Low Complexity Sparse Channel Estimation Based on Compressed Sensing

Fei Zhou, Yantao Su, Xinyue Fan

Abstract


In wireless communication, channel estimation is a key technology to receive signal precisely. Recently, a new method named compressed sensing (CS) has been proposed to estimate sparse channel, which improves spectrum efficiency greatly. However, it is difficult to realize it due to its high computational complexity. Although the proposed Orthogonal Matching Pursuit (OMP) can reduce the complexity of CS, the efficiency of OMP is still low because only one index is identified per iteration. Therefore, to solve this problem, more efficient schemes are proposed. At first, we apply Generalized Orthogonal Matching Pursuit (GOMP) to channel estimation, which lower computational complexity by selecting multiple indices in each iteration. Then a more effective scheme that selects indices by least squares (LS) method is proposed to significantly reduce the computational complexity, which is a modified method of GOMP. Simulation results and theoretical analysis show the effectivity of the proposed algorithms.

Keywords


channel estimation; compressed sensing; computational complexity; index; atom

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References


Hlawatsch F, Matz G. Wireless communications over rapidly time-varying channels. USA: Academic Press. 2011: 199-236.

Wenbo Ding, Fang Yang, Changyong Pan, Linglong Dai, Jian Song. Compressive sensing based channel estimation for OFDM systems under long delay channels. IEEE Transactions on Broadcasting. 2014; 60(2): 313-321.

Jianzhong Huang, Berger CR, Shengli Zhou, Jie Huang. Comparison of basis pursuit algorithms for sparse channel estimation in underwater acoustic OFDM. OCEANS 2010 IEEE. Sydney. 2010: 1-6.

Teng Sun, Zhiqun Song, Yongjie Zhang. Matching pursuit based sparse channel estimation using pseudorandom sequences. Global Symposium on Millimeter Waves (GSMM). Harbin. 2012: 33-37.

Aboutorab N, Hardjawana W, Vucetic B. Application of compressive sensing to channel estimation of high mobility OFDM systems. IEEE International Conference on Communications (ICC). Budapest. 2013: 4946-4950.

Muqaibel AH, Alkhodary MT. Practical application of compressive sensing to ultra-wideband channels. IET on Communications. 2012; 6(16): 2534-2542.

Baohao Chen, Qimei Cui, Fan Yang, Jin Xu. A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system. International Conference on Wireless Communications and Signal Processing (WCSP). Hefei. 2014: 1-5.

Hui Xie, Andrieux G, Yide Wang, Diouris JF, Suili Feng. A novel effective compressed sensing based sparse channel estimation in OFDM system. IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC). KunMing. 2013: 1-6.

Xueyun He, Rongfang Song, Weiping Zhu. Pilot allocation for sparse channel estimation in MIMO-OFDM systems. IEEE Transactions on Circuits and Systems II: Express Briefs. 2013; 60(9): 612-616.

Xiang Ren, Wen Chen, Meixia Tao, Xiaofei Shao. Compressed channel estimation with joint pilot symbol and placement design for high mobility OFDM systems. International Workshop on High Mobility Wireless Communications (HMWC). Beijing. 2014: 38-42.

Ziji Ma, Hongli Liu, Higashino T, Okada M, Furudate H. Low-complexity channel estimation for ISDB-T over doubly-selective fading channels. International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS). Naha. 2013: 114-118.

Junjie Pan, Feifei Gao. Efficient channel estimation using expander graph based compressive sensing. IEEE International Conference on Communications (ICC). Sydney. 2014: 4542-4547.

Jian Wang, Kwon S, Shim B. Generalized orthogonal matching pursuit. IEEE Transactions on Signal Processing. 2012; 60(12): 6202-6216.

Candes EJ. The restricted isometry property and its implications for compresses sensing. Comptes Rendus Mathematique. 2008; 346(9): 589-592.

Baraniuk R, Davenport M, DeVore R, Wakin M. A simple proof of the restricted isometry property for random matrices. Constructive Approximation. 2008; 28(3): 253-263.

Tropp JA, Gilbert AC. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory. 2007; 53(12): 4655-4666.




DOI: http://dx.doi.org/10.12928/telkomnika.v14i2.3147

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