SCIENCE CHINA Information Sciences, Volume 59, Issue 10: 102306(2016) https://doi.org/10.1007/s11432-015-5441-4

TDD reciprocity calibration for multi-user massive MIMO systems with iterative coordinate descent

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  • ReceivedJul 18, 2015
  • AcceptedAug 14, 2015
  • PublishedDec 1, 2015


For massive multiple-input multiple-output (MIMO) antenna systems, time division duplexing (TDD) is preferred since the downlink precoding matrix can be obtained through the uplink channel estimation, thanks to the channel reciprocity. However, the mismatches of the transceiver radio frequency (RF) circuits at both sides of the link make the whole communication channel non-symmetric. This paper extends the total least square (TLS) method to the case of self-calibration, where only the antennas of the access points (APs) are involved to exchange the calibration signals with each other and the feedback from the user equipments (UEs) is not required. Then, the proof of the equivalence between the TLS method and the least square (LS) method is presented. Furthermore, to avoid the eigenvalue decomposition required by these two methods to obtain the calibration coefficients, a novel algorithm named as iterative coordinate descent (ICD) method is proposed. Theoretical analysis and simulation results show that the ICD method significantly reduces the complexity and achieves almost the same performance of the LS method.



This work was supported in part by National Basic Research Program of China (973) (Grant Nos. 2013CB336600, 2012CB316004), National Natural Science Foundation of China (NSFC) (Grant No. 61221002, 61271205), National High Technology Research and Development Program of China (863) (Grant No. 2014AA01A706), Colleges and Universities in Jiangsu Province Plans to Graduate Research and Innovation (Grant No. KYLX15\_0075).


[1] Rusek F, Persson D, Lau B K, et al. Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process Mag, 2013, 30: 40-60 Google Scholar

[2] Ma Z, Zhang Z, Ding Z, et al. Key techniques for 5G wireless communications: network architecture, physical layer, and MAC layer perspectives. Sci China Inf Sci, 2015, 58: 041301-60 Google Scholar

[3] Wang D M, Wang J Z, You X H, et al. Spectral efficiency of distributed MIMO systems. IEEE J Sel Areas Commun, 2013, 31: 2112-2127 CrossRef Google Scholar

[4] Wang J Z, Zhu H, Gomes N J. Distributed antenna systems for mobile communications in high speed trains. IEEE J Sel Areas Commun, 2012, 30: 675-683 CrossRef Google Scholar

[5] Zhu H. Performance comparison between distributed antenna and microcellular systems. IEEE J Sel Areas Commun, 2011, 29: 1151-1163 CrossRef Google Scholar

[6] Yang A, Jing Y D, Xing C W, et al. Performance analysis and location optimization for massive MIMO systems with circularly distributed antennas. arXiv preprint, 2014, arXiv: 1408-1163 Google Scholar

[7] Bourdoux A, Come B, Khaled N. Non-reciprocal transceivers in OFDM/SDMA systems: impact and mitigation. In: Proceedings of IEEE Radio and Wireless Conference (RAWCON 03), Boston, 2003. 183--186. Google Scholar

[8] Kaltenberger F, Jiang H, Guillaud M. Relative channel reciprocity calibration in MIMO/TDD systems. In: Proceedings of IEEE Future Network and Mobile Summit, Florence, 2010. 1--10. Google Scholar

[9] Kouassi B, Ghauri I, Deneire L. Reciprocity-based cognitive transmissions using a MU massive MIMO approach. In: Proceedings of IEEE International Conference on Communications (ICC), Budapest, 2013. 2738--2742. Google Scholar

[10] Huang F, Geng J, Wang Y. Performance analysis of antenna calibration in coordinated multi-point transmission system. In: Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Taipei, 2010. 1--5. Google Scholar

[11] Su L Y, Yang C Y, Wang G, et al. Retrieving channel reciprocity for coordinated multi-point transmission with joint processing. IEEE Trans Commun, 2014, 62: 1541-1553 CrossRef Google Scholar

[12] Shepard C, Yu H, Anand N. Argos: practical many-antenna base stations. In: Proceedings of the 18th annual International Conference on Mobile Computing and Networking, Istanbul, 2012. 53--64. Google Scholar

[13] Rahul H S, Kumar S, Katabi D. JMB: scaling wireless capacity with user demands. In: Proceedings of the ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, Helsinki, 2012. 235--246. Google Scholar

[14] Rogalin R, Bursalioglu O Y, Papadopoulos H C. Hardware-impairment compensation for enabling distributed large-scale MIMO. In: Proceedings of IEEE Information Theory and Applications Workshop (ITA), San Diego, 2013. 1--10. Google Scholar

[15] Rogalin R, Bursalioglu O Y, Papadopoulos H, et al. Scalable synchronization and reciprocity calibration for distributed multiuser MIMO. IEEE Trans Wirel Commun, 2014, 13: 1815-1831 CrossRef Google Scholar

[16] Golub G H, van C F. Matrix Computations. Baltimore: Johns Hopkins University Press, 2012. Google Scholar

[17] Golub G H. Some modified matrix eigenvalue problems. Siam Rev, 1973, 15: 318-334 CrossRef Google Scholar

[18] Bertsekas D P, Tsitsiklis J N. Parallel and Distributed Computation: Numerical Methods. Upper Saddle River: Prentice-Hall, 1989. Google Scholar

[19] Luo Z Q, Tseng P. On the convergence of the coordinate descent method for convex differentiable minimization. J Optimiz Theory Appl, 1992, 72: 7-35 CrossRef Google Scholar

[20] Liu J X, Wang D M. An improved dynamic clustering algorithm for multi-user distributed antenna system. In: Proceedings of IEEE International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, 2009. 1--5. Google Scholar

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