Channel Estimation for Wired MIMO Communication Systems 05/05/2005 Channel Estimation for Wired MIMO Communication Systems Final Presentation Daifeng Wang Dept. of Electrical and Computer Engineering The University of Texas at Austin wang@ece.utexas.edu

Introduction Review Today Wired MIMO Communication Systems Multicarrier Modulation Training-Based Channel Estimation Today Which channel estimation strategy for wired communication systems? How to design the training sequence? What is the channel model? How to estimate the wired MIMO channel? 11/30/2018

Training-Based Channel Estimation Strategy Block-Type All subcarriers + Period Least Square (LS), Minimum Mean-Square (MMSE) Slow Fading/Varying Channels Decision Feedback Equalizer Comb-Type Selective subcarriers + Interpolation LS, MMSE, Least Mean-Square (LMS) Fast Fading/Varying Channels Interpolation Linear Second order Low-pass Spline Cubic Time domain Tradeoff between performance and complexity 11/30/2018

[Fragouli, Dhahir & Turin, 2003] Training Sequences y = S h + n h: L-tap channel S: transmitted N x L Toeplitz matrix made up of N training symbols n: AWGN Domain Method Minimum MSE Complexity Optimal Sequence* Time Periodic [Chen & Mitra, 2000] Yes High(2N) Aperiodic [Tella, Guo & Barton, 1998] No Medium(N2) L-Perfect (MIMO) [Fragouli, Dhahir & Turin, 2003] Almost Low(Nlog2N) Sometimes Frequency [Tella, Guo & Barton, 1999] * impulse-like autocorrelation and zero crosscorrelation 11/30/2018

Training-Based MIMO Channel Model 2 X 2 MIMO Model TX 1 RX 1 h11 h21 h12 TX 2 RX 2 h22 11/30/2018

Training-Based Channel Estimation for MIMO Least Square (LS) Assumes S has full column rank Mean-Square (MSE) zero-mean and white Gaussian noise: Sequences satisfy above are optimal sequences Optimal sequences: impulse-like autocorrelation and zero crosscorrelation 11/30/2018