Adaptive Algorithm Based on Compressive Sensing to Improve the Channel Estimation of M-MIMO Systems

Subject Areas : Wireless communication

Mohammad Ali  Abedi ¹ (Department of Mechanical, Electrical and Computer Engineering- Science and Research Branch, Islamic Azad University, Tehran, Iran)
Afrooz  Haghbin ² (Department of Mechanical, Electrical and Computer Engineering- Science and Research Branch, Islamic Azad University, Tehran, Iran)
Farbod  Razzazi ³ (Department of Mechanical, Electrical and Computer Engineering- Science and Research Branch, Islamic Azad University, Tehran, Iran)

Received: 2022-06-22 Accepted : 2022-10-20 Published : 2022-10-20

Keywords: Compressive Sensing, Channel Estimation, Multiple-input multiple-output, required pilot, spatial sparsity,

Abstract :

To overcome the problem of channel estimation in massive multiple-input multiple-output (M-MIMO) systems, in this paper we propose a downlink link channel estimation scheme in frequency-division duplex (FDD) based on structured compressive sensing to reduce the pilot required by which Intrinsic spatial sparsity of M-MIMO delay channels are amplified. For this purpose, first, after discussing the different methods of channel estimation and examining the existing challenges, we define our roadmap and propose our algorithm, in which we estimate the channel based on the greedy orthogonal matching pursuit (OMP) algorithm. In this algorithm, spatial correlation between the channel impulse response of different transmitter antennas is used for accurate channel estimation. This algorithm obtains the channel sparsity in an adaptive way, which negates the ideal assumption of the previous works that the channel sparsity is in hand. In this case, this algorithm estimates the channel with good accuracy in cases when the exact amount of channel sparsity is not known. Finally, we present simulations that demonstrate the ability of this method to reduce the required pilot. The simulations show that the proposed channel estimation reliably obtains the channel sparsity level and the support set compared to similar methods.

References:

[1]   M.K. Ozdemir, H. Arslan, "Channel estimation for wireless OFDM systems", IEEE Communications Surveys and Tutorials, vol. 9, no. 2, pp. 18–48, July 2007 (doi: 10.1109/COMST.2007.382406).

[2]   N.D. Lahbib, M. Cherif, M. Hizem, R. Bouallegue, "Massive MIMO uplink channel estimation using compressive sensing", Proceeding of the IEEE/SoftCOM, pp. 1-6, Split, Croatia, Sept. 2019 )doi: 10.2391­9­/­S­OF­TCO­M.2019.8903860(.

[3]   S. Jacobsson, G. Durisi, M. Coldrey, T. Goldstein, C. Studer, "Quantized precoding for massive MU-MIMO", IEEE Trans. on Communications, vol. 65, no. 11, pp. 4670–4684, July 2017 (doi: 10.1109/TCO­MM.20­17.2­723­000).

[4]   N. Nouri, M. J. Azizipour, K. Mohamed-Pour, "A compressed CSI estimation approach for FDD massive MIMO systems", Proceeding of the IEEE/ ICEE, pp. 1-6,  Tabriz, Iran, Aug. 2020 )doi: 10.1109/ICEE50­131.­20­2­0.9260725(.

[5]   L. Jingzhi, W. Kai, L. Haibo, "Distributed compressed sensing of doubly selective channel in massive MIMO systems", Proceeding of the IEEE/WCCCT, pp. 21–25, Dalian, China, Jan.  2021 )doi: 10.1109/WCCCT5­209­1.2­021.00012(.

[6]   A. Waseem, A.N. Malik, A. Sardar, M. Arshad, H. Anis, I.M. Qureshi, "Compressive sensing based channel estimation for massive MIMO communication systems", Proceeding of the IEEE/WCMC, pp. 1-15, Shanghai, China, April 2019 )doi:10.1155/2019/6374764(.

[7]   W. Zeng, Y. He, B. Li, S. Wang, "Sparsity learning-based CSI feedback for FDD massive MIMO systems", IEEE Wireless Communication Letters, vol. 10, no. 3, pp. 585–588, Mar. 2021 )doi: 10.1109/LWC.20­20.30­388­93(.

[8] A. Akbarpour-Kasgari, M. Ardebilipour, "Massive MIMO-OFDM channel estimation via distributed compres­s­ed sensing", IEEE Wireless Communications Letters, vol. 8, no. 2, pp. 376-379, April 2019 (doi: 10.1109/L­W­C.2­018.2873339).

[9]  Y. Mei, Z. Gao, "CS-based CSIT estimation for downlink pilot decontamination in multi-cell FDD massive MIMO", Proceeding of the IEEE/ICCC, pp. 1-5, China, Aug. 2021 (doi: 10.1109/ICCC527­77.20­21.9­58­0­3­9­8­).

[10] Y. Huang, Y. He, Q. Luo, L. Shi, Y. Wu, "Channel estimation in MIMO–OFDM systems based on a new      adaptive greedy algorithm", IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 29-32, Feb. 2019 (doi: 10.1109/LWC.2018.2848916).

[11] R. Yin, X. Zhou, A. Wang, C. Zhong, C. Wu,  X. Chen, "Adaptive channel estimation and tracking for URA-based massive MIMO systems", IEEE Access, vol. 8, pp. 54213–54224, March 2020 (doi: 10.1109/ACCESS­.2­02­0.2981396).

[12] S. Oymak, A. Jalali, M. Fazel, Y. C. Eldar, B. Hassibi, "Simultaneously structured models with application to sparse and low-rank matrices", IEEE Trans. on Information Theory, vol. 61, no. 5, pp. 2886-2908, May 2015 (doi: 10.1109/TIT.2015.2401574).

[13] M.F. Duarte, Y.C. Eldar, "Structured compressed sensing: From theory to applications", IEEE Trans. on Signal Processing, vol. 59, no. 9, pp. 4053–4085, Sept. 2011 (doi: 10.1109/TSP.2011.2161982).

[14] W. Xu, Y. Zhao, "Reducing MMV-based OMP channel estimation for massive MIMO OFDM systems", Proceeding of the IEEE/CISCE, pp. 289-293, Haikou, China, July 2019 (doi: 10.1109/CISCE.2019.00072).

[15] E.J. Candès, T. Tao, "Decoding by linear programming", IEEE Trans. on Information Theory, vol. 51, no. 12, pp. 4203–4215, Dec. 2005 (doi: 10.1109/TIT.2005.858979).

[16] J.A. Tropp, A.C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit", IEEE Trans. on Information Theory, vol. 53, no. 12, pp. 4655–4666, Dec. 2007 (doi: 10.1109/TIT.2007.­90­91­08).

[17] H. Xie, F. Gao, S. Jin, "An overview of low-rank channel estimation for massive MIMO systems", IEEE Access, vol. 4, pp. 7313–7321, Nov. 2016 (doi: 10.1109/ACCESS.2016.2623772).

[18] N.D. Lahbib, M. Cherif, M. Hizem, R. Bouallegue, "Channel estimation for TDD uplink massive MIMO systems via compressed sensing", Proceeding of the IEEE/ IWCMC, pp. 1680–1684, Tangier, Morocco, June 2019 (doi: 10.1109/IWCMC.2019.8766722).

[19] D. Zhao, T. Han, "Low-Complexity compressed sensing downlink channel estimation for multi-antenna terminals in FDD massive MIMO systems", IEEE Access, vol. 8, pp. 130183–130193, July 2020 )doi: 10.11­09/AC­CESS.2020.3008175(.

[20] B. Wang, L. Li, "Sparsity adaptive channel estimation algorithm based on compressed sensing", Proceeding of the IEEE/ISCEIC, pp. 30-33, Nanjing, China, Aug. 2021 (doi: 10.1109/ISCEIC53685.2021.00014).

[21] N.D. Lahbib, M. Cherif, M. Hizem, R. Bouallegue, "Channel estimation for TDD uplink massive MIMO systems via compressed sensing", Proceeding of the IEEE/IWCMC, pp. 1680-1684, Tangier, Morocco, June 2019 (doi: 10.1109/IWCMC.2019.8766722).

[22] L. Chen, A. Liu, X. Yuan, "Structured turbo compressed sensing for massive MIMO channel estimation using a markov prior", IEEE Trans. on Vehicular Technology, vol. 67, no. 5, pp. 4635–4639, May 2018 (doi: 10.1­10­9/T­VT.2017.2787708).

[23] X. Kuai, L. Chen, X. Yuan, A. Liu, "Structured turbo compressed sensing for downlink massive MIMO-OFDM channel estimation", IEEE Trans. on Wireless Communications, vol. 18, no. 8, pp. 3813-3826, Aug. 2019 (doi: 10.1109/TWC.2019.2917905).

[24] A. Liu, F. Zhu, V.K.N. Lau, "Closed-loop autonomous pilot and compressive CSIT feedback resource adaptation in multi-user FDD massive MIMO systems", IEEE Trans. on Signal Processing, vol. 65, no. 1, pp. 173–183, Jan. 2017 )doi: 10.1109/TSP.2016.2616326(.

[25] Y. Huang, Y. He, W. He, L. Shi, T. Cheng, Y. Sui, "Channel estimation in massive MIMO systems based on generalized block adaptive matching pursuit algorithm", IEEE Wireless Communications Letters, vol. 9, no. 12, pp. 2098–2101, Aug. 2020 )doi: 10.1109/LWC.2020.3013689(.

[26] Z. Gao, L. Dai, Z. Lu, C. Yuen, Z. Wang, "Super-resolution sparse MIMO-OFDM channel estimation based on spatial and temporal correlations", IEEE Communications Letters, vol. 18, no. 7, pp. 1266–1269, July 2014 )doi: 10.1109/LCOMM.2014.2325027(.

[27] C. Qi, L. Wu, "Uplink channel estimation for massive MIMO systems exploring joint channel sparsity", IET Electronics Letters, vol. 50, no. 23, pp. 1770–1772, Nov. 2014 (doi: 10.1049/el.2014.2769).

[28] H. Minn, N. Al-Dhahir, Y. Li, "Optimal training signals for MIMO OFDM channel estimation in the presence of frequency offset and phase noise", IEEE Trans. on Communication, vol. 54, no. 10, pp. 1754–1759, Oct. 2006 )doi: 10.1109/TCOMM.2006.881358(.

[29] C.C. Tseng, J.Y. Wu, T.S. Lee, "Enhanced compressive downlink CSI recovery for FDD massive MIMO systems using weighted block-minimization", IEEE Trans. on Communications, vol. 64, no. 3, pp. 1055–1067, March 2016 (doi:10.1109/TCOMM.2016.2520945).

[30] X. Rao, V.K.N. Lau, "Compressive sensing with prior support quality information and application to massive MIMO channel estimation with temporal correlation", IEEE Trans. on Signal Processing, vol. 63, no. 18, pp. 4914–4924, Sept. 2015 (doi: 10.1109/TSP.2015.2446444).

[31] L. Dai, Z. Wang, "Spectrally efficient time-frequency training OFDM for MIMO systems", Proceeding of the IEEE/WCNC, vol. 31, no. 2, pp. 3753–3757, Shanghai, China, April 2013 )doi: 10.1109/WCNC.2013.65­551­7­2(.

[32] F. Wan, W.P. Zhu, M.N.S. Swamy, "Semi-blind most significant tap detection for sparse channel estimation of OFDM systems", IEEE Trans. on Circuits and Systems I Regular Papers, vol. 57, no. 3, pp. 703–713, March 2010 )doi: 10.1109/TCSI.2009.2023765(.

[33] C.R. Berger, Z. Wang, J. Huang, S. Zhou, "Application of compressive sensing to sparse channel estimation", IEEE Communications Magazins, vol. 48, no. 11, pp. 164–174, Nov. 2010) doi: 10.1109/MCOM.2010.56­21­9­84(.

[34] C. Qi, Y. Huang, S. Jin, L. Wu, "Sparse channel estimation based on compressed sensing for massive MIMO systems", Proceeding of the IEEE/ICC, pp. 4558–4563, London, UK, June 2015 (doi: 10.1109/ICC.20­15.72­49­041).

[35] X. Gao, L. Dai, Y. Hu, Y. Zhang, Z. Wang, "Low-complexity signal detection for large-scale MIMO in optical wireless communications", IEEE Journals on  Selelected Areas in Communications, vol. 33, no. 9, pp. 1903-1912, Sept. 2015 (doi: 10.1109/JSAC.2015.2457211).

[36]S. Lyu, X. Guan, "Low-complexity MMSE signal detection algorithm based on BiCGSTAB method for uplink massive MIMO systems", Proceeding of the IEEE/ICAIT, pp. 285-289, Chengdu, China, Nov. 2017 (doi: 10.1109/ICAIT.2017.8388931).

[37] W. Jinming, X. Dexin, B. Baodong, "A new method for solving line equations with large sparse symmetric and indefinite coefficients matrix", IEEE Trans. on Magnetics, vol. 40, no. 2, pp. 1069-1071, March 2004, (doi: 10.1109/TMAG.2004.825437).

[38] D.L. Donoho, M. Elad, "Optimally sparse representation in general dictionaries via minimization", Proceedings of the National Academy of Sciences of the USA, vol. 100, no. 5, pp. 2197-2202, Feb. 2003 (doi: 10.1073/pnas.0437847100).