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
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2016

01.01.2016

Multiple Base Stations Cooperation: A Novel Clustering Algorithm and Its Energy Efficiency

verfasst von: Chao Meng, Tian Liang, Wei Heng, Xiaoming Wang

Erschienen in: Wireless Personal Communications | Ausgabe 2/2016

Einloggen

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

search-config
loading …

Abstract

Multiple base stations (BSs) cooperation can effectively reduce the inter-cell interference and especially improve the performance of the cell-edge users, which has been regarded as an important technology in future wireless communication system. All BSs full cooperation is unaffordable for system overhead, so how to partition the BSs in the system into different clusters to cooperate with a low complexity is a challenging issue. In this paper, a novel dynamic clustering algorithm for multiple BSs cooperation in downlink is proposed, and system energy efficiency (EE) is investigated. Firstly, with equal power allocation per symbol and per antenna equal power constraint, the formulas of spectral efficiency (SE) and EE for the case of ideal transmit and the case of actual transmit are derived, respectively. In addition, a novel dynamic clustering algorithm based on channel norm is presented. By calculating the mutual interference matrix according to channel norm, for each clustering judgment, the BS which has the biggest element in the present interference matrix is selected as the leader BS. Then the rest BSs which have the larger interference coefficient with the leader BS are chosen to joint the cluster until the cluster is formed. The computational complexity of the proposed algorithm is analyzed. Simulation results show that EE of the proposed algorithm is better than that of the static clustering one and slightly worse than that of the decentralized algorithm but with a lower complexity.
Nächster Artikel Multi-function Current Differencing Cascaded Transconductance Amplifier (MCDCTA) and Its Application to Current-Mode Multiphase Sinusoidal Oscillator

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
Literatur
1.
Gesbert, D., Hanly, S., Huang, H., Shamai, S. S., Simeone, O., & Yu, W. (2010). Multi-cell MIMO cooperative networks: A new look at interference. IEEE Journal on Selected Areas in Communications, 28(9), 1380–1408.CrossRef
2.
Zhang, H., & Dai, H. (2004). Cochannel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks. EURASIP Journal on Wireless Communications and Networking, 2, 222–235.
3.
Zhang, J., & Andrews, J. G. (2010). Adaptive spatial intercell interference cancellation in multicell wireless networks. IEEE Journal on Selected Areas in Communications, 28(9), 1455–1468.CrossRef
4.
Zhang, R. (2010). Cooperative multi-cell block diagonalization with per-base-station power constraints. IEEE Journal on Selected Areas in Communications, 28(9), 1435–1445.CrossRef
5.
Zhang, J., Chen, R., Andrews, J. G., Ghosh, A., & Robert, W. H. J. (2009). Networked MIMO with clustered linear precoding. IEEE Transactions on Wireless Communications, 8(4), 1910–1921.CrossRef
6.
Ng, D. W. K., Lo, E. S., & Schober, R. (2012). Energy-efficient resource allocation in multi-cell OFDMA systems with limited backhaul capacity. IEEE Transactions on Wireless Communications, 11(10), 3618–3631.CrossRef
7.
Tolli, A., Pennanen, H., & Komulainen, P. (2011). Decentralized minimum power multi-cell beamforming with limited backhaul signaling. IEEE Transactions on Wireless Communications, 10(2), 570–580.CrossRef
8.
Huh, H., Tulino, A. M., & Caire, G. (2012). Network MIMO with linear zero-forcing beamforming: Large system analysis, impact of channel estimation, and reduced-complexity scheduling. IEEE Transactions on Information Theory, 58(5), 2911–2934.MathSciNetCrossRef
9.
Mikami, M., Miyashita, M., Miyajima, H., Hoshino, K., Yoshino, H., & Fujii, T. (2012). Field Evaluations on a prototype system of cooperative multi-cell MIMO transmission for asynchronous inter-site base station networks. In Proceedings of IEEE 75th Vehicular Technology Conference (VTC Spring) (pp. 1–5).
10.
Wang, X., Zhu, P., Sheng, B., & You, X. (2013). Energy-efficient downlink transmission in multi-cell coordinated beamforming systems. In Proceedings of IEEE Wireless Communications and Networking Conference (WCNC) (pp. 2554–2558).
11.
Ng, D. W. K., Lo, E. S., & Schober, R. (2012). Energy-efficient resource allocation for secure OFDMA systems. IEEE Transactions on Vehicular Technology, 61(6), 2572–2585.CrossRef
12.
Xiong, C., Li, G. Y., Zhang, S., Chen, Y., & Xu, S. (2012). Energy-efficient resource allocation in OFDMA networks. IEEE Transactions on Communications, 60(12), 3767–3778.CrossRef
13.
Han, S., Yang, C., Wang, G., & Lei, M. (2011). On the energy efficiency of base station sleeping with multicell cooperative transmission. In Proceedings of IEEE 22nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 2011) (pp. 1536–1540).
14.
Fehske, J., Marsch, P., & Fettweis, G. P. (2010). Bit per joule efficiency of cooperating base stations in cellular networks. In Proceedings of IEEE GLOBECOM Workshops (GC Wkshps) (pp. 1406–1411).
15.
Papadogiannis, A., & Alexandropoulos, G. C. (2010). The value of dynamic clustering of base stations for future wireless networks. In Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ 2010) (pp. 1–6).
16.
Sun, H., Zhang, X., & Fang, W. (2011). Dynamic cell clustering design for realistic coordinated multipoint downlink transmission. In Proceedings of IEEE 22nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 2011) (pp. 1331–1335).
17.
Chen, Y., Zhang, S., Xu, S., & Li, G. Y. (2011). Fundamental trade-offs on green wireless networks. IEEE Communications Magazine, 49(6), 30–37.CrossRef
18.
Yu, W., & Lan, T. (2007). Transmitter optimization for the multi-antenna downlink with per-antenna power constraints. IEEE Transactions on Signal Processing, 55(6), 2646–2660.MathSciNetCrossRef
19.
Mai, V. (2011). MISO capacity with per-antenna power constraint. IEEE Transactions on Communications, 59(5), 1268–1274.MathSciNetCrossRef
20.
Hasan, Z., Boostanimehr, H., & Bhargava, V. K. (2011). Green cellular networks: A survey, some research issues and challenges. IEEE Communications Surveys and Tutorials, 13(4), 524–540.CrossRef
21.
Wang, X., Vasilakos, A. V., Chen, M., Liu, Y., & Kwon, T. T. (2012). A survey of green mobile networks: Opportunities and challenges. ACM Mobile Networks and Applications, 17(1), 4–20.CrossRef
22.
Li, X., Wang, H., Meng, C., Wang, X., Liu, N., & You, X. (2013). Total energy minimization through dynamic station-user connection in macro-relay network. In Proceedings of IEEE Wireless Communications and Networking Conference (WCNC 2013) (pp. 697–702).
23.
Arnold, O., Richter, F., Fettweis, G., & Blume, O. (2010). Power consumption modeling of different base station types in heterogeneous cellular networks. In Proceedings of IEEE Future Network and Mobile Summit (pp. 1–8).
24.
Andreas, A., & Lu, W. S. (2007). Practical optimization: Algorithms and engineering applications. New York: Springer.
25.
Liu, J., & Wang, D. (2009). An improved dynamic clustering algorithm for multi-user distributed antenna system. In Proceedings of IEEE International Conference on Wireless Communications and Signal Processing (WCSP 2009) (pp. 1–5).
26.
Papadogiannis, A., Gesbert, D., & Hardouin, E. (2008). A dynamic clustering approach in wireless networks with multi-cell cooperative processing. In Proceedings of IEEE International Conference on Communications (ICC 2008) (pp. 4033–4037).
27.
Zhou, S., Gong, J., Niu, Z., Jia, Y., & Yang, P. (2009). A decentralized framework for dynamic downlink base station cooperation. In Proceedings of IEEE Global Telecommunications Conference (GLOBECOM 2009) (pp. 1–6).
28.
Zhou, S., Gong, J., Jia, Y., & Niu, Z. (2010). A decentralized clustering scheme for dynamic downlink base station cooperation. IEICE Transactions on Communications, 93(12), 3656–3659.CrossRef
29.
You, X., Wang, D., Zhu, P., & Sheng, B. (2011). Cell edge performance of cellular mobile systems. IEEE Journal on Selected Areas in Communications, 29(6), 1139–1150.CrossRef
Metadaten
Titel
Multiple Base Stations Cooperation: A Novel Clustering Algorithm and Its Energy Efficiency
verfasst von
Chao Meng
Tian Liang
Wei Heng
Xiaoming Wang
Publikationsdatum
01.01.2016
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2016
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-015-3118-3

Weitere Artikel der Ausgabe 2/2016

Wireless Personal Communications 2/2016 Zur Ausgabe

OriginalPaper

Joint Game Algorithm of Power Control and Channel Allocation Considering Channel Interval and Relay Transmission Obstacle for WSN

OriginalPaper

Dual Arm Patch Antenna for Wideband Applications Using Metamaterial

OriginalPaper

Printed Monopole Diversity Antenna for USB Dongle Applications

OriginalPaper

Mobility and Heterogeneity Aware Cluster-Based Data Aggregation for Wireless Sensor Network

OriginalPaper

Efficient Overlay Construction for Wireless Sensor Networks

OriginalPaper

A Range Based Localization System in Multihop Wireless Sensor Networks: A Distributed Cooperative Approach

Neuer Inhalt

    Bildnachweise