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2020 | OriginalPaper | Buchkapitel

1. Introduction

verfasst von : Lei Jiao

Erschienen in: Channel Aggregation and Fragmentation for Traffic Flows

Verlag: Springer International Publishing

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Abstract

With the rapid development of modern communication systems and electronics technologies, spectrum utilization becomes more and more flexible and dynamic. Traditionally, a traffic flow is sent within one communication channel. With the help of channel aggregation (CA) technology, it is possible to adopt multiple channels for transmitting one flow, while the channel fragmentation (CF) technology can help divide one channel into multiple segments in order to transmit multiple flows. Studies on CA and CF and their relevant topics are numerous. To indicate the amount of the studies, we searched channel aggregation as the keyword in IEEE Xplore, on January 20th, 2019, and found 1256 relevant articles. In this chapter, we introduce the principle of CA and CF, and the concepts that are similar to them. We also provide an incomplete survey of these techniques with the main focus on cognitive radio networks (CRNs).

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Nächstes Kapitel Markov Chain and Stationary Distribution
Fußnoten
1
Cognitive radio (CR) is a software-defined radio with built-in intelligence that can detect available spectrum opportunities in a wireless spectrum and adaptively adjust transmission parameters to operate concurrently with existing systems. A CR normally does not own a communication spectrum and thus needs to access channels belonging to other systems opportunistically. A CRN is a communication network composed by CRs.
 
2
Wireless sensor networks are composed by a number of dispersed sensors with communication modules for monitoring or collecting data from a specific environment. WSNs have been applied in many fields, such as industrial and home automation.
 
3
Primary users are the owners of the spectrum who have priority in spectrum access in the CRNs.
 
4
Secondary users in CRNs are users who borrow the spectrum belonging to PUs. We use SUs and CRs interchangeably when there is no ambiguity.
 
5
Quality of service describes the overall performance of a service in a communications system, which is usually quantitatively measured via parameters such as throughput, delay, jitter, packet loss ratio, and etc.
 
6
We consider channel selection as a special case of resource allocation. If no power is allocated on a channel, the channel is not opted for communication.
 
7
More detailed definition of QSR can be found in Sect. 4.​1.​3.
 
8
For a complete system description, please refer to Sect. 3.​1.​2.
 
9
This capacity is different from Shannon capacity. More specifically, the capacity is defined by the number of completed flows over a unit time.
 
Literatur
1.
2.
3.
4.
Bukhari SHR, Rehmani MH, Siraj S (2016) A survey of channel bonding for wireless networks and guidelines of channel bonding for futuristic cognitive radio sensor networks. IEEE Commun Surv Tutorials 18(2):924–948CrossRef
5.
3GPP (2017) 3rd Generation Partnership Project; Technical specification group radio access network; Evolved universal terrestrial radio access (E-UTRA); User equipment (UE) radio transmission and reception (release 12)
6.
Cao Y, Sunde EJ, Chen K (2019) Multiplying channel capacity: aggregation of fragmented spectral resources. IEEE Microw Mag 20(1):70–77CrossRef
7.
Jiao L, Balapuwaduge IAM, Li FY, Pla V (2014) On the performance of channel assembling and fragmentation in cognitive radio networks. IEEE Trans Wirel Commun 13(10):5661–5675CrossRef
8.
Balapuwaduge IAM, Jiao L, Pla V, Li FY (2014) Channel assembling with priority-based queues in cognitive radio networks: strategies and performance evaluation. IEEE Trans Wirel Commun 13(2):630–645CrossRef
9.
Rostami S, Arshad K, Rapajic P (2018) Optimum radio resource management in carrier aggregation based LTE-advanced systems. IEEE Trans Veh Technol 67(1):580–589CrossRef
10.
Yuan G, Zhang X, Wang W, Yang Y (2010) Carrier aggregation for LTE-advanced mobile communication systems. IEEE Commun Mag 48(2):88–93CrossRef
11.
Maheshwari MK, Roy A, Saxena N (2019) DRX over LAA-LTE-a new design and analysis based on semi-Markov model. IEEE Trans Mobile Comput 18(2):276–289CrossRef
12.
Yuan Y, Bahl P, Chandra R, Chou PA, Ferrell JI, Moscibroda T, Narlanka S, Wu Y (2007) KNOWS: Cognitive radio networks over white spaces. In: Proceedings of IEEE DySPAN, Dublin, Ireland
13.
Su H, Zhang X (2008) Cross-layer based opportunistic MAC protocols for QoS provisionings over cognitive radio mobile wireless networks. IEEE J Sel Areas Commun 26(1):118–129CrossRef
14.
Cao L, Yang L, Zheng H (2010) The impact of frequency-agility on dynamic spectrum sharing. In: Proceedings of IEEE DySPAN, SingaporeCrossRef
15.
Parkvall S, Furuskar A, Dahlman E (2011) Evolution of LTE toward IMT-Advanced. IEEE Commun Mag 49(2):84–91CrossRef
16.
IEEE 802.11 task group ac (2013) IEEE Std 802.11ac-2013 (Amendment to IEEE Std 802.11-2012, as amended by IEEE Std 802.11ae-2012, IEEE Std 802.11aa-2012, and IEEE Std 802.11ad-2012) pp 1–425
17.
IEEE 802.22 WG (2011) IEEE standard for wireless regional area networks part 22: Cognitive wireless RAN medium access control (MAC) and physical layer (PHY) specifications: policies and procedures for operation in the TV bands
18.
Morgado A, Huq KMS, Mumtaz S, Rodriguez J (2018) A survey of 5G technologies: regulatory, standardization and industrial perspectives. Digital Commun Netw 4(2):87–97CrossRef
19.
Zhu X, Shen L, Yum TP (2007) Analysis of cognitive radio spectrum access with optimal channel reservation. IEEE Commun Lett 11(4):304–306CrossRef
20.
Anand S, Sengupta S, Hong K, Subbalakshmi KP, Chandramouli R, Cam H (2014) Exploiting channel fragmentation and aggregation/bonding to create security vulnerabilities. IEEE Trans Veh Technol 63(8):3867–3874CrossRef
21.
Coffman E, Robert P, Simatos F, Tarumi S, Zussman G (2012) A performance analysis of channel fragmentation in dynamic spectrum access systems. Queueing Syst 71(3):293–320MathSciNetMATHCrossRef
22.
Holma H, Toskala A (2009) LTE for UMTS: OFDMA and SC-FDMA based radio access. Wiley, ChichesterCrossRef
23.
Anand S, Hong K, Sengupta S, Chandramouli R (2011) Is channel fragmentation/bonding in IEEE 802.22 networks secure? In: Proceedings of IEEE ICC, pp 1–5
24.
Ghods F, Yousefi H, Hemmatyar AMA, Movaghar A (2013) MC-MLAS: Multi-channel minimum latency aggregation scheduling in wireless sensor networks. Comput Netw 57(18):3812–3825CrossRef
25.
Joshi S, Pawelczak P, Cabric D, Villasenor J (2012) When channel bonding is beneficial for opportunistic spectrum access networks. IEEE Trans Wirel Commun 11(11):3942–3956CrossRef
26.
Lin Z, Ghosh M, Demir A (2013) A comparison of MAC aggregation vs. PHY bonding for WLANs in TV white spaces. In: Proceedings of IEEE PIMRC, pp 1829–1834
27.
Chandra R, Mahajan R, Moscibroda T, Raghavendra R, Bahl P (2008) A case for adapting channel width in wireless networks. In: Proceedings of ACM SIGCOMM, New York, NY, USA, pp 135–146
28.
Xu L, Yamamoto K, Yoshida S (2007) Performance comparison between channel-bonding and multi-channel CSMA. In: Proceedings of IEEE WCNC, pp 406–410
29.
Deek L, Garcia-Villegas E, Belding E, Lee S, Almeroth K (2013) Joint rate and channel width adaptation for 802.11 MIMO wireless networks. In: Proceedings of IEEE SECON, pp 167–175
30.
Deek L, Garcia-Villegas E, Belding E, Lee S, Almeroth K (2014) Intelligent channel bonding in 802.11n WLANs. IEEE Transactions on Mobile Computing 13(6):1242–1255CrossRef
31.
Wang X, Huang P, Xie J, Li M (2014) OFDMA-based channel-width adaptation in wireless mesh networks. IEEE Trans Veh Technol 63(8):4039–4052CrossRef
32.
Zhang W, Wang C, Ge X, Chen Y (2018) Enhanced 5G cognitive radio networks based on spectrum sharing and spectrum aggregation. IEEE Trans Commun 66(12):6304–6316CrossRef
33.
Jiao L, Razaviyayn M, Song E, Luo ZQ, Li FY (2011) Power allocation in multi-channel cognitive radio networks with channel assembling. In: Proceedings of IEEE SPAWC, pp 86–90
34.
Ramaboli AL, Falowo OE, Chan AH (2012) Bandwidth aggregation in heterogeneous wireless networks: a survey of current approaches and issues. J Netw Comput Appl 35(6):1674–1690CrossRef
35.
Khan Z, Ahmadi H, Hossain E, Coupechoux M, Dasilva LA, Lehtomäki JJ (2014) Carrier aggregation/channel bonding in next generation cellular networks: methods and challenges. IEEE Netw 28(6):34–40CrossRef
36.
Salameh HAB, Krunz MM, Younis O (2009) MAC protocol for opportunistic cognitive radio networks with soft guarantees. IEEE Trans Mobile Comput 8(10):1339–1352CrossRef
37.
Su H, Zhang X (2007) The cognitive radio based multi-channel MAC protocols for wireless ad hoc networks. In: Proceedings of IEEE GLOBECOM, Washington DC, USACrossRef
38.
Bukhari SHR, Siraj S, Rehmani MH (2016) PRACB: A novel channel bonding algorithm for cognitive radio sensor networks. IEEE Access 4:6950–6963CrossRef
39.
Foukalas F, Shakeri R, Khattab T (2018) Distributed power allocation for multi-flow carrier aggregation in heterogeneous cognitive cellular networks. IEEE Trans Wirel Commun 17(4):2486–2498CrossRef
40.
Hsu AC, Wei DSL, Kuo CCJ (2007) A cognitive MAC protocol using statistical channel allocation for wireless ad-hoc networks. In: Proceedings of IEEE WCNC, Hongkong, ChinaCrossRef
41.
Shajaiah H, Abdelhadi A, Clancy C (2017) Resource allocation with carrier aggregation in cellular networks: optimality and spectrum sharing using C++ and MATLAB. Springer, Cham
42.
Jiao L, Li FY, Pla V (2012) Modeling and performance analysis of channel assembling in multichannel cognitive radio networks with spectrum adaptation. IEEE Trans Veh Technol 61(6):2686–2697CrossRef
43.
Jiao L, Song E, Pla V, Li FY (2013) Capacity upper bound of channel assembling in cognitive radio networks with quasistationary primary user activities. IEEE Trans Veh Technol 62(4):1849–1855CrossRef
44.
Shaat M, Bader F (2010) Computationally efficient power allocation algorithm in multicarrier based cognitive radio networks: OFDM and FBMC systems. EURASIP J Adv Signal Process 2010(1):1–13CrossRef
45.
Re ED, Argenti F, Ronga LS, Bianchi T, Suffritti R (2008) Power allocation strategy for cognitive radio terminals. In: Proceedings of CogART, Aalborg, Denmark
46.
Haddad M, Hayar AM, Debbah M (2007) Optimal power allocation for cognitive radio based on a virtual noise threshold. In: Proceedings of ACM CWNETS, Vancouver, CanadaCrossRef
47.
Jia J, Zhang Q, Shen X (2008) HC-MAC: a hardware-constrained cognitive MAC for efficient spectrum management. IEEE J Sel Areas Commun 26(1):106–117CrossRef
48.
Zhang R, Liang YC, Cui S (2010) Dynamic resource allocation in cognitive radio networks: a convex optimization perspective. IEEE Signal Process Mag 27(3):102–114CrossRef
49.
Federal Communications Commission (2003) Notice of inquiry and notice of proposed rulemaking: in the matter of establishment of an interference temperature metric to quantify and manage interference and to expand available unlicensed operation in certain fixed, mobile and satellite frequency bands. ET Docket No 03-237, FCC-03-289
50.
Federal Communications Commission (2007) Order: in the matter of establishment of an interference temperature metric to quantify and manage interference and to expand available unlicensed operation in certain fixed, mobile and satellite frequency bands. ET Docket No 03-237, FCC 07-78
51.
Urgaonkar R, Neely MJ (2009) Opportunistic scheduling with reliability guarantees in cognitive radio networks. IEEE Trans Mobile Comput 8(6):766–777CrossRef
52.
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, CambridgeMATHCrossRef
53.
Akyildiz IF, Lee WY, Chowdhury K (2009) CRAHNs: cognitive radio ad hoc networks. Ad Hoc Netw 7(5):810–836CrossRef
54.
Martinez-Bauset J, Pla V, Pacheco-Paramo D (2009) Comments on “analysis of cognitive radio spectrum access with optimal channel reservation”. IEEE Commun Lett 13(10):739
55.
Wong EWM, Foh CH (2009) Analysis of cognitive radio spectrum access with finite user population. IEEE Commun Lett 13(5):294–296CrossRef
56.
Jiao L, Pla V, Li FY (2010) Analysis on channel bonding/aggregation for multi-channel cognitive radio network. In: Proceedings of European wireless, Lucca, ItalyCrossRef
57.
Lee J, So J (2010) Analysis of cognitive radio networks with channel aggregation. In: Proceedings of IEEE WCNC, Sydney, AustraliaCrossRef
58.
Jiao L, Li FY, Pla V (2011) Greedy versus dynamic channel aggregation strategy in CRNs: Markov models and performance evaluation. LNCS 6827:22–31
59.
Jiao L, Li FY, Pla V (2011) Dynamic channel aggregation strategies in cognitive radio networks with spectrum adaptation. In: Proceedings of IEEE GLOBECOM, Houston, Texas, USA
60.
Ai S, Jiao L, Li FY, Radin M (2016) Channel aggregation with guard-band in D-OFDM based CRNs: Modeling and performance evaluation. In: Proceedings of IEEE WCNC, pp 1–6
Metadaten
Titel
Introduction
verfasst von
Lei Jiao
Copyright-Jahr
2020
Buch
Channel Aggregation and Fragmentation for Traffic Flows

Print ISBN: 978-3-030-33079-8

Electronic ISBN: 978-3-030-33080-4

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-33080-4_1

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