Simplified p-norm-like constraint LMS algorithm for efficient estimation of underwater acoustic channels
- 130 Downloads
- 6 Citations
Abstract
Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as: multipath, ambient noise, time-frequency selective fading. The exploitation of sparsity contained in underwater acoustic channels provides a potential solution to improve the performance of underwater acoustic channel estimation. Compared with the classic l 0 and l 1 norm constraint LMS algorithms, the p-norm-like (l p ) constraint LMS algorithm proposed in our previous investigation exhibits better sparsity exploitation performance at the presence of channel variations, as it enables the adaptability to the sparseness by tuning of p parameter. However, the decimal exponential calculation associated with the p-norm-like constraint LMS algorithm poses considerable limitations in practical application. In this paper, a simplified variant of the p-norm-like constraint LMS was proposed with the employment of Newton iteration method to approximate the decimal exponential calculation. Numerical simulations and the experimental results obtained in physical shallow water channels demonstrate the effectiveness of the proposed method compared to traditional norm constraint LMS algorithms.
Keywords
p-norm-like constraint underwater acoustic channels LMS algorithm sparsity exploitationPreview
Unable to display preview. Download preview PDF.
References
- Akyildiz I, Pompili D, Melodia T (2005). Underwater acoustic sensor networks: Research challenges. Ad Hoc Networks, 3(3), 257–279.CrossRefGoogle Scholar
- Angelosante D, Bazerque JA, Giannakis GB (2010). Online adaptive estimation of sparse signals: where RLS meets the l1-norm. IEEE Trans. Signal Process, 58(7), 3436–3447.MathSciNetCrossRefGoogle Scholar
- Chitre MS, Shahabodeen S, Stojanovic M (2008). Underwater acoustic communications and networking: Recent advances and future challenges. Marine Technology Society, 42(1), 103–116.CrossRefGoogle Scholar
- Cotter SF, Rao BD (2002). Sparse channel estimation via matching pursuit with application to equalization. IEEE Trans. Commun., 50(3), 374–377.CrossRefGoogle Scholar
- Gu Y, Jin J, Mei J (2009). l0 norm constraint LMS algorithm for sparse system identification. IEEE Signal Processing Letters, 16(9), 774–777.CrossRefGoogle Scholar
- Hosein M, Massoud BZ, Christian J (2009). A fast approach for over complete sparse decomposition based on smoothed l0 norm. IEEE Trans Signal Process, 57(1), 289–301.MathSciNetCrossRefGoogle Scholar
- Jin J, Gu Y, Mei S (2010). A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework. IEEE J. Sel. Topics Signal Process, 4(2), 409–420.CrossRefGoogle Scholar
- Kalouptsidis N, Mileounis G, Babadi B, Tarokh T (2011). Adaptive algorithms for sparse system identification. Signal Processing, 91(8), 1910–1919.MATHCrossRefGoogle Scholar
- Li W, Preisig JC (2007). Estimation of rapidly time-varying sparse channels. IEEE J. Ocean. Eng., 32(4), 927–939.CrossRefGoogle Scholar
- Naylor PA, Cui J, Brookes M (2006). Adaptive algorithms for sparse echo cancellation. Signal Processing, 86(6), 1182–1192.MATHCrossRefGoogle Scholar
- Rao BD, Delgado KK (1999). An affine scaling methodology for best basis selection. IEEE Trans. Signal Process, 47(1), 187–200.MathSciNetMATHCrossRefGoogle Scholar
- Richard FK (1971). Improved Newton iteration for integral roots. Math. Comput. 25(114), 299–304.CrossRefGoogle Scholar
- Shi K, Shi P (2010). Convergence analysis of sparse LMS algorithms with l1-norm penalty based on white input signal. Signal Processing, 90(12), 3289–3293.MATHCrossRefGoogle Scholar
- Shi K, Shi P (2011). Adaptive sparse Volterra system identification with l0-norm penalty. Signal Processing, 91(10), 2432–2436.MATHCrossRefGoogle Scholar
- Singer AC, Nelson JK, Kozat SS (2009). Signal processing for underwater acoustic communications. IEEE Communications Magazine, 47(1), 90–96.CrossRefGoogle Scholar
- Stojanovic M (2005). Retrofocusing techniques for high rate acoustic communications. J. Acoust. Soc. Am., 117(3), 1173–1185.MathSciNetCrossRefGoogle Scholar
- Stojanovic M (2008). Efficient processing of acoustic signals for high-rate information transmission over sparse underwater channels. Physical Communication, 1(2), 146–161.CrossRefGoogle Scholar
- Taylor GD (1970). Optimal starting approximations for Newton’s method. J. Approximation Theory, 3(2), 156–163.MathSciNetMATHCrossRefGoogle Scholar
- Wu FY, Tong F (2013). Gradient optimization p-norm-like constraint LMS algorithm for sparse system estimation. Signal Processing, 93, 967–971.CrossRefGoogle Scholar
- Zeng WJ, XU W (2012). Fast estimation of sparse doubly spread acoustic channels. Journal of Acoustical Society of America, 131(1), 303–317.CrossRefGoogle Scholar
- Zhang YB, Zhao JW, GIO YC, LI JM(2011). Blind adaptive MMSE equalization of underwater acoustic channels based on the linear prediction method. Journal of Marine Science and Application, 10, 113–120.CrossRefGoogle Scholar