Skip to main content Accessibility help
×
Hostname: page-component-797576ffbb-xg4rj Total loading time: 0 Render date: 2023-12-05T15:45:27.172Z Has data issue: false Feature Flags: { "corePageComponentGetUserInfoFromSharedSession": true, "coreDisableEcommerce": false, "useRatesEcommerce": true } hasContentIssue false

4 - Interference modeling for cognitive femtocells

Published online by Cambridge University Press:  05 May 2013

Alberto Rabbachin
Affiliation:
European Commission
Tony Q. S. Quek
Affiliation:
Singapore University of Technology and Design
Hyundong Shin
Affiliation:
Kyung Hee University
Moe Z. Win
Affiliation:
Massachusetts Institute of Technology
Tony Q. S. Quek
Affiliation:
Singapore University of Technology and Design
Guillaume de la Roche
Affiliation:
Mindspeed Technologies
İsmail Güvenç
Affiliation:
Florida International University
Marios Kountouris
Affiliation:
SUPÉLEC (Ecole Supérieure d'Electricité)
Get access

Summary

Introduction

The possibility of increasing coverage and capacity of radio cellular systems by deploying femtocell base stations is strongly dependent on the capability of avoiding interference with the macrocell network (macrocell users and macrocell base stations). A centralized radio resource allocation procedure controlling the spectrum allocation of the multi-tier network would ensure a perfect coexistence between the macrocell and the femtocell networks. However, such a centralized system is of very high complexity. Considering the distributed nature of the femtocell network, with femtocell base stations placed without planning, a distributed resource allocation system is therefore desirable. In the broader field of cognitive radio networks, frequency band sharing can be based on different approaches. For less dynamic systems, access to the spectrum can be based on the information provided by a central database where geographical information on the spectrum usage is stored. In this case, the cognitive device is aware of its geographical position and can decide which frequency bands can be used to establish a communication link. For the case of femtocells, since the spectrum usage from the macrocell system is quite dynamic and changes frequently, a spectrum allocation relying on database information is not feasible. As for distributed secondary networks, cognitive radio resource management (CRRM) is a solution for a femtocell user (FU) to detect and access the idle spectrum [1-4]. A way to implement the CRRM is to activate a dedicated signaling channel between the macrocell and femtocell networks. The busy channel assessment can be based on the detection of a preamble shared between the macrocell and femtocell networks.

Type
Chapter
Information
Small Cell Networks
Deployment, PHY Techniques, and Resource Management
, pp. 82 - 95
Publisher: Cambridge University Press
Print publication year: 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] J., Xiang, M. Y., Zhang, T., Skeie, and L., Xie, “Downlink spectrum sharing for cognitive radio femtocell networks,” IEEE Syst. J., vol. 4, no. 4, pp. 524–34, Dec. 2010.Google Scholar
[2] G., Gur, S., Bayhan, and F., Alagoz, “Cognitive femtocell networks: an overlay architecture for localized dynamic spectrum access,” IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 62–70, Aug. 2010.Google Scholar
[3] S.-Y., Lien, Y.-Y., Lin, and K.-C., Chen, “Cognitive and game-theoretical radio resource management for autonomous femtocells with QoS guarantees,” IEEE Trans. Wireless Commun., vol. 10, no. 7, pp. 2196–206, July 2011.Google Scholar
[4] S., Al-Rubaye, A., Al-Dulaimi, and J., Cosmas, “Cognitive femtocell,” IEEE Vehicular Technology Magazine, vol. 6, no. 1, pp. 44–51, Mar. 2011.Google Scholar
[5] A., Ghasemi and E. S., Sousa, “Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs,” IEEE Commun. Mag., vol. 46, no. 4, pp. 32–9, Apr. 2008.Google Scholar
[6] D., Cabric, S. M., Mishra, and R. W., Brodersen, “Implementation issues in spectrum sensing for cognitive radios,” in Proc. Asilomar Conf. on Signals, Systems, and Computers (ASILOMAR), Pacific, Grove, Nov. 2004, pp. 772–6.Google Scholar
[7] A., Sonnenschein and P. M., Fishman, “Radiometric detection of spread-spectrum signals in noise,” IEEE Trans. Aerosp. Electron. Syst., vol. 28, no. 3, pp. 654–60, July 1992.Google Scholar
[8] ECC, “Technical requirements for UWB DAA (detect and avoid) devices to ensure the protection of radiolocation services in the bands 3.1–3.4 GHz and 8.5–9 GHz and BWA terminals in the band 3.4–4.2 GHz,” 2008.
[9] R., Tandra and A., Sahai, “SNR walls for signal detection,” IEEE J. Sel. Topics Signal Proc., vol. 2, no. 1, pp. 4–17, Feb. 2008.Google Scholar
[10] R., Tandra, S. M., Mishra, and A., Sahai, “What is a spectrum hole and what does it take to recognize one?IEEE Proceedings, vol. 97, no. 5, pp. 824–48, May 2009.Google Scholar
[11] G. V., Trunk, “Further results on the detection of targets in non-Gaussian sea clutter,” IEEE Trans. Aerosp. Electron. Syst., vol. 7, no. 3, pp. 553–6, May 1971.Google Scholar
[12] G. V., Trunk and S. F., George, “Detection of targets in non-Gaussian sea clutter,” IEEE Trans. Aerosp. Electron. Syst., vol. 6, no. 5, pp. 620–8, Sep. 1970.Google Scholar
[13] C. L., Nikias and M., Shao, Signal Processing with Alpha-Stable Distributions and Applications. Wiley-Interscience, 1995.Google Scholar
[14] M., Shao and C., Nikias, “Signal processing with fractional lower order moments: stable processes and their applications,” IEEE Proceedings, vol. 81, no. 7, pp. 986–1010, 1993.Google Scholar
[15] G., Samoradnitsky and M., Taqqu, Stable Non-Gaussian Random Processes. Chapman and Hall, 1994.Google Scholar
[16] E., Salbaroli and A., Zanella, “Interference analysis in a Poisson field of nodes of finite area,” IEEE Trans. Veh. Technol., vol. 58, no. 4, pp. 1776–83, May 2009.Google Scholar
[17] H., Inaltekin, M., Chiang, H. V., Poor, and S. B., Wicker, “The behavior of unbounded path-loss models and the effect of singularity on computed network characteristics,” IEEE J. Sel. Areas Commun. (JSAC), vol. 27, no. 7, pp. 1078–92, Sep. 2009.Google Scholar
[18] A., Ghasemi and E. S., Sousa, “Interference aggregation in spectrum-sensing cognitive wireless networks,” IEEE J. Sel. Topics Signal Proc., vol. 2, no. 1, pp. 41–56, Feb. 2008.Google Scholar
[19] R., Menon, R. M., Buehrer, and J. H., Reed, “On the impact of dynamic spectrum sharing techniques on legacy radio systems,” IEEE Trans. Wireless Commun., vol. 7, no. 11, pp. 4198–207, Nov. 2008.Google Scholar
[20] W., Ren, Q., Zhao, and A., Swami, “Power control in spectrum overlay networks: how to cross a multi-lane highway,” IEEE J. Sel. Areas Commun. (JSAC), vol. 27, no. 7, pp. 1283–96, Sep. 2009.Google Scholar
[21] A., Rabbachin, T. Q. S., Quek, H., Shin, and M. Z., Win, “Cognitive network interference,” IEEE J. Sel. Areas Commun. (JSAC), vol. 29, no. 2, pp. 480–93, Feb. 2011.Google Scholar
[22] M., Beil, F., Fleischer, S., Paschke, and V., Schmidt, “Statistical analysis of the three-dimensional structure of centromeric heterochromatin in interphase nuclei,” J. Micros., vol. 217, pp. 60–8, Jan. 2005.Google Scholar
[23] S., Chandrasekhar, “Stochastic problems in physics and astronomy,” Reviews of Modern Physics, vol. 15, no. 1, pp. 1–89, Jan. 1943.Google Scholar
[24] M. Y., Vardi, L., Shepp, and L., Kaufman, “A statistical model for positron emission tomography,” J. Am. Statist. Assoc., vol. 80, no. 389, pp. 8–20, Mar. 1985.Google Scholar
[25] F., Baccelli and B., Błaszczyszyn, Stochastic Geometry and Wireless Networks, Volume I – Theory, ser. Foundations and Trends in Networking. NoW Publishers, 2009.Google Scholar
[26] J. F., Kingman, Poisson Processes. Oxford University Press, 1993.Google Scholar
[27] M. Z., Win, P. C., Pinto, and L. A., Shepp, “A mathematical theory of network interference and its applications,” IEEE Proceedings, vol. 97, no. 2, pp. 205–30, Feb. 2009.Google Scholar
[28] P., Carr, H., Geman, D. B., Madan, and M., Yor, “The fine structure of asset returns: an empirical investigation,” J. Bus., vol. 75, no. 2, pp. 305–32, Apr. 2002.Google Scholar
[29] A., Rabbachin, T. Q. S., Quek and M. Z., Win, “Statistical modeling of cognitive network interference,” Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, pp. 1–6, 1–6 Dec. 2010.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×