheme (CRC): Unit Sensing Delay . . . . 24
4.1.2 Proposed Cancellation Scheme (CRC): Block Sensing Delay . . . 32
4.1.3 Proposed Cancellation Scheme (CRC): SU Performance and Throughput
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1.4 SnC Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.1.5 Proposed Cancellation Scheme: Low-Power (CRL) . . . . . . . . 37
4.1.6 Penalty: Average Decoding Delay per Message . . . . . . . . . . 39
4.2 Asymptotic Second-Order Coding Rate of Non Cancellation-based Scheme 41
4.2.1 SnS Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2.2 SnL Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.2.3 CRD Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.4 Average Decoding Delay per Message . . . . . . . . . . . . . . . 44
4.3 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3.1 ProU up to Second-Order Coding Rate Results . . . . . . . . . . 45
4.3.2 SU up to Second-Order Coding Rate Results . . . . . . . . . . . 47
4.3.3 Sum-throughput Results . . . . . . . . . . . . . . . . . . . . . . 47
4.3.4 Performance when ProU Minimize the Impact . . . . . . . . . . 47
5 Non-Asymptotic Analysis: One-Shot Bounds 51
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2 Non-Asymptotic Results of Non Interference Cancellation Schemes . . . 52
5.2.1 Distribution of ProU Information Density . . . . . . . . . . . . . 52
5.2.2 Distribution of SU Information Density . . . . . . . . . . . . . . 53
5.2.3 Throughput Performance . . . . . . . . . . . . . . . . . . . . . . 54
5.3 Non-Asymptotic Results of Interference Cancellation Scheme . . . . . . 55
5.3.1 Distribution of ProU Information density . . . . . . . . . . . . . 55
5.3.2 Distribution of SU Information Density . . . . . . . . . . . . . . 56
5.3.3 Throughput Performance . . . . . . . . . . . . . . . . . . . . . . 56
5.4 Simulation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.4.1 ProU Throughput Performance . . . . . . . . . . . . . . . . . . . 57
5.4.2 SU Throughput Performance . . . . . . . . . . . . . . . . . . . . 60
6 Cancellation-based Schemes in Multi-User System 63
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.2 Loss in Conventional Schemes . . . . . . . . . . . . . . . . . . . . . . . 64
6.3 Single-ProU-Multi-SU Cellular System(SPMS) . . . . . . . . . . . . . . 65
6.3.1 SPMS Downlink Cellular System . . . . . . . . . . . . . . . . . 65
6.3.2 SPMS Uplink Cellular System . . . . . . . . . . . . . . . . . . . 68
6.4 Multi-ProU-Single-SU Cellular System(MPSS) . . . . . . . . . . . . . . 72
6.4.1 MPSS Uplink Cellular System . . . . . . . . . . . . . . . . . . . 72
6.4.2 MPSS Downlink Cellular System . . . . . . . . . . . . . . . . . 74
6.5 Multiple Standalone ProU System . . . . . . . . . . . . . . . . . . . . . 76
7 Conclusion 79
A Proof of Lemma.4.1.3 . . . 81
B Proof of Corollary. 4.1.2 . . . 83
C Proof of Eigenvalue Decomposition in Retransmission Low-Power scheme . . . 87
D Proof of General Asymptotic Approximation Result . . . 89
Bibliography . . . 93
dc.language.isoen
dc.title改善偵測延遲之次級使用者頻譜分享系統zh_TW
dc.titleDelay-Sensing Resilient Spectrum Sharing System among Dynamic Secondary Usersen
dc.typeThesis
dc.date.schoolyear104-2
dc.description.degree碩士
dc.contributor.oralexamcommittee陳伯寧(Po-Ning Chen),黃昱智(Yu-Chih Huang)
dc.subject.keyword頻譜分享,三階層框架,偵測延遲,重傳機制,有限長度消息理論分析,多使用者系統,zh_TW
dc.subject.keywordspectrum sharing,three-tiered framework,sensing delay system,non-i.i.d bursty channel,retransmission scheme,finite blocklength analysis,multi-user cellular system,en
dc.relation.page95
dc.identifier.doi10.6342/NTU201602012
dc.rights.note有償授權
dc.date.accepted2016-08-19
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
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