Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Cognitive Radio Communication System: Spectrum Sharing Techniques Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

9. Channel Capacity of Cognitive Radio in a Fading Environment with CSI and Interference Power Constraints

verfasst von : Shweta Pandit, Ghanshyam Singh

Erschienen in: Spectrum Sharing in Cognitive Radio Networks

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Channel capacity is used as a basic performance measurement tool for analysis and design of more efficient techniques to improve the spectral efficiency of wireless communication systems. In spectrum sharing systems, channel state information (CSI) is used at the cognitive/secondary transmitter to adaptively adjust the transmission. The knowledge of secondary link CSI and information at the secondary transmitter/cognitive radio transmitter regarding the channel between the secondary transmitter and the primary receiver (PR) are used to obtain the optimal power transmission policy of the secondary user (SU) under the constraints on the peak and average received power at the primary receiver. In this chapter, we numerically compute the channel capacity in the fading environment under average interference power constraints with two different adaptation policies for spectrum sharing in cognitive radio communication systems: power adaptation and rate and power adaptation for multilevel quadrature amplitude modulation format. We explore the small-scale fading effect on the transmit power of the secondary transmitter. The rate and power of the secondary transmitter is varied based upon the sensing information and channel state information of the secondary link. The channel capacity is maximized for these two policies by considering the Lagrange optimization problem for the average interference power constraint.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Capacity Limits Over Fading Environment with Imperfect Channel State Information for Cognitive Radio Networks
Nächstes Kapitel Framework for Cross-Layer Optimization in Cognitive Radio Network
Literatur
1.
E. Biglieri, J. Proakis, S. Shamai, Fading channels: Information theoretic and communications aspects. IEEE Trans. Inf. Theory 44(6), 2619–2692 (1998)MathSciNetCrossRefMATH
2.
A.-J. Goldsmith, P. Varaiya, Capacity of fading channels with channel side information. IEEE Trans. Inf. Theory 43(6), 1986–1992 (1997)MathSciNetCrossRefMATH
3.
M.-A. Khojastepour, B. Aazhang, The capacity of average and peak power constrained fading channels with channel side information, Proceedings of IEEE Wireless Communication and Networking Conference (WCNC’04) (Atlanta, CA, USA, Mar. 2004), pp. 77–82
4.
A. Ghasemi, E.S. Sousa, Capacity of fading channels under spectrum-sharing constraints, Proceedings of IEEE International Conference on Communication (ICC’06) (Istanbul, Turkey, June 2006), pp. 4373–4378
5.
L. Musavian, S. Aissa, Ergodic and outage capacities of spectrum sharing systems in fading channels, Proceedings of IEEE Global Telecommunication Conference (GLOBECOM’07) (Washington, DC, USA, Nov. 2007), pp. 3327–3331
6.
A. Ghasemand, E.-S. Sousa, Fundamental limits of spectrum-sharing in fading environments. IEEE Trans. Wirel. Commun. 6(2), 649–658 (2007)
7.
B. Wang, K.-J.-R. Liu, Advances in cognitive radio networks: a survey. IEEE J. Sel. Top. Sign. Proces. 5(1), 5–23 (2011)
8.
V. Asghari, S. Aissa, Resource sharing in cognitive radio systems: outage capacity and power allocation under soft sensing. Proceedinds of IEEE Global Telecommunication Conference (GLOBECOM’08) (New Orleans, LA, USA, Nov. 2008), pp. 1–5
9.
I.-F. Akyildiz, W.-Y. Lee, M.-C. Vuran, S. Mohanty, NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput. Netw. 50(13), 2127–2159 (2006)CrossRefMATH
10.
S. Pandit, G. Singh, Throughput maximization with reduced data loss rate in cognitive radio network. Telecommun. Syst. 57(2), 209–215 (2014)
11.
T. Yucek, H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutorials 11(1), 116–130 (2009)CrossRef
12.
H. Urkowitz, Energy detection of unknown deterministic signals. Proc. IEEE 55(4), 523–531 (1967)CrossRef
13.
S. Srinivasa, S.A. Jafar, Soft sensing and optimal power control for cognitive radio. IEEE Trans. Wirel. Commun. 9(12), 3638–3649 (2010)CrossRef
14.
Z. Rezki, M.-S. Alouini, Ergodic capacity of cognitive radio under imperfect channel state information. IEEE Trans. Veh. Technol. 61(5), 2108–2119 (2012)CrossRef
15.
S. Pandit, Medium access control protocol for spectrum sharing in cognitive radio communication system. Ph D Thesis, Jaypee University of Information Technology, Waknaghat, Solan-173234, India
16.
S. Parsaeefard, A.-R. Sharafat, Robust distributed power control in cognitive radio networks. IEEE Trans. Mob. Comput. 12(4), 609–620 (2013)CrossRef
17.
G. Zhao, C. Yang, G.-Y. Li, D. Li, A.-C.-K. Soong, Power and channel allocation for cooperative relay in cognitive radio networks, IEEE J. Sel. Top. Sign. Process. 5(1), 151–159 (2011)
18.
X. Kang, H.-K. Garg, Y.-C. Liang, R. Zhiang, Optimum power allocation for OFDM-based cognitive radio with new primary transmission protection criteria. IEEE Trans. Wirel. Commun. 9(6), 2066–2075 (2010)
19.
Q. Li, K.-H. Li, K.-C. Teh, Diversity-multiplexing tradeoff of wireless communication systems with user cooperation. IEEE Trans. Inf. Theory 57(9), 5794–5819 (2011)
20.
A. Sendonaris, E. Erkip, B. Aazhang, User cooperation diversity—part I: system description. IEEE Trans. Commun. 51(11), 1927–1938 (2003)CrossRef
21.
V. Asghari, S. Aissa, Adaptive rate and power transmission in spectrum-sharing systems. IEEE Trans. Wirel. Commun. 9(10), 3272–3280 (2010)CrossRef
22.
A.-J. Goldsmith, S.-G. Chua, Variable-rate variable-power MQAM for fading channels. IEEE Trans. Commun. 45(10), 1218–1230 (1997)
23.
M. Abramowitz, I.A. Stegun, Handbook of Mathematical Functions: with Formulas, Graphs, and Mathematical Tables, 9th edn. (Dover, New York, 1972)MATH
24.
S.-S. Tzeng, C.-W. Huang, Effective throughput maximization for in-band sensing and transmission in cognitive radio networks. Wireless Netw. 17(4), 1025–1029 (2011)
25.
S. Haykin, Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)CrossRef
26.
P. Potier, C. Sorrells, Y. Wang, L. Qian, H. Li, Spectrum inpainting: a new framework for spectrum status determination in large cognitive radio networks. Wireless Netw. 20(3), 423–439 (2014)CrossRef
27.
J.-K. Cavers, Variable rate transmission for Rayleigh fading channels. IEEE Trans. Commun. 20(1), 15–22 (1972)MathSciNetCrossRef
28.
W.-T. Webb, R. Steele, Variable rate QAM for mobile radio. IEEE Trans. Commun. 43(7), 2223–2230 (1995)CrossRef
29.
M.-S. Alouini, A.-J. Goldsmith, Adaptive modulation over Nakagami fading channels. Wireless Pers. Commun. 13(1–2), 119–143 (2000)CrossRef
30.
X. Liu, S. Shankar, Sensing-based opportunistic channel access. Mob. Netw. Appl. 11(4), 577–591 (2006)CrossRef
31.
S. Boyd, L. Vandenberghe, Convex Optimization (Cambridge University Press, New York, 2004)CrossRefMATH
32.
D.P. Bertsekas, Nonlinear Programming (Athena Scientific, Belmont, MA, 1995)MATH
33.
H. Suzuki, A statistical model for urban multipath propagation. IEEE Trans. Commun. 25(7), 673–680 (1977)CrossRef
34.
A.-U. Sheikh, M. Abdi, M. Handforth, Indoor mobile radio channel at 946 MHz: measurements and modeling. Proceedings of IEEE Vehicular Technology Conference (VTC ’93) (Secaucus, NJ, May 1993), pp. 73–76
Metadaten
Titel
Channel Capacity of Cognitive Radio in a Fading Environment with CSI and Interference Power Constraints
verfasst von
Shweta Pandit
Ghanshyam Singh
Copyright-Jahr
2017
Buch
Spectrum Sharing in Cognitive Radio Networks

Print ISBN: 978-3-319-53146-5

Electronic ISBN: 978-3-319-53147-2

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-53147-2_9

Neuer Inhalt

    Bildnachweise