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Considering that the conventional channel interpolation method with sparse and irregular spaced pilots will lead to an error floor in underwater acoustic (UWA) orthogonal frequency division multiple access (OFDMA) uplink communications, a method for sparse channel estimation and pilot optimization is proposed in this paper. A compressed sensing (CS) algorithm is utilized for sparse channel impulse response estimation, which performs well in sparse and irregular spaced pilots and significantly decreases the channel estimation error. Besides, the pilots’ pattern and power joint optimization algorithm based on the random search technique is proposed for the minimum mutual coherence criterion in CS theory, which further improves the performance of CS estimation algorithm. During each iteration step, we randomly pick a pilots’ pattern from the subcarrier index set and a pilots’ power subset from the available power set. Then we perform this step iteratively within a certain searching time. Finally, the local optimal solution of the objective function for minimizing mutual coherence is considered as the feasible pilots’ pattern and power. Simulation results show that the convergence performance of the pilots’ pattern and power joint optimization algorithm is much better than that of the pilots’ pattern optimization algorithm. Furthermore, the channel estimation error of the proposed method is much lower than that of conventional least-squares channel estimator based on linear interpolation, CS channel estimator without pilot optimization, and CS channel estimator merely with pilots’ pattern optimization in channels of different multipath delay spreads. Finally, performance of the proposed method is demonstrated in the UWA uplink OFDMA systems with interleaved and generalized carrier assignment schemes respectively in the two-user case in a pool experiment. Experimental results show that the proposed method decreases dramatically the bit error rate in both carrier assignment schemes, and simultaneous reception for two users is achieved when signal noise ratio is larger than 10 dB.
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Keywords:
- underwater acoustic communication /
- orthogonal frequency division multiple access /
- channel estimation /
- compressed sensing
[1] Ying Y Z, Ma L, Guo S M 2011 Chin. Phys. B 20 054301
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[11] Tu K, Duman TM, Stojanovic M, Proakis J G 2013 IEEE J. Ocean Eng. 38 333
[12] Huang Y, Wan L, Zhou S L, Wang Z H, Huang J Z 2014 Phys. Commun. 13 156
[13] Tu K, Duman T M, Stojanovic M, Proakis, J G 2011 49th Annual Allerton Conference on Communication, Control, and Computing University of Illinois Monticello, IL, USA, September 28-30, 2011, p633
[14] Candes E J, Wakin M B 2008 Signal Process. Mag. 25 21
[15] Kunis S, Rauhut H 2008 Foundations of Comput. Math. 8 737
[16] Candes E J 2008 Comptes Rendus Math. 346 589
[17] Tropp J A, Gilbert A C 2007 IEEE Trans. Inf. Theory 53 4655
[18] Liu S Z, Qiao G, Ismail A 2013 J. Acoust. Soc. Am. 133 300
[19] Liu S Z, Qiao G, Yin Y L 2013 Acta Phys. Sin. 62 144303 (in Chinese) [刘凇佐, 乔钢, 尹艳玲 2013 62 144303]
[20] Donoho D L, Elad M, Temlyakov V N 2006 IEEE Trans. Inf. Theory 52 6
[21] He X Y, Song R F, Zhou K Q 2012 J. Commun. 32 85 (in Chinese) [何雪云, 宋荣方, 周克琴 2012 通信学报 32 85]
[22] Qi C H, Wu L N, Zhu P C 2014 J. Electronic & Information Technology 36 763 (in Chinese) [戚晨皓, 吴乐南, 朱鹏程 2014 电子与信息学报 36 763]
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[1] Ying Y Z, Ma L, Guo S M 2011 Chin. Phys. B 20 054301
[2] Yin J W, Yang S, Du P Y, Yu Y 2012 Acta Phys. Sin. 61 064302 (in Chinese) [殷敬伟, 杨森, 杜鹏宇, 余赟, 陈阳 2012 61 064302]
[3] Khalil I M, Gadallah Y, Hayajneh M, Khreishah A 2012 Sensors 12 8782
[4] Jabba D, Labrador M 2009 IEEE Conference Oceans 2009 Bremen, Germany, May 11-14 2009, p1
[5] Morelli M, Kuo C, Pun M-O 2007 Proceedings of the IEEE 95 1394
[6] Ma Y, Tafazolli R 2007 IEEE Trans. Signal Process. 55 1568
[7] Fertl P, Matz G 2007 IEEE International Conference on Acoustics, Speech and Signal Processing Honolulu, Hawaii, USA, April 15-20 2007, p297
[8] Raghavendra M R, Lior E, Bhashyam S, Giridhar K 2007 IEEE Trans. Signal Process. 55 5370
[9] Huang W C, Pan C H, Li C P, Li H J 2010 IEEE Trans. Broadcasting 56 58
[10] Berger C R, Zhou S L, Preisig J C, Willett P 2010 IEEE Trans. Signal Process. 58 1708
[11] Tu K, Duman TM, Stojanovic M, Proakis J G 2013 IEEE J. Ocean Eng. 38 333
[12] Huang Y, Wan L, Zhou S L, Wang Z H, Huang J Z 2014 Phys. Commun. 13 156
[13] Tu K, Duman T M, Stojanovic M, Proakis, J G 2011 49th Annual Allerton Conference on Communication, Control, and Computing University of Illinois Monticello, IL, USA, September 28-30, 2011, p633
[14] Candes E J, Wakin M B 2008 Signal Process. Mag. 25 21
[15] Kunis S, Rauhut H 2008 Foundations of Comput. Math. 8 737
[16] Candes E J 2008 Comptes Rendus Math. 346 589
[17] Tropp J A, Gilbert A C 2007 IEEE Trans. Inf. Theory 53 4655
[18] Liu S Z, Qiao G, Ismail A 2013 J. Acoust. Soc. Am. 133 300
[19] Liu S Z, Qiao G, Yin Y L 2013 Acta Phys. Sin. 62 144303 (in Chinese) [刘凇佐, 乔钢, 尹艳玲 2013 62 144303]
[20] Donoho D L, Elad M, Temlyakov V N 2006 IEEE Trans. Inf. Theory 52 6
[21] He X Y, Song R F, Zhou K Q 2012 J. Commun. 32 85 (in Chinese) [何雪云, 宋荣方, 周克琴 2012 通信学报 32 85]
[22] Qi C H, Wu L N, Zhu P C 2014 J. Electronic & Information Technology 36 763 (in Chinese) [戚晨皓, 吴乐南, 朱鹏程 2014 电子与信息学报 36 763]
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