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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2015

01.05.2015

Primary Network Interference Compensation-Based Dynamic Spectrum Leasing and Secondary Network Power Control

verfasst von: Weng-jiang Feng, Weiheng Jiang

Erschienen in: Wireless Personal Communications | Ausgabe 2/2015

Einloggen

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

search-config
loading …

Abstract

The underlay paradigm is an important spectrum sharing model for cognitive-based secondary networks, in which the interference temperature limit (ITL) quantifies the amount of resources that can be shared by a secondary network and power control is known to play a crucial role. Many recent studies have discussed ITL-based underlay spectrum sharing and power control, but issues such as ITL decision-making and the value range for the ITL have not been analyzed. We also know that the presence of secondary network interference may increase the transmission power of primary users (PUs) who have hard traffic quality of service requirements. This additional resource sharing cost (increased transmit power) may discourage the PUs from sharing resources with secondary networks; the motivation in favor of resource sharing has not yet been completely explored. To address these unresolved problems, underlay-based spectrum sharing is reconsidered in this paper, using the scenario in which a cell primary network coexists with a cell secondary network that shares the spectrum resource with the primary network. First, under a fixed interference cap (IC) design, a simple primary network power control scheme is introduced. We then discuss IC design issues from the primary network point of view. Finally, a primary network interference compensation-based dynamic spectrum leasing scheme is proposed, in which the primary network suboptimal price search and SU’s power iteration algorithm are also provided. We use simulation to demonstrate the performance of our proposed schemes.
Vorheriger Artikel Cooperative MIMO Based Reporting System for IEEE 802.22 Standard Over Fading Channels
Nächster Artikel Erratum to: Primary Network Interference Compensation-Based Dynamic Spectrum Leasing and Secondary Network Power Control

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

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

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 "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
The concept of interference cap was firstly proposed in [17] to indicate a dynamic/radical ITL, and the IC concept used in this paper follows [17].
 
2
\(\rho (\mathbf{F}^{(p)})\) is defined as the spectral radius of matrix \(\mathbf{F}^{(p)}\). According to the Perron-Frobenius theorem [43], we know that if we denote eigenvalues of matrix \(\mathbf{F}^{(p)}\) by \(\lambda _{1}, \lambda _{2},\ldots , \lambda _{\mathrm{k}}^{(\mathrm{p})}\), then we have \(\rho ({\mathbf{F}^{(p)}})= \max {\vert }\lambda _{i}{\vert }, i= 1,2, \ldots , k^{(p)}\).
 
3
One may argue that the APO may not be the only performance degradation for primary network/PUs or the APO may not be the unique performance loss expression; other performance loss/expressions, such as bit-rate-decease, may also exist. While, for our considered scenario, because we have assumed PUs with hard-QoS (the minimum SINR requirements), that is, their minimum SINR requirements must be satisfied. Then the presence of secondary network interference will surely increase the transmission power of PUs, that is, the APO.
 
4
Without considering the secondary network interference, the feasibility of primary network power control with MTPCs has been analyzed in [28].
 
5
Herein, we always assume that all PUs in the network will truthfully report their residual power, and the motivation of spending additional power (or APO) to support the coexistence of SUs is that all PUs will receive some compensation.
 
6
For the primary network, the immediate effects of the presence of secondary network interference are a reduction in the available SINR for PUs and a bit-rate decrease or bit-error-rate (BER) increase. However, as mentioned earlier, we have restricted that the PUs have hard QoS requirements, so that all PUs have to increase their transmission power to achieve their SINR targets. Thus, we quantify the PUs’ transmit power increase by APO and use it as the cost of resource sharing.
 
7
As mentioned earlier, the unit APO price \(\rho \) reflects how PUs care for their power consumption. For our interference compensation-based DSL, it can be seen as a power trading between the primary and secondary networks in which the PUs use more transmission power to support the coexistence of SUs to earn rewards, while the SUs obtain IC resources but with interference-compensated payment.
 
8
As shown in [25] and [41], a typical SINR in the IS-95 CDMA system is 6–7 dB.
 
9
In this setting, the optimal and suboptimal prices are the same, as can be noted in Fig. 2(b). Therefore, we did not discriminate them in the following discussion.
 
10
Because the SUs have a co-located receiver SUBS and the same primary and secondary network signal correlation coefficient \(1/G_{p}^{(s)}\), all SUs suffer from same primary network interference.
 
11
As mentioned earlier, the case \(\lambda _{0}=0\) is for interweave-based spectrum sharing. As the motivation of underlay-based spectrum sharing for the primary network is to obtain external revenue, we always have \(\lambda _{0}>0\).
 
12
The cases \(\lambda _0 =\max _{i\in \mathrm{A}(\lambda _0)} \left( {2\omega _i^{(s)} \gamma _{i,tar}^{(s)}}\right) \) and \(\lambda _{0}=0\) are omitted here for lack of sense, as mentioned earlier (please see “Appendix1”).
 
Literatur
1.
Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6, 13–18.CrossRef
2.
Zhao, Q., & Sadler, B. M. (2007). A survey of dynamic spectrum access. IEEE Signal Processing Magazine, 24(3), 79–89.CrossRef
3.
Federal Communications Commission (2002). Spectrum policy task force report. ET Docket No. 02–135, Nov 2002.
4.
Huang, J., Berry, R., & Honig, M. L. (2006). Auction-based spectrum sharing. ACM/Springer Mobile Networks and Applications (MONET), 11(3), 405–418.CrossRef
5.
Al Daoud, A., Alpcan, T., Agarwal, S., & Alanyali, M. (2008). A stackelberg game for pricing uplink power in wide-band cognitive radio networks. In Proceedings of 47th IEEE conference on decision and control, Mexico, Dec 2008.
6.
Li, Z., Gao, L., Wang, X., Gao, X., & Hossain, E. (2010). Pricing for uplink power control in cognitive radio networks. IEEE Transactions on Vehicular Technology, 59(4), 1769–1778.CrossRef
7.
Xing, Y., Mathur, C. N., Haleem, M. A., Chandramouli, R., & Subbalakshmi, K. P. (2007). Dynamic spectrum access with QoS and interference temperature constraints. IEEE Transactions on Mobile Computing, 6(4), 423–433.CrossRef
8.
Wang, F., Krunz, M., & Cui, S. G. (2008). Price-based spectrum management in cognitive radio networks. IEEE Journal of Selected Topics in Signal Processing, 2(1), 74–87.CrossRef
9.
Scutari, G., Palomar, D. P., & Barbarossa, S. (2008). Optimal linear precoding strategies for wideband noncooperative systems based on game theory—Part I: Nash equilibria. IEEE Transactions on Signal Processing, 56(3), 1230–1249.CrossRefMathSciNet
10.
Scutari, G., Palomar, D. P., & Barbarossa, S. (2008). Optimal linear precoding strategies for wideband noncooperative systems based on game theory—Part II: Algorithms. IEEE Transactions on Signal Processing, 56(3), 1250–1267.CrossRefMathSciNet
11.
Hong, M., & Garcia, A. (2011). Equilibrium pricing of interference in cognitive radio networks. IEEE Transactions on Signal Processing, 59(12), 6058–6072.CrossRefMathSciNet
12.
Hong, M., Garcia, A., & Wilson, S. G. (2011). Distributed uplink resource allocation in cognitive radio networks—Part I/II: Equilibria and algorithms for power allocation. CoRR abs/1102.1959.
13.
Hong, M., Garcia, A., Barrera, J., & Wilson, S. G. (2013). Joint access point selection and power allocation for uplink wireless networks. IEEE Transactions on Signal Processing, 61(13), 3334–3347.CrossRefMathSciNet
14.
Shaat, M., & Bader, F. (2012). Asymptotically optimal resource allocation in OFDM-based cognitive networks with multiple relays. IEEE Transactions on Wireless Communications, 11(3), 893–897.CrossRef
15.
Alsharoa, A., Bader, F., & Alouini, M.-S. (2013). Relay selection and resource allocation for two-way DF-AF cognitive radio networks. IEEE Wireless Communications Letters, 2(4), 427–430.CrossRef
16.
Wang, T., Song, L., Han, Z., & Jiao, B. (2011). Improve secure communications in cognitive two-way relay networks using sequential second price auction. In Fifth IEEE international symposium on new frontiers in dynamic spectrum access networks 2011 (DySPAN 2011). Aachen, Germany, May 2011.
17.
Li, T., & Jayaweera, S. K. (2008). A novel primary-secondary user power control game for cognitive radios. In The 2008 international symposium on information theory and its applications (ISITA2008). Auckland, NZ, Dec 2008.
18.
Jayaweera, S. K., Vazquez-Vilar, G., & Mosquera, C. (2010). Dynamic spectrum leasing (DSL): A new paradigm for spectrum sharing in cognitive radio networks. IEEE Transactions on Vehicular Technology, 59(5), 2328–2339.CrossRef
19.
Jayaweera, S. K., & Li, T. (2009). Dynamic spectrum leasing in cognitive radio networks via primary-secondary user power control games. IEEE Transactions on Wireless Communications, 8(6), 3300–3310.CrossRef
20.
Hakim, K., Jayaweera, S. K., et al. (2010). Efficient dynamic spectrum sharing in cognitive radio networks: Centralized dynamic spectrum leasing (C-DSL). IEEE Transactions on Wireless Communications, 9(9), 2956–2967.CrossRef
21.
El-howayek, G., & Jayaweera, S. K. (2011). Distributed dynamic spectrum leasing (D-DSL) for spectrum sharing over multiple primary channels. IEEE Transactions on Wireless Communications, 10(1), 55–60.CrossRef
22.
Vazquez-Vilar, G., Mosquera, C., & Jayaweera, S. K. (2010). Primary user enters the game: Performance of dynamic spectrum leasing in cognitive radio networks. IEEE Transactions on Wireless Communications, 10(12), 3625–3629.
23.
Foschini, G. J., & Miljanic, Z. (1993). A simple distributed autonomous power control algorithm and its convergence. IEEE Transactions on Vehicular Technology, 42(4), 641–646.CrossRef
24.
Bambos, N., Chen, C., & Pottie, G. J. (2000). Channel access algorithms with active link protection for wireless communication networks with powercontrol. IEEE/ACM Transactions on Networking, 8(5), 583–597.CrossRef
25.
Tan, C. W., Palomar, D. P., & Chiang, M. (2009). Energy–Robustness tradeoff in cellular network power control. IEEE/ACM Transactions on Networking, 17(3), 912–925.CrossRef
26.
Buzzi, S., & Saturnino, D. (2011). A game-theoretic approach to energy-efficient power control and receiver design in cognitive CDMA wireless networks. IEEE Journal of Selected Topics in Signal Processing, 5(1), 137–150.CrossRef
27.
Sorooshyari, S., Tan, C. W., & Chiang, M. (2012). Power control for cognitive radio networks: Axioms, algorithms, and analysis. IEEE/ACM Transactions on Networking, 20(3), 878–891.CrossRef
28.
Grandhi, S. A., Zander, J., & Yates, R. (1995). Constrained power control. Wireless Personal Communications, 1(4), 257–270.CrossRef
29.
Yates, R. D. (1995). A framework for up link power control in cellular radio systems. IEEE JSAC, 13(7), 1341–1347.MathSciNet
30.
Meshkati, F., Chiang, M., Poor, H. V., & Schwartz, S. (2006). A non-cooperative power control game for multi-carrier CDMA systems. IEEE Journal of Selected Areas in Communications, 24(6), 1115–1129.CrossRef
31.
Alpcan, T., Basar, T., Srikant, R., & Altman, E. (2002). CDMA uplink power control as a noncooperative game. Wireless Networks, 8, 659–670.CrossRefMATH
32.
Le Treust, M., & Lasaulce, S. (2010). A repeated game formulation of energy-efficient decentralized power control. IEEE Transactions on Wireless Communications, 9(9), 2860–2868.CrossRef
33.
Kakhbod, A., & Teneketzis, D. (2012). Power allocation and spectrum sharing in multi-user, multi-channel systems with strategic users. IEEE Transactions on Automatic Control, AC–57(9), 2338–2342.CrossRefMathSciNet
34.
Ren, S., & van der Schaar, M. (2011). Pricing and distributed power control in wireless relay networks. IEEE Transactions on Signal Processing, 59(6), 2913–2926.CrossRefMathSciNet
35.
Saraydar, C. U., Mandayam, N. B., & Goodman, D. J. (2002). Efficient power control via pricing in wireless data networks. IEEE Transactions on Communications, 50, 291–303.CrossRef
36.
37.
Sediq, A. B., et al. (2013). Optimal tradeoff between sum-rate efficiency and Jain’s fairness index in resource allocation. IEEE Transactions on Wireless Communications, 12(7), 3496–3509.CrossRef
38.
Chiang, M., Hande, P., Lan, T., & Tan, C. W. (2008). Power control in wireless cellular networks. Foundations and Trends in Networking, 2(4), 381–533.CrossRef
39.
Osborne, M. J., & Rubinstein, A. (1994). A Course in Game Theory. Cambridge, MA: MIT Press.MATH
40.
Varian, H. R. (1992). Microeconomic Analysis. New York: W.W. Norton & Company.
41.
Tse, D. N. C., & Viswanath, P. (2005). Fundamentals of Wireless Communication (1st ed.). Cambridge, UK: Cambridge University Press.CrossRefMATH
42.
Debreu, G. (1952). A social equilibrium existence theorem. Proceedings of the National Academy of Sciences of the USA, 38, 886–893.
43.
Horn, R. A., & Johnson, C. R. (1985). Matrix Analysis. Cambridge: Cambridge University Press.CrossRefMATH
Metadaten
Titel
Primary Network Interference Compensation-Based Dynamic Spectrum Leasing and Secondary Network Power Control
verfasst von
Weng-jiang Feng
Weiheng Jiang
Publikationsdatum
01.05.2015
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2015
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-014-2261-6

Weitere Artikel der Ausgabe 2/2015

Wireless Personal Communications 2/2015 Zur Ausgabe

OriginalPaper

Cooperative MIMO Based Reporting System for IEEE 802.22 Standard Over Fading Channels

OriginalPaper

Analytical Model for Estimating the Bounds in Predictive Entropy Coding of Digital Speech Signals

OriginalPaper

Analytical Models of Correlation Functions, DLL Discriminator Outputs and Multipath Envelope Errors for CosBOC(m, n) Modulated Signals in Coherent and Non-coherent Configurations

OriginalPaper

A Distributed Cluster Computing Energy-Efficient Routing Scheme for Internet of Things Systems

OriginalPaper

Number of Cooperative Wireless Nodes to Achieve a Desired BER

OriginalPaper

Hybrid Channel Estimation Technique with Reduced Complexity for LTE Downlink

Neuer Inhalt

    Bildnachweise