http://iet.metastore.ingenta.com
1887

Enabling polarisation filtering in wireless communications: models, algorithms and characteristics

Enabling polarisation filtering in wireless communications: models, algorithms and characteristics

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

To suppress co-channel interference in polarisation-enabled wireless communication systems, this work aims to provide an interference suppression scheme by exploiting polarisation domain, besides the state-of-the-art temporal, frequency, spatial and code domains. System models, algorithms, characteristics and applications of polarisation filtering (PF) for co-channel interference suppressions for polarisation-enabled (e.g. orthogonal dually polarised antennas) wireless communications are investigated. Specifically, four system models for PF using subspace analysis are established and discussed. The four proposed system models are categorised based on different statistic characteristics of the target signal and that of the interfering signal: both the target signal and interference are temporal deterministic, the target signal is deterministic whereas interference is temporal random, the target signal is random whereas interference is deterministic and both the target signal and interference are random, respectively. Based on the statistic characteristics and subspace theory, the detailed PF implementation for each model is analysed and the closed-form filtering operator is given. It is also shown that the PF implementation for each model can be attained by using one of the zero-forcing matched subspace processing, decorrelating matched subspace processing or Wiener subspace processing. Furthermore, relationship among these four models indicates that, under certain conditions, the implementation of the other three models can be fulfilled by using the implementation of the first model. Numerical and simulation results show the effectiveness of the proposed scheme.

References

    1. 1)
      • 1. Mott, H.: ‘Polarization in antennas and radar’ (Wiley-Interscience, New York, 1986).
    2. 2)
      • 2. Michael, R., Partha, P., Robert, D.L.: ‘Tripling the capacity of wireless communications using electromagnetic polarization’, Nature, 2001, 498, pp. 316318.
    3. 3)
      • 3. Dietrich, C., Dietze, K., Nealy, J.: ‘Spatial, polarization, and pattern diversity for wireless handheld terminals’, IEEE Trans. Antennas Propag., 2001, 49, pp. 12711281.
    4. 4)
      • 4. Vaughan, R.: ‘Polarization diversity in mobile communications’, IEEE Trans. Veh. Technol., 2007, 39, pp. 177186.
    5. 5)
      • 5. Cao, B., Zhang, Q., Jin, L.: ‘Polarization division multiple access with polarization modulation for LOS wireless communications’, EURASIP J. Wirel. Commun. Netw., 2011, 77, pp. 19.
    6. 6)
      • 6. Cao, B., Mark, J.W., Zhang, Q.: ‘A polarization enabled cooperation framework for cognitive radio networking’. Proc. GLOBECOM, Anaheim, CA, USA, December 2012.
    7. 7)
      • 7. Poleman, A.: ‘Virtual polarisation adaptation-a method of increasing the detection capability of a radar system through polarisation-vector processing’, Proc. IEE F, 1981, 128, (5), pp. 261270.
    8. 8)
      • 8. Giuli, D.: ‘Suboptimum adaptive polarisation cancellers for dual-polarisation radars’, Proc. IEE F, 1988, 135, (1), pp. 6062.
    9. 9)
      • 9. Poelman, A.J.: ‘Polarisation-vector translation in radar systems’, IEE Proc. F, 1983, 130, (2), pp. 161165.
    10. 10)
      • 10. Mars, J., Glangeaud, F., Vanpe, J., Boelle, J.: ‘Wave separation by an oblique polarization filtering’. Proc. PSIP 1999, 1999, pp. 94100.
    11. 11)
      • 11. Mao, X., Liu, Y.: ‘Null phase-shift polarization filtering for high-frequency radar’, IEEE Trans. AES, 2007, 43, (4), pp. 13971408,.
    12. 12)
      • 12. Zhang, Q., Cao, B., Wang, J., Zhang, N.: ‘Polarization filtering technique based on oblique projections’, SCIENCE CHINA Inf. Sci., 2010, 53, pp. 10561066.
    13. 13)
      • 13. Cao, B., Zhang, Q., Liang, D., Wen, S., Jin, L., Zhang, Y.: ‘Blind adaptive polarization filtering based on oblique projection’. Proc ICC 2010, 2010, pp. 15.
    14. 14)
      • 14. Cao, B., Zhang, Q., Jin, L., Zhang, N.: ‘Oblique projection polarization filtering based interference suppressions for radar sensor networks’, EURASIP J. Wirel. Commun. Netw., 2010, 2010, Article ID 605103, 10 pages doi:10.1155/2010/605103.
    15. 15)
      • 15. Behrens, R., Scharf, L.: ‘Signal processing applications of oblique projection operators’, IEEE Trans. Signal Process., 1994, 42, (6), pp. 14131424.
    16. 16)
      • 16. Scharf, L., Friedlander, B.: ‘Matched subspace detectors’, IEEE Trans. Signal Process., 1994, 42, (8), pp. 21462157.
    17. 17)
      • 17. Scharf, L., McCloud, M.: ‘Blind adaptation of zero forcing projections and oblique pseudo-inverse for subspace detection and estimation when interference dominates noise’, IEEE Trans. Signal Process., 2002, 50, (12), pp. 29382946.
    18. 18)
      • 18. Oestges, C., Clerckx, B., Guillaud, M., Debbah, M.: ‘Dual-polarized wireless communications: from propagation models to system performance evaluation’, IEEE Trans. Wirel. Commun., 2008, 7, (10), pp. 40194031.
    19. 19)
      • 19. Kwon, S., Stüber, G.: ‘Geometrical theory of channel depolarization’, IEEE Trans. Veh. Technol., 2011, 60, (8), pp. 35423556.
    20. 20)
      • 20. Kay, M.: ‘Fundamentals of statistical signal processing, Volume 1: estimation theory’ (Prentice-Hall, New Jersey, 1993).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2012.0409
Loading

Related content

content/journals/10.1049/iet-com.2012.0409
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address