On the Distribution of SINR for MMSE MIMO Systems

While the performance analysis of linear zero-forcing (ZF) and minimum mean-square error (MMSE) receivers has received much attention, there has been no exact closed-form distribution of signal-to-interference-plus-noise ratio (SINR) for MMSE multiple-input multiple-output (MIMO) systems in the arbitrary fading environments for over the past 20 years. In this paper, an exact and general distribution of SINR for MMSE MIMO systems is presented under uncorrelated Rayleigh fading environments. We start our analysis by finding regular patterns from the probability density function (PDF) of SINR for small dimensions, and using the regular patterns, we extend the PDF to arbitrary dimensions. From the exact and general PDF of SINR for MMSE MIMO systems, the cumulative distribution function and the moment-generating function are derived in terms of the incomplete gamma function and the confluent hypergeometric function of the second kind, respectively. As applications, the error probability, outage probability, and achievable sum rate are investigated by using the distribution of SINR for MMSE MIMO systems. Most notably, the achievable sum rates for ZF and MMSE MIMO systems are defined by using the Meijer $G$ -function, which are the first closed-form expressions in the literature.

[1]  J. Speidel,et al.  Unifying performance analysis of linear MIMO receivers in correlated Rayleigh fading environments , 2004, Eighth IEEE International Symposium on Spread Spectrum Techniques and Applications - Programme and Book of Abstracts (IEEE Cat. No.04TH8738).

[2]  M. V. Clark,et al.  Theoretical reliability of MMSE linear diversity combining in Rayleigh-fading additive interference channels , 1998, IEEE Trans. Commun..

[3]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[4]  Hyundong Shin,et al.  Schur Complement Based Analysis of MIMO Zero-Forcing for Rician Fading , 2014, IEEE Transactions on Wireless Communications.

[5]  Giuseppa Alfano,et al.  SNR gap between MIMO linear receivers: Characterization and applications , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[6]  Yi Jiang,et al.  Performance Analysis of ZF and MMSE Equalizers for MIMO Systems: An In-Depth Study of the High SNR Regime , 2011, IEEE Transactions on Information Theory.

[7]  Jun Won Choi,et al.  Detection of Large-Scale Wireless Systems via Sparse Error Recovery , 2017, IEEE Transactions on Signal Processing.

[8]  Jun Won Choi,et al.  New approach for massive MIMO detection using sparse error recovery , 2014, 2014 IEEE Global Communications Conference.

[9]  Giuseppa Alfano,et al.  Rayleigh Quotient Based Analysis of MIMO Linear Receivers , 2017, WSA.

[10]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[11]  Robert W. Heath,et al.  Transmit selection in spatial multiplexing systems , 2002, IEEE Communications Letters.

[12]  Reinaldo A. Valenzuela,et al.  Simplified processing for high spectral efficiency wireless communication employing multi-element arrays , 1999, IEEE J. Sel. Areas Commun..

[13]  J. Craig A new, simple and exact result for calculating the probability of error for two-dimensional signal constellations , 1991, MILCOM 91 - Conference record.

[14]  Mohamed-Slim Alouini,et al.  BER computation of 4/M-QAM hierarchical constellations , 2001, 12th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. PIMRC 2001. Proceedings (Cat. No.01TH8598).

[15]  Hyundong Shin,et al.  Exact MIMO Zero-Forcing Detection Analysis for Transmit-Correlated Rician Fading , 2013, IEEE Transactions on Wireless Communications.

[16]  John M. Cioffi,et al.  Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization , 2003, IEEE Trans. Signal Process..

[17]  Dongweon Yoon,et al.  New BER Expression of MPSK , 2011, IEEE Transactions on Vehicular Technology.

[18]  H. Vincent Poor,et al.  Probability of error in MMSE multiuser detection , 1997, IEEE Trans. Inf. Theory.

[19]  Francis C. M. Lau,et al.  Performance analysis for MIMO systems using zero forcing detector over fading channels , 2006 .

[20]  Hyuncheol Park,et al.  Performance Analysis of MIMO System with Linear MMSE Receiver , 2008, IEEE Transactions on Wireless Communications.

[21]  Dongweon Yoon,et al.  Generalized BER Expression of MPSK in the Presence of Phase Error , 2013, IEEE Communications Letters.

[22]  Aria Nosratinia,et al.  Diversity Order of MMSE Single-Carrier Frequency Domain Linear Equalization , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[23]  John M. Cioffi,et al.  On the distribution of SINR for the MMSE MIMO receiver and performance analysis , 2006, IEEE Transactions on Information Theory.

[24]  David Tse,et al.  Linear Multiuser Receivers: Effective Interference, Effective Bandwidth and User Capacity , 1999, IEEE Trans. Inf. Theory.

[25]  Aria Nosratinia,et al.  Diversity of MMSE MIMO receivers , 2010, ISIT.

[26]  Matthew R. McKay,et al.  Capacity and performance of MIMO-BICM with zero-forcing receivers , 2005, IEEE Transactions on Communications.

[27]  Erik G. Larsson,et al.  Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems , 2011, IEEE Transactions on Communications.

[28]  Stephan ten Brink,et al.  Achieving near-capacity on a multiple-antenna channel , 2003, IEEE Trans. Commun..

[29]  Matthew R. McKay,et al.  Achievable Sum Rate of MIMO MMSE Receivers: A General Analytic Framework , 2010, IEEE Transactions on Information Theory.

[30]  Giuseppe Caire,et al.  Asymptotic Performance of Linear Receivers in MIMO Fading Channels , 2008, IEEE Transactions on Information Theory.

[31]  Giuseppe Caire,et al.  On maximum-likelihood detection and the search for the closest lattice point , 2003, IEEE Trans. Inf. Theory.

[32]  M. S. Milgram,et al.  The generalized integro-exponential function , 1985 .

[33]  Aria Nosratinia,et al.  Outage and Diversity of Linear Receivers in Flat-Fading MIMO Channels , 2007, IEEE Transactions on Signal Processing.

[34]  Hyuckjae Lee,et al.  SINR Distribution for MIMO MMSE Receivers in Transmit-Correlated Rayleigh Channels: SER Performance and High-SNR Power Allocation , 2013, IEEE Transactions on Vehicular Technology.

[35]  Antonia Maria Tulino,et al.  Random Matrix Theory and Wireless Communications , 2004, Found. Trends Commun. Inf. Theory.

[36]  Dongweon Yoon,et al.  On the general BER expression of one- and two-dimensional amplitude modulations , 2002, IEEE Trans. Commun..

[37]  Jiaheng Wang,et al.  Sparse-Aware Minimum Mean Square Error Detector for MIMO Systems , 2017, IEEE Communications Letters.

[38]  Richard D. Gitlin,et al.  The impact of antenna diversity on the capacity of wireless communication systems , 1994, IEEE Trans. Commun..

[39]  Mohamed-Slim Alouini,et al.  Digital Communication over Fading Channels: Simon/Digital Communications 2e , 2004 .

[40]  David Tse,et al.  Linear multiuser receivers in random environments , 2000, IEEE Trans. Inf. Theory.

[41]  Shlomo Shamai,et al.  Spectral Efficiency of CDMA with Random Spreading , 1999, IEEE Trans. Inf. Theory.

[42]  H. Vincent Poor,et al.  Iterative (turbo) soft interference cancellation and decoding for coded CDMA , 1999, IEEE Trans. Commun..

[43]  Michail Matthaiou,et al.  Novel Generic Bounds on the Sum Rate of MIMO ZF Receivers , 2011, IEEE Transactions on Signal Processing.

[44]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[45]  Matthew R. McKay,et al.  Multiplexing/Beamforming Switching for Coded MIMO in Spatially Correlated Channels Based on Closed-Form BER Approximations , 2007, IEEE Transactions on Vehicular Technology.

[46]  Matthew R. McKay,et al.  Error Performance of MIMO-BICM with Zero-Forcing Receivers in Spatially-Correlated Rayleigh Channels , 2007, IEEE Transactions on Wireless Communications.

[47]  Erik G. Larsson,et al.  Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays , 2012, IEEE Signal Process. Mag..

[48]  Mohamed-Slim Alouini,et al.  Digital Communication Over Fading Channels: A Unified Approach to Performance Analysis , 2000 .

[49]  M. O. Damen,et al.  A unified framework for tree search decoding: rediscovering the sequential decoder , 2005, SPAWC 2005.