References & Further Reading

References

  1. G. J. Foschini, G. D. Golden, R. A. Valenzuela, and P. W. Wolniansky, Simplified Processing for High Spectral Efficiency Wireless Communication Employing Multi-element Arrays, 1999

    The V-BLAST architecture paper.

  2. E. Telatar, Capacity of Multi-antenna Gaussian Channels, 1999

    Foundational MIMO capacity paper.

  3. D. Tse and P. Viswanath, Fundamentals of Wireless Communication, Cambridge University Press, 2005

    Chapters 8 and 9 cover MIMO detection and V-BLAST from a teaching-first perspective.

  4. E. Viterbo and J. Boutros, A Universal Lattice Code Decoder for Fading Channels, 1999

    Introduced sphere decoding to wireless communications.

  5. B. Hassibi and H. Vikalo, On the Sphere-Decoding Algorithm I: Expected Complexity, 2005

    Expected polynomial complexity analysis at moderate SNR.

  6. J. Jaldén and B. Ottersten, On the Complexity of Sphere Decoding in Digital Communications, 2005

    Proves that expected complexity is exponential in $n_t$ at any fixed SNR.

  7. A. K. Lenstra, H. W. Lenstra Jr., and L. Lovász, Factoring Polynomials with Rational Coefficients, 1982

    The LLL algorithm.

  8. C. Windpassinger and R. F. H. Fischer, Low-Complexity Near-Maximum-Likelihood Detection and Precoding for MIMO Systems using Lattice Reduction, 2003

    First application of LLL to MIMO detection.

  9. Y. H. Gan, C. Ling, and W. H. Mow, Complex Lattice Reduction Algorithm for Low-Complexity Full-Diversity MIMO Detection, 2009

    Complex-valued LLL for MIMO.

  10. J. Jaldén and P. Elia, DMT Optimality of LR-Aided Linear Decoders for a General Class of Channels, Lattice Designs, and System Models, 2010

    Proves full diversity for LLL-aided linear detection.

  11. G. Taricco and E. Biglieri, Space-Time Decoding with Imperfect Channel Estimation, 2005

    Diversity analysis under channel estimation uncertainty.

  12. D. Micciancio, The Hardness of the Closest Vector Problem with Preprocessing, 2001

    NP-hardness of CVP.

  13. J. Zhan, B. Nazer, U. Erez, and M. Gastpar, Integer-Forcing Linear Receivers, 2014

    Integer-forcing receivers — CommIT-adjacent contribution.

  14. U. Fincke and M. Pohst, Improved Methods for Calculating Vectors of Short Length in a Lattice, Including a Complexity Analysis, 1985

    Origin of lattice sphere enumeration.

  15. C. P. Schnorr and M. Euchner, Lattice Basis Reduction: Improved Practical Algorithms and Solving Subset Sum Problems, 1994

    The Schnorr-Euchner enumeration order.

Further Reading

For readers who want to go deeper into specific topics from this chapter.

  • Soft-output MIMO detection for iterative decoding

    Hochwald & ten Brink, 'Achieving Near-Capacity on a Multiple-Antenna Channel,' IEEE Trans. Commun., 2003

    Bridges from hard-decision MIMO detection to soft information for LDPC/Turbo decoders.

  • Approximate message passing (AMP) for MIMO

    Jeon, Hong, Ma, 'A 5.8 pJ/bit LDPC-Coded 4x4 MIMO Receiver,' IEEE JSSC, 2018

    Shows how AMP-based detectors compete with SIC and sphere decoders in modern chips.

  • Lattice codes for the Gaussian channel

    Zamir, Lattice Coding for Signals and Networks, Cambridge, 2014

    The mathematical counterpart: designing the transmit lattice, not just decoding it.

  • Deep-learning MIMO detection

    Samuel, Diskin, Wiesel, 'Deep MIMO Detection,' IEEE SPAWC, 2017

    Learned detectors that unroll projected gradient or AMP iterations.

ExercisesCh 13 →