Variable-step-size based sparse adaptive filtering algorithm for channel estimation in broadband wireless communication systems
- Guan Gui^{1}Email author,
- Wei Peng^{2},
- Li Xu^{1},
- Beiyi Liu^{1} and
- Fumiyuki Adachi^{3}
https://doi.org/10.1186/1687-1499-2014-195
© Gui et al.; licensee Springer. 2014
Received: 17 December 2013
Accepted: 6 November 2014
Published: 22 November 2014
Abstract
Sparse channels exist in many broadband wireless communication systems. To exploit the channel sparsity, invariable step-size zero-attracting normalized least mean square (ISS-ZA-NLMS) algorithm was applied in adaptive sparse channel estimation (ASCE). However, ISS-ZA-NLMS cannot achieve a good trade-off between the convergence rate, the computational cost, and the performance. In this paper, we propose a variable step-size ZA-NLMS (VSS-ZA-NLMS) algorithm to improve the ASCE. The performance of the proposed method is theoretically analyzed and verified by numerical simulations in terms of mean square deviation (MSD) and bit error rate (BER) metrics.
Keywords
Sparse channel ZA-NLMS Invariable step size Variable step size ASCE1 Introduction
Channel structures in different mobile communication systems
Generations of mobile communication systems | 2G cellular (IS-95) | 3G cellular (WCDMA) | 4G/5G cellular (LTE-Advanced~) |
---|---|---|---|
Transmission bandwidth | 1.23 MHz | 10 MHz | 20 ~ 100 MHz |
Time delay spread (assume) | 0.4 μs | 0.4 μs | 0.4 μs |
Sampling channel length | 1 | 8 | 16 ~ 80 |
Number of nonzero taps | 1 | 4 | 6 |
Channel model | Dense | Approximate sparse | Sparse |
It is well known that step size is a critical parameter which determines the estimation performance, convergence rate, and computational cost. However, ISS-NLMS and ZA-ISS-NLMS adopt a fixed step size, and as a result, they are unable to achieve a good balance between steady-state estimation performance and convergence speed. Different from ISS-NLMS [4], variable step-size NLMS (VSS-NLMS) was first proposed to improve the estimation performance [11] without sacrificing the convergence speed. Variable step size is controlled by the instantaneous square error of each iteration, i.e., lower error will decrease the step size and vice versa. To the best of our knowledge, the application of sparse VSS-NLMS to simultaneously exploit the channel sparsity and control the step size has not been reported in the literature.
In this paper, we propose a zero-attracting VSS-NLMS (ZA-VSS-NLMS) algorithm for sparse channel estimation. The main contribution of this paper is to propose the ZA-VSS-NLMS using VSS rather than ISS for estimating spare channels. In addition, the step size of the proposed algorithm is updated in each iteration according to the error information. In the following, conventional ZA-ISS-NLMS is introduced and its drawback is analyzed at first. ZA-VSS-NLMS is then proposed using an adaptive step size to achieve a lower steady-state estimation error. To derive the adaptive step size, different from the traditional VSS-NLMS algorithm in [11], two practical problems are considered: sparse channel model and tractable independent assumptions [12]. At last, numerical simulations are carried out to evaluate the proposed algorithm in terms of two metrics: mean square deviation (MSD) and bit error rate (BER).
The remainder of this paper is organized as follows. A system model is described and ZA-ISS-NLMS algorithm is introduced in Section 2. In Section 3, ZA-VSS-NLMS algorithm is proposed. Numerical results are presented in Section 4 to evaluate the performance of the proposed ASCE method. Finally, we conclude the paper in Section 5.
2 ZA-ISS-NLMS algorithm
3 Proposed algorithm
where μ(n +1) is the VSS which is calculated from the estimation error and the variance of the additive noise. Comparing Equation 7 with Equation 4, it can be found that the step size is different, i.e., step size in Equation 4 is invariant while step size in Equation 7 is adaptively variant. There are two facts about μ(n) and ρ that should be noticed: 1) the variant step-size μ(n) is adopted to speed up the convergence speed in the case of large estimation error, while to ensure the stability in the case of small estimation error; 2) the parameter ρ, which depends on the initial step-size μ and regularization parameter λ, is utilized to exploit channel sparsity effectively. Otherwise, variant parameter ρ(n) = μ(n)λ may cause extracomputational complexity and ineffectiveness use of the channel sparsity.
- (A1):
Input vector x(t) and the additive noise z(t) are mutually independent at time t.
- (A2):
Input vector x(t) is a stationary sequence of independent zero mean Gaussian random variables with a finite variance${\mathrm{s}}_{\mathrm{x}}^{2}\text{.}$
- (A3):
z(t) is an independent zero mean random variables with variance${\mathrm{s}}_{\mathrm{z}}^{2}\text{.}$
- (A4):
$\tilde{\mathbf{h}}\left(n\right)$is independent of x(t).
4 Numerical simulations
Simulation parameters
Parameters | Values |
---|---|
Transmission bandwidth | W =40 MHz |
Delay spread | τ =0.8 μs |
Channel length | N =64 |
Number of nonzero coefficients | K =2 and 6 |
Distribution of nonzero coefficient | random Gaussian $\mathcal{C}\mathcal{N}\left(0,1\right)$ |
Threshold parameter for VSS-NLMS | $C=\left\{\begin{array}{c}\hfill 6.0\times {10}^{-6},\phantom{\rule{0.12em}{0ex}}\mathrm{\text{for}}\phantom{\rule{0.12em}{0ex}}\mathrm{\text{SNR}}=5\phantom{\rule{0.25em}{0ex}}\mathrm{dB}\hfill \\ \hfill 3.0\times {10}^{-6},\phantom{\rule{0.12em}{0ex}}\mathrm{\text{for}}\phantom{\rule{0.12em}{0ex}}\mathrm{\text{SNR}}=10\phantom{\rule{0.25em}{0ex}}\mathrm{dB}\hfill \\ \hfill 2.0\times {10}^{-6},\phantom{\rule{0.12em}{0ex}}\mathrm{\text{for}}\phantom{\rule{0.12em}{0ex}}\mathrm{\text{SNR}}=20\phantom{\rule{0.25em}{0ex}}\mathrm{dB}\hfill \end{array}\right.$ |
Received SNR E_{ s }/N_{0} | 0 ~ 40 dB |
Step size | μ = 0.5 and μ_{max} = 2 |
Regularization parameter | $\rho =0.0015\phantom{\rule{0.25em}{0ex}}{\sigma}_{n}^{2}$ |
Modulation schemes | 8 PSK, 16 PSK, 16 QAM, and 64 QAM |
Therefore, it has been confirmed that the proposed algorithm can achieve the advantages of good performance and fast convergence speed.
5 Conclusions
Step size is a key parameter for NLMS-based adaptive filtering algorithms to balance the steady-state estimation performance and convergence speed. Either ISS-NLMS or ZA-ISS-NLMS cannot update their step size in the process of adaptive error updating. In this paper, a ZA-VSS-NLMS filtering algorithm was proposed for channel estimation. Unlike the traditional algorithms, the proposed algorithm utilizes VSS which can update the step size adaptively according to the updating error. Therefore, the proposed method can achieve a better steady-state performance while keeping a comparable convergence speed when compared with the existing methods. Simulation results have been presented to confirm the effectiveness of the proposed method in terms of MSD and BER metrics.
Declarations
Acknowledgements
The authors would like to extend their appreciation to the anonymous reviewers for their constructive comments. This work was supported in part by the Japan Society for the Promotion of Science (JSPS) research activity start-up research grant (No. 26889050), Akita Prefectural University start-up research grant, as well as the National Natural Science Foundation of China under grants (Nos. 61401069, 61261048, and 61201273).
Authors’ Affiliations
References
- Adachi F, Tomeba H, Takeda K: Introduction of frequency-domain signal processing to broadband single-carrier transmissions in a wireless channel. IEICE Trans. Commun. 2009, E92-B(no. 9):2789-2808. 10.1587/transcom.E92.B.2789View ArticleGoogle Scholar
- Adachi F, Kudoh E: New direction of broadband wireless technology. Wirel. Commun. Mob. Comput. 2007, 7(no. 8):969-983. 10.1002/wcm.507View ArticleGoogle Scholar
- Dai L, Wang Z, Yang Z: Next-generation digital television terrestrial broadcasting systems: Key technologies and research trends. IEEE Commun. Mag. 2012, 50(6):150-158.View ArticleGoogle Scholar
- Widrow B, Stearns D: Adaptive Signal Processing. Prentice Hall, New Jersey; 1985.MATHGoogle Scholar
- Herdin M, Bonek E, Member S, Fleury BH, Czink N, Yin X, Ozcelik H: Cluster characteristics in a MIMO indoor propagation environment. IEEE Trans. Wirel. Commun. 2007, 6(no. 4):1465-1475.View ArticleGoogle Scholar
- Wyne S, Czink N, Karedal J, Almers P, Tufvesson F, Molisch A: A Cluster-Based Analysis of Outdoor-to-Indoor Office MIMO Measurements at 5.2 GHz,” IEEE 64th Vehicular Technology Conference (VTC-Fall). Montreal, Canada; 2006:1-5. doi:10.1109/VTCF.2006.15Google Scholar
- Vuokko L, Kolmonen V-M, Salo J, Vainikainen P: Measurement of large-scale cluster power characteristics for Geometric channel models. IEEE Trans. Antennas Propag. 2007, 55(no. 11):3361-3365.View ArticleGoogle Scholar
- Tibshirani R: Regression Shrinkage and Selection via the Lasso. J. R. Stat. Soc. 1996, 58(no. 1):267-288.MATHMathSciNetGoogle Scholar
- Gui G, Peng W, Adachi F: Improved adaptive sparse channel estimation based on the least mean square algorithm. IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China 2013, 3130-3134.Google Scholar
- Gui G, Adachi F: Improved adaptive sparse channel estimation using least mean square algorithm. EURASIP J. Wirel. Commun. Netw. 2013, 2013(no. 1):1-18. 10.1186/1687-1499-2013-1View ArticleGoogle Scholar
- Shin H, Sayed AH, Song W: Variable step-size NLMS and affine projection algorithms. IEEE Signal Process. Lett. 2004, 11(no. 2):132-135. 10.1109/LSP.2003.821722View ArticleGoogle Scholar
- Eweda E, Bershad NJ: Stochastic analysis of a stable normalized least mean fourth algorithm for adaptive noise canceling with a white Gaussian reference. IEEE Trans. Signal Process. 2012, 60(12):6235-6244.MathSciNetView ArticleGoogle Scholar
- Huang Z, Gui G, Huang A, Xiang D, Adachi F: Regularization selection methods for LMS-Type sparse multipath channel estimation. The 19th Asia-Pacific Conference on Communications (APCC), Bali Island, Indonesia 2013, 1-5.Google Scholar
- Gui G, Mehbodniya A, Adachi F: Least mean square/fourth algorithm for adaptive sparse channel estimation. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), London, UK 2013, 1-5.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.