Related Papers

Overview

E.G. Larsson, “MIMO detection methods: How they work,” IEEE Signal Process. Mag., vol. 26, no. 3, pp. 91-95, May 2009.

Semidefinite relaxation detectors

Concepts

Z.-Q. Luo, W.-K. Ma, A.M.-C. So, Y. Ye, and S. Zhang, “Semidefinite relaxation of quadratic optimization problems,” IEEE Signal Process. Mag., vol. 27, no. 3, pp. 20-34, May 2010.

P.H. Tan, L.K. Rasmussen and T.J. Lim, “The application of semidefinite programming for detection in CDMA,” IEEE J. Sel. Areas Commun.,vol. 19, no.8, pp. 1442-1449, Aug. 2001.

W.-K. Ma, T.N. Davidson, K.M. Wong, Z.-Q. Luo and P.C. Ching, “Quasi-maximum-likelihood multiuser detection using semi-definite relaxation with application to synchronous CDMA,” IEEE Trans. Signal Process., vol. 50, no. 4, pp. 912-922, Apr. 2002.

W.-K. Ma, P.C. Ching, and Z. Ding, “Semidefinite relaxation based multiuser detection for M-ary PSK multiuser systems,” IEEE Trans. Signal Process., vol. 52, no. 10, pp. 2862-2872, Oct. 2004.

W.-K. Ma, C.-C. Su, J. Jaldén, T.-H. Chang, and C.-Y. Chi, “The equivalence of semidefinite relaxation MIMO detectors for higher-order QAM,” IEEE J. Sel. Topics Signal Process., vol. 3, no. 6, pp. 1038-1052, Dec. 2009.

Performance analyses

M. Kisialiou and Z.-Q. Luo, “Probabilistic analysis of semidefinite relaxation for binary quadratic minimization,” SIAM Journal on Optimization, vol. 20, no. 4, pp. 1906-1922, 2010.

J. Jaldén and B. Ottersten, “The diversity order of the semidefinite relaxation detector,” IEEE Trans. Inf. Theory, vol. 54, no. 4, pp. 1406-1422, Apr. 2008.

Implementations

C. Helmberg, F. Rendl, R. J. Vanderbei, and H. Wolkowicz,“An interior-point method for semidefinite programming,” SIAM Journal on Optimization, vol. 6, no. 2, pp. 342-361, Oct. 1996.

S.J. Benson and Y.Ye, “DSDP5-software for semidefinite programming,” ACM Transactions on Mathematical Software, Vol. 34 , No. 3, May 2008.

M. Kisialiou, X. Luo, and Z.-Q. Luo, “Efficient implementation of quasi-maximum-likelihood detection based on semidefinite relaxation,” IEEE Trans. Signal Process., vol. 57, no. 12, pp. 4811-4822, Dec. 2009.

Z. Wen, D. Goldfarb, S. Ma, and K. Scheinberg, “Row by row methods for semidefinite programming,” Tech. Rep., Dept of IEOR, Columbia University, Apr. 2009

H.-T. Wai, W.-K. Ma, A.M.-C. So, “Cheap semidefinite relaxation MIMO Detection using row-by-row block coordinate descent,” in Proceedings of the 2011 IEEE Intl. Conf. Acoustic, Speech, and Signal Processing, May 2011.

Related links

Semidefinite Relaxation codes for the discrete Intebger least Squares prolem is a software package for SDR implementation of MIMO detectors.

CVX: MATLAB software for disciplined convex programming is a general solver for convex program (including SDP).

Lattice-based detectors

D. Wübben, D. Seethaler, J. Jaldén, and G. Matz, “Lattice reduction,” IEEE Signal Proces. Mag., vol. 28, no. 3, pp. 70-91, May. 2011.

A. D. Murugan, H. El Gamal, M. O. Damen, and G. Caire, “A unified framework for tree search decoding: rediscovering the sequential decoder,” IEEE Trans. Inf. Theory, vol. 52, no. 3, pp. 933-953, Mar. 2006.

J. Jaldén and P. Elia, “DMT optimality of LR-aided linear decoders for a general class of channels, lattice designs, and system models,” IEEE Trans. Inf. Theory, vol. 56, no. 10, pp. 4765-4780, Oct. 2010.

H. Yao and G. Wornell, “Lattice-reduction-aided detectors for MIMO communication systems”, in Proc. IEEE Global Conf. Commun., vol. 1, Taipei, Taiwan, Nov. 2002, pp. 424-428.

D. Wübben, R. Böhnke, V. Kühn, and K.-D. Kammeyer, “Near-maximum-likelihood detection of MIMO systems using MMSE-based lattice reduction,” in Proc. IEEE Int. Conf. Commun., vol. 2, Paris, France, Jun. 2004, pp. 798-802.

J. Pan, W.-K. Ma, and J. Jaldén, “MIMO detection by Lagrangian dual maximum-likelihood relaxation: Reinterpreting regularized lattice decoding,” IEEE Trans. Signal Process., vol. 62, no. 2, pp. 511-524, Jan. 2014. [PDF]

Deep Unfolding-based detectors

N. Samuel, T. Diskin, and A. Wiesel, “Deep MIMO detection,” in Proc. 2017 IEEE Int. Workshop Signal Process. Advances Wireless Commun. (SPAWC), 2017.

N. Samuel, T. Diskin, and A. Wiesel, “Learning to detect,” IEEE Trans. Signal Process., vol. 67, no. 10, pp. 2554–2564, 2019.

Mingjie Shao and Wing-Kin Ma, “Binary MIMO Detection via Homotopy Optimization and Its Deep Adaptation,” IEEE Trans. Signal Process., vol. 69, pp. 781-796, 2021. [PDF]