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Channel Estimation for Movable Antenna Communication Systems: A Framework Based on Compressed Sensing (2312.06969v1)

Published 12 Dec 2023 in cs.IT, eess.SP, and math.IT

Abstract: Movable antenna (MA) is a new technology with great potential to improve communication performance by enabling local movement of antennas for pursuing better channel conditions. In particular, the acquisition of complete channel state information (CSI) between the transmitter (Tx) and receiver (Rx) regions is an essential problem for MA systems to reap performance gains. In this paper, we propose a general channel estimation framework for MA systems by exploiting the multi-path field response channel structure. Specifically, the angles of departure (AoDs), angles of arrival (AoAs), and complex coefficients of the multi-path components (MPCs) are jointly estimated by employing the compressed sensing method, based on multiple channel measurements at designated positions of the Tx-MA and Rx-MA. Under this framework, the Tx-MA and Rx-MA measurement positions fundamentally determine the measurement matrix for compressed sensing, of which the mutual coherence is analyzed from the perspective of Fourier transform. Moreover, two criteria for MA measurement positions are provided to guarantee the successful recovery of MPCs. Then, we propose several MA measurement position setups and compare their performance. Finally, comprehensive simulation results show that the proposed framework is able to estimate the complete CSI between the Tx and Rx regions with a high accuracy.

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References (30)
  1. E. Telatar, “Capacity of multi-antenna Gaussian channels,” Eur. Trans. Telecomm., vol. 10, no. 6, pp. 585–595, 1999.
  2. A. Paulraj, D. Gore, R. Nabar, and H. Bolcskei, “An overview of MIMO communications - a key to gigabit wireless,” Proc. IEEE, vol. 92, no. 2, pp. 198–218, Feb. 2004.
  3. G. Stuber, J. Barry, S. McLaughlin, Y. Li, M. Ingram, and T. Pratt, “Broadband MIMO-OFDM wireless communications,” Proc. IEEE, vol. 92, no. 2, pp. 271–294, Feb. 2004.
  4. L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: Benefits and challenges,” IEEE J. Sel. Top. Signal Process., vol. 8, no. 5, pp. 742–758, Oct. 2014.
  5. L. Zhu, J. Zhang, Z. Xiao, X. Cao, D. O. Wu, and X.-G. Xia, “Millimeter-wave NOMA with user grouping, power allocation and hybrid beamforming,” IEEE Trans. Wirel. Commun., vol. 18, no. 11, pp. 5065–5079, Nov. 2019.
  6. F. Adachi and R. Takahashi, “Multi-user MIMO using ZF-based multiplexing coordinated with user-wise spatial diversity,” in Proc. IEEE Veh. Technol. Conf. (VTC2020-Fall), Nov. 2020, pp. 1–5.
  7. M. A. Albreem, M. Juntti, and S. Shahabuddin, “Massive MIMO detection techniques: A survey,” IEEE Commun. Surv. Tutor., vol. 21, no. 4, pp. 3109–3132, Aug. 2019.
  8. X. Zhang, A. Molisch, and S.-Y. Kung, “Variable-phase-shift-based RF-baseband codesign for MIMO antenna selection,” IEEE Trans. Signal Process., vol. 53, no. 11, pp. 4091–4103, Nov. 2005.
  9. L. Zhu, W. Ma, and R. Zhang, “Modeling and performance analysis for movable antenna enabled wireless communications,” arXiv preprint arXiv:2210.05325, 2022.
  10. ——, “Movable antennas for wireless communication: Opportunities and challenges,” IEEE Commun. Mag., early access, Oct. 16, 2023, doi: 10.1109/MCOM.001.2300212.
  11. W. Ma, L. Zhu, and R. Zhang, “MIMO capacity characterization for movable antenna systems,” IEEE Trans. Wirel. Commun., early access, Sep. 7, 2023, doi: 10.1109/TWC.2023.3307696.
  12. L. Zhu, W. Ma, B. Ning, and R. Zhang, “Movable-antenna enhanced multiuser communication via antenna position optimization,” arXiv preprint arXiv:2302.06978, 2023.
  13. Z. Xiao, X. Pi, L. Zhu, X.-G. Xia, and R. Zhang, “Multiuser communications with movable-antenna base station: Joint antenna positioning, receive combining, and power control,” arXiv preprint arXiv:2308.09512, 2023.
  14. L. Zhu, W. Ma, and R. Zhang, “Movable-antenna array enhanced beamforming: Achieving full array gain with null steering,” IEEE Commun. Lett., early access, Oct. 11, 2023, doi: 10.1109/LCOMM.2023.3323656.
  15. Y. Wu, D. Xu, D. W. K. Ng, W. Gerstacker, and R. Schober, “Movable antenna-enhanced multiuser communication: Optimal discrete antenna positioning and beamforming,” arXiv preprint arXiv:2308.02304, 2023.
  16. Z. Cheng, N. Li, J. Zhu, X. She, C. Ouyang, and P. Chen, “Sum-rate maximization for movable antenna enabled multiuser communications,” arXiv preprint arXiv:2309.11135, 2023.
  17. X. Chen, B. Feng, Y. Wu, D. W. K. Ng, and R. Schober, “Joint beamforming and antenna movement design for moveable antenna systems based on statistical CSI,” arXiv preprint arXiv:2308.06720, 2023.
  18. Z. Cheng, N. Li, J. Zhu, X. She, C. Ouyang, and P. Chen, “Movable antenna-empowered aircomp,” arXiv preprint arXiv:2309.12596, 2023.
  19. J. Lee, G.-T. Gil, and Y. H. Lee, “Channel estimation via orthogonal matching pursuit for hybrid MIMO systems in millimeter wave communications,” IEEE Trans. Commun., vol. 64, no. 6, pp. 2370–2386, Jun. 2016.
  20. X. Li, J. Fang, H. Li, and P. Wang, “Millimeter wave channel estimation via exploiting joint sparse and low-rank structures,” IEEE Trans. Wirel. Commun., vol. 17, no. 2, pp. 1123–1133, Feb. 2018.
  21. Z. Gao, L. Dai, Z. Wang, and S. Chen, “Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO,” IEEE Trans. Signal Process., vol. 63, no. 23, pp. 6169–6183, Dec. 2015.
  22. W. Ma, L. Zhu, and R. Zhang, “Compressed sensing based channel estimation for movable antenna communications,” IEEE Commun. Lett., early access, Aug. 31, 2023, doi: 10.1109/LCOMM.2023.3310535.
  23. S. Li, F. Gao, G. Ge, and S. Zhang, “Deterministic construction of compressed sensing matrices via algebraic curves,” IEEE Trans. Inf. Theory, vol. 58, no. 8, pp. 5035–5041, Aug. 2012.
  24. L. Applebaum, S. D. Howard, S. Searle, and R. Calderbank, “Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery,” Appl. Comput. Harmon. Anal., vol. 26, no. 2, pp. 283–290, 2009.
  25. W. U. Bajwa, J. D. Haupt, G. M. Raz, S. J. Wright, and R. D. Nowak, “Toeplitz-structured compressed sensing matrices,” in Proc. IEEE Workshop Stat. Signal Process., Aug. 2007, pp. 294–298.
  26. R. R. Naidu, P. Jampana, and C. S. Sastry, “Deterministic compressed sensing matrices: Construction via euler squares and applications,” IEEE Trans. Signal Process., vol. 64, no. 14, pp. 3566–3575, Jul. 2016.
  27. E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: Universal encoding strategies?” IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406–5425, Dec. 2006.
  28. E. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203–4215, Dec. 2005.
  29. R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, “A simple proof of the restricted isometry property for random matrices,” Constr. Approx., vol. 28, no. 3, pp. 253–263, Dec. 2008.
  30. J.-S. Hu and Z.-X. Song, “A new pilot design criterion of channel estimation based on compressive sensing,” in Proc. Int. Comput. Conf. Wavelet Act. Media Technol. Inf. Process., ICCWAMTIP, Dec. 2016, pp. 119–122.
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