Skip to main content
Log in

Signal to Interference Ratio Based Antenna Selection for Spatial Multiplexing

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

To achieve reliable high throughput wireless links spatial multiplexing and diversity modes of multiple-input multiple-output are used in combination. Antenna selection (AS) has minimal complexity among other spatial diversity methods. Bell Labs Layered Space Time (BLAST) is a low complexity spatial multiplexing technique, especially when minimum mean squared error (MMSE) receivers are considered. However, optimal AS is known to be computationally intensive when used along with spatial multiplexing, since an orthogonal subset channel matrix is required to be found. Known suboptimal algorithms are still relatively complex and incur performance penalties. In this paper we propose a low complexity AS algorithm for BLAST. It uses an approximate signal to interference ratio metric as a heuristic measure to select a given number of antennas. It produces the selection choice after a single iteration only. A structure to reduce hardware complexity by reusing the MMSE equalizer block is also proposed. Such reuse can be applied to several AS algorithms. We compare the performance of the proposed algorithm against others using mean spectral efficiency (SE), 10 % outage SE, symbol error rate performance and implementation complexity. The impact of approximate expressions used in the proposed algorithm is also analyzed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Paulraj, A. (2003). Introduction to space time wireless communications. Cambridge: Cambridge University Press.

    Google Scholar 

  2. Telatar, I. (1999). Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications, 10, 585–595.

    Article  Google Scholar 

  3. Tse, D., & Viswanath, P. (2005). Fundamentals of wireless communication. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  4. Biglieri, E. (2007). MIMO wireless communications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  5. Molisch, A. (2003). MIMO systems with antenna selection—an overview. In Radio and wireless conference (RAWCON). (pp. 167–170).

  6. Molisch, A., & Win, M. (2004). MIMO systems with antenna selection. IEEE Microwave Magazine, 5(1), 46–56.

    Article  Google Scholar 

  7. Golden, G. D., & Foschini, G. J. (1999). Detection algorithm and initial laboratory results using the V-BLAST spacetime communication architecture. Electronics Letters, 35, 14–15.

    Article  Google Scholar 

  8. Zhang, R., & Cioffi, J. (2008). Approaching MIMO-OFDM capacity with zero-forcing V-BLAST decoding and optimized power, rate, and antenna-mapping feedback. IEEE Transactions on Signal Processing, 56(10), 5191–5203.

    Article  MathSciNet  Google Scholar 

  9. Lebrun, T. Y. G., & Faulkner, M. (2001). MIMO transmission over a time-varying TDD channel using SVD. IEEE Communications Letters, 37(22), 1363–1364.

    Google Scholar 

  10. Busche, H., et al. (2008). SVD-based MIMO precoding and equalization schemes for realistic channel knowledge: Design criteria and performance evaluation. Wireless Personal Communications, 48, 347–359.

    Article  Google Scholar 

  11. Lee, J., et al. (2009). MIMO Technologies in 3GPP LTE and LTE-Advanced. EURASIP Journal on Wireless Communications and Networking, 2009(302092), 1–10.

  12. Lee, K.-J., & Lee, I. (2008, May). Diversity analysis of coded SVD schemes for MIMO spatial multiplexing systems. In: IEEE international conference on communications, pp. 4703–4707.

  13. Gutierrez, A. C., & Stojanovic, M. (2004, Sep). Effect of channel estimation error on the performance of SVD-based MIMO communication system. In Fifteenth IEEE international symposium on personal, indoor and mobile radio communications, pp. 508–512.

  14. Gore, D., Heath, R. J., & Paulraj, A. (2002). Transmit selection in spatial multiplexing systems. IEEE Communications Letters, 6(11), 491–493.

    Article  Google Scholar 

  15. Zheng, L., & Tse, D. N. C. (2003). Diversity and multiplexing: A fundamental tradeoff in multiple-antenna channels. IEEE Transactions on Information Theory, 5(49), 1073–1096.

    Article  Google Scholar 

  16. Heath, R. J., & Love, D. (2005). Multimode antenna selection for spatial multiplexing systems with linear receivers. IEEE Transactions on Signal Processing, 53(8), 3042–3056.

    Article  MathSciNet  Google Scholar 

  17. Narasimhan, R. (2003). Spatial multiplexing with transmit antenna and constellation selection for correlated MIMO fading channels. IEEE Transactions on Signal Processing, 51(11), 2829–2838.

    Article  Google Scholar 

  18. Gore, D., Heath, R. J., & Paulraj, A. (2002). Transmit selection in spatial multiplexing systems. IEEE Communications Letters, 6(11), 491–493.

    Article  Google Scholar 

  19. Sudarshan, P., Mehta, N., Molisch, A., & Zhang, J. (2004). Antenna selection with RF pre-processing: Robustness to RF and selection non-idealities. In IEEE radio and wireless conference.

  20. ITU-R. (2009). Guidelines for evaluation of radio interface technologies for IMT-A. ITU-R, Tech. Rep. M.2135-1. http://www.itu.int/ITU-R/go/rsg5-imt-advanced

  21. Sandhu, S., Nabar, R., Gore, D., & Paulraj, A. (2000). Near-optimal selection of transmit antennas for a MIMO channel based on Shannon capacity. In Thirty-fourth asilomar conference on signals, systems and computers, Vol. 1, pp. 567–571.

  22. Gore, D., Nabar, R., & Paulraj, A. (2000). Selecting an optimal set of transmit antennas for a low rank matrix channel. In ICASSP, Vol. 5, pp. 2785–2788.

  23. Gorokhov, D. G. A., & Paulraj, A. (2003). Receive antenna selection for MIMO flat-fading channels: Theory and algorithms. IEEE Transactions on Information Theory, 49(10), 2687–2696.

    Article  MathSciNet  MATH  Google Scholar 

  24. Heath, R. J., Sandhu, S., & Paulraj, A. (2001). Antenna selection for spatial multiplexing systems with linear receivers. IEEE Communications Letters, 5(4), 142–144.

    Article  Google Scholar 

  25. Heath, R., & Paulraj, A. (2001). Antenna selection for spatial multiplexing systems based on minimum error rate. In Proceedings of IEEE international control conference. Helsinki, Finland., Vol. 7, pp. 2276–2280.

  26. Gharavi-Alkhansari, M., & Gershman, A. B. (2004). Fast antenna subset selection in MIMO systems. IEEE Transactions Signal Process, 52(2), 339–347.

    Article  MathSciNet  Google Scholar 

  27. Smith, P. J., et al. (2006). An analysis of low complexity algorithms for MIMO antenna selection. In IEEE. ICC. (pp. 1380–1385).

  28. Gorokhov, A. (2002). Antenna selection algorithms for MEA transmision systems. In ICASSP.

  29. Lebrun, G., Spiteri, S., & Faulkner, M. (2003). MIMO complexity reduction through antenna selection. In Proceedings of ATNAC.

  30. Choi, Y.-S., Molisch, A., Win, M., & Winters, J. (2003). Fast algorithms for antenna selection in MIMO systems. In IEEE 58th vehicular technology conference. VTC-Fall., Vol. 3, pp. 1733–1737.

  31. Mogensen et al., P. (2007, Apr). LTE capacity compared to the Shannon bound. In IEEE 65th vehicular technology conference. VTC-Spring. 1234–1238.

  32. Hanzo, L., et al. (2011). MIMO-OFDM for LTE, WiFi and Wimax. Colorado: Wiley.

    Google Scholar 

  33. Chung, S. T., et al. (2001, Oct). Approaching eigenmode BLAST channel capacity using V-BLAST with rate and power feedback. In IEEE VTC, Vol. 2, pp. 915–919.

  34. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, Tech. Rep. http://cm.bell-labs.com/cm/ms/what/shannonday/shannon1948.pdf

  35. Yoo, T., & Goldsmith, A. (2006). Capacity and power allocation for fading MIMO channels with channel estimation error. IEEE Transactions on Information Theory, 52(5), 2203–2214.

    Article  MathSciNet  MATH  Google Scholar 

  36. Blahut, R. E. (2010). Fast algorithms for signal processing. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  37. Woods, R., McAllister, J., Lightbody, G., & Yi, Y. (2008). FPGA-based implementation of signal processing systems. Colorado: Wiley.

    Book  Google Scholar 

  38. Fixed Point Square Root (1994). Apple, Technical Report, October. www.realitypixels.com/turk/computergraphics/FixedSqrt.pdf

  39. Tetsushi, A. (2009). 3GPP self-evaluation methodology and results: self-evaluation results. 3GPP, Technical Report 3GPP TR36.814 ver 1.5.0.

  40. Barry, J. R., Lee, E. A., & Messerschmitt, D. G. (2004). Digital communications. Berlin: Springer.

    Book  Google Scholar 

  41. Zhang, H., Dai, H., Zhou, Q., & Hughes, B. L. (2006). On the diversity order of spatial multiplexing systems with transmit antenna selection: A geometrical approach. IEEE Transactions on Information Theory, 52(12), 5297–5311.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Subhendu Batabyal.

Appendix

Appendix

Note: \(\varXi \) represents complexity of the abbreviated algorithm mentioned in the superscript in terms of the number of operations of the specific type (RM/RA etc.) mentioned in the subscript. The algorithm MaxCap is abbreviated as ‘MXCP’. \(\varPsi \) and \(\varPsi _{A}\) represent the multiplication and addition complexity (respectively) of the operation \(\hat{\mathbf {L}}= \hat{\mathbf {H}}^{\mathrm{H}}\hat{\mathbf {H}}\) (see Table 1).

$$\begin{aligned} \Psi (p,q)&= \frac{pq}{6}(3q+1). \end{aligned}$$
(16)
$$\begin{aligned} {\Psi _{A}}(p,q)&= \frac{1}{4}(7q^{2}p - 3pq - 2q^{2}). \end{aligned}$$
(17)

Part 1: Complexity Expressions Without Reuse

$$\begin{aligned} \varXi ^{MXCP}_{RM}&= 3\varXi ^{MXCP}_{CM} \end{aligned},$$
(18)

where

$$\begin{aligned} \varXi ^{MXCP}_{CM}&= \frac{2}{3}{\mathrm {{M}}}_{\mathrm{T}}\mathrm {{M}}_{\mathrm{R}}+ \frac{4}{3}+2{\mathrm {{M}}}_{\mathrm{T}}\nonumber \\&\quad+ \max ({\mathrm {{M}}}_{{\mathrm{R}}_{\mathrm{s}}}-2,0)({\mathrm {{M}}}_{\mathrm{T}}^2 + \frac{2}{3}{\mathrm {{M}}}_{\mathrm{T}}+ \frac{1}{3}) \nonumber \\&\quad+ ({\mathrm {{M}}}_{{\mathrm{R}}_{\mathrm{s}}}-1)\Psi (1,{\mathrm {{M}}}_{\mathrm{T}}) + (\frac{2}{3}{\mathrm {{M}}}_{\mathrm{T}}+ 1)({\mathrm {{M}}}_{\mathrm{R}}- 1) \nonumber \\&\quad+({\mathrm {{M}}}_{\mathrm{R}}+ \frac{2}{3})\max ({\mathrm {{M}}}_{{\mathrm{R}}_{{\mathrm{s}}}}-2,0)({\mathrm {{M}}}_{\mathrm{R}}- \frac{\mathrm {{M}}_{{\mathrm{R}}_{\mathrm{s}}}+1}{2}).\end{aligned}$$
(19)
$$\begin{aligned} \varXi ^{MXCP}_{RA}&= 5\varXi ^{MXCP}_{CM} + 2\varXi ^{MXCP}_{RA1},\end{aligned}$$

where

$$\begin{aligned} \varXi ^{MXCP}_{RA1}&= 1 + \max ({\mathrm {{M}}}_{{\mathrm{R}}_{\mathrm{s}}}- 2,0)(1 + 2{\mathrm {{M}}}_{\mathrm{T}}({\mathrm {{M}}}_{\mathrm{T}}-1)) \nonumber \\&\quad+ ({\mathrm {{M}}}_{\mathrm{R}}-1)(\mathrm {{M}}_{\mathrm{T}}+1) \nonumber \\&\quad+ {\mathrm {{M}}}_{\mathrm{T}}\max ({\mathrm {{M}}}_{{\mathrm{R}}_{\mathrm{s}}}-2,0)(2{\mathrm {{M}}}_{\mathrm{R}}-{\mathrm {{M}}}_{{\mathrm{R}}_{\mathrm{s}}}-1).\end{aligned}$$
(20)
$$\begin{aligned} \varXi ^{SBS}_{RM}&= 3\varXi ^{SBS}_{CM}, \end{aligned}$$

where

$$\begin{aligned} \varXi ^{SBS}_{CM}&= \Psi ({\mathrm {{M}}}_{\mathrm{R}},\mathrm {{M}}_{\mathrm{T}}) + \frac{2}{3}{\mathrm {{M}}}_{\mathrm{T}}^2. \end{aligned}$$
(21)
$$\begin{aligned} \varXi ^{SBS}_{RA}&= {\Psi _{A}}({\mathrm {{M}}}_{\mathrm{R}},{\mathrm {{M}}}_{\mathrm{T}}) + {\mathrm {{M}}}_{\mathrm{T}}({\mathrm {{M}}}_{\mathrm{T}}-2). \end{aligned}$$
(22)
$$\begin{aligned} \varXi ^{HB}_{RM}&= 3\varXi ^{HB}_{CM} \hbox {, where } \end{aligned}$$
(23)
$$\begin{aligned} \varXi ^{HB}_{CM}&= \Psi ({\mathrm {{M}}}_{\mathrm{R}},{\mathrm {{M}}}_{\mathrm{T}}) + \frac{2}{3}{\mathrm {{M}}}_{\mathrm{T}}({\mathrm {{M}}}_{\mathrm{T}}-1). \end{aligned}$$
(24)
$$\begin{aligned} \varXi ^{HB}_{RA}&= {\Psi _{A}}({\mathrm {{M}}}_{\mathrm{R}},{\mathrm {{M}}}_{\mathrm{T}}) + ({\mathrm {{M}}}_{\mathrm{T}}-1)(\mathrm {{M}}_{\mathrm{T}}-2). \end{aligned}$$
(25)
$$\begin{aligned} \varXi ^{XBS}_{RM}&= 3\Psi ({\mathrm {{M}}}_{\mathrm{R}},{\mathrm {{M}}}_{\mathrm{T}}) + {\mathrm {{M}}}_{\mathrm{T}}({\mathrm {{M}}}_{\mathrm{T}}-1). \end{aligned}$$
(26)
$$\begin{aligned} \varXi ^{XBS}_{RA}&= 2{\Psi _{A}}({\mathrm {{M}}}_{\mathrm{R}},{\mathrm {{M}}}_{\mathrm{T}}) + \frac{\mathrm {{M}}_{\mathrm{T}}}{2}({\mathrm {{M}}}_{\mathrm{T}}-1). \end{aligned}$$
(27)
$$\begin{aligned} \varXi ^{NBS}_{RM}&= 2{\mathrm {{M}}}_{\mathrm{T}}{\mathrm {{M}}}_{\mathrm{R}}. \end{aligned}$$
(28)
$$\begin{aligned} \varXi ^{NBS}_{RA}&= {\mathrm {{M}}}_{\mathrm{T}}(2{\mathrm {{M}}}_{\mathrm{R}}-1). \end{aligned}$$
(29)
$$\begin{aligned} \varOmega (p,q)&= \frac{1}{6}[(p(p+1)(2p+1))-(q(q+1)(2q+1))]. \end{aligned}$$
(30)
$$\begin{aligned} \varXi ^{SBS,iter}_{RM}&= (2 + \frac{3{\mathrm {{M}}}_{\mathrm{R}}}{2})\varOmega ({\mathrm {{M}}}_{\mathrm{T}},{\mathrm {M}}_{\mathrm{T}_\mathrm{s}}) \nonumber \\&\quad+\frac{\mathrm {{M}}_{\mathrm{R}}}{4}({\mathrm {{M}}}_{\mathrm{T}}- {\mathrm {M}}_{\mathrm{T}_{\mathrm{s}}})(\mathrm {{M}}_{\mathrm{T}}+ {\mathrm {M}}_{\mathrm{T}_{\mathrm{s}}}+ 1).\end{aligned}$$
(31)
$$\begin{aligned} \varXi ^{SBS,iter}_{RA}&= {\mathrm {{M}}}_{\mathrm{R}}\varOmega (\mathrm {{M}}_{\mathrm{T}},{\mathrm {M}}_{\mathrm{T}_{\mathrm{s}}}) \nonumber \\&\quad +(\mathrm {{M}}_{\mathrm{T}}- {\mathrm {M}}_{\mathrm{T}_{\mathrm{s}}})(\mathrm {{M}}_{\mathrm{T}}+ {\mathrm {M}}_{\mathrm{T}_\mathrm{s}}+ 1). \end{aligned}$$
(32)

Part 2: Complexity Expressions With Reuse

Note: the underlined variables indicate complexity with reuse.

$$\begin{aligned} \underline{\varXi ^{SBS}_{RM}}&= 2{\mathrm {{M}}}_{\mathrm{T}}^{2}. \end{aligned}$$
(33)
$$\begin{aligned} \underline{\varXi ^{SBS}_{RA}}&= {\mathrm {{M}}}_{\mathrm{T}}(\mathrm {{M}}_{\mathrm{T}}-1). \end{aligned}$$
(34)
$$\begin{aligned} \underline{\varXi ^{HB}_{RM}}&= 2{\mathrm {{M}}}_{\mathrm{T}}(\mathrm {{M}}_{\mathrm{T}}-1). \end{aligned}$$
(35)
$$\begin{aligned} \underline{\varXi ^{HB}_{RA}}&= (\mathrm {{M}}_{\mathrm{T}}-1)(\mathrm {{M}}_{\mathrm{T}}-2). \end{aligned}$$
(36)
$$\begin{aligned} \underline{\varXi ^{XBS}_{RM}}&= {\mathrm {{M}}}_{\mathrm{T}}(\mathrm {{M}}_{\mathrm{T}}-1). \end{aligned}$$
(37)
$$\begin{aligned} \underline{\varXi ^{XBS}_{RA}}&= \frac{\mathrm {{M}}_{\mathrm{T}}}{2}(\mathrm {{M}}_{\mathrm{T}}-1). \end{aligned}$$
(38)
$$\begin{aligned} \underline{\varXi ^{MXCP}_{RM}}&= 3(\varXi ^{MXCP}_{CM} - (\mathrm {{M}}_{{\mathrm{R}}_{\mathrm{s}}}-1)\Psi (1,{\mathrm {{M}}}_{\mathrm{T}}) - \frac{2}{3}{\mathrm {{M}}}_{\mathrm{T}}\mathrm {{M}}_{\mathrm{R}}). \nonumber \\ \underline{\varXi ^{MXCP}_{RA}}&= \frac{5}{3}\underline{\varXi ^{MXCP}_{RM}} + 2\varXi ^{MXCP}_{RA1}. \end{aligned}$$
(39)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Batabyal, S., Das, S.S. Signal to Interference Ratio Based Antenna Selection for Spatial Multiplexing. Wireless Pers Commun 83, 975–993 (2015). https://doi.org/10.1007/s11277-015-2435-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2435-x

Keywords

Navigation