Antenna selection and RF processing for MIMO systems IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Antenna selection and RF processing for MIMO systems July 2004 Andreas F. Molisch, Neelesh B. Mehta, Jianxuan Du, and Jin Zhang MERL (Mitsubishi Electric Research Labs), Cambridge, MA, USA (contact: molisch@merl.com) A. F. Molisch et al, MERL

Contents System model Performance analysis IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Contents System model Performance analysis Antenna selection algorithms Effect of nonidealities RF preprocessing Summary and conclusions Now that we know how to measure, and seen some examples, we want to investigate how to create channel models from those results. We will discuss the three basic modelling appraoches...... A. F. Molisch et al, MERL

Antenna selection Additional costs for MIMO September 2003July 2004 Antenna selection Additional costs for MIMO more antenna elements (cheap) more signal processing (Moore’s law) one RF chain for each antenna element Basic idea of antenna selection: have many antenna elements, but select only best for downconversion and processing only at one link end: cost reductions might be more important at one link end (MS) than the other Hybrid antenna selection: select best L out of available N antenna elements, use those for processing A. F. Molisch et al, MERL

September 2003July 2004 System model A. F. Molisch et al, MERL

Contents System model Performance analysis IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Contents System model Performance analysis Antenna selection algorithms Effect of nonidealities RF preprocessing Summary and conclusions Now that we know how to measure, and seen some examples, we want to investigate how to create channel models from those results. We will discuss the three basic modelling appraoches...... A. F. Molisch et al, MERL

Antenna and weight selection for diversity September 2003July 2004 Antenna and weight selection for diversity Weight selection if all antenna elements are used Write channel matrix as Excite channel with Vi, receive with UiH Received power is i2 Antenna and weight selection for H-S/MRT Create submatrices by striking rows Compute maximum singular value for this submatrix Search submatrix that gives largest max. singular val. Use singular values associated with selected submatrix as antenna weights A. F. Molisch et al, MERL

HS-MRT vs. MRT – diversity case September 2003July 2004 HS-MRT vs. MRT – diversity case A. F. Molisch et al, MERL

Antenna selection for space-time codes September 2003July 2004 Antenna selection for space-time codes knowledge at transmitter only about statistics of fading performance: full diversity order loss of coding gain in correlated systems: select antennas so that determinants of correlation matrices at TX and RX are maximized A. F. Molisch et al, MERL

Antenna selection for spatial multiplexing IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Antenna selection for spatial multiplexing Capacity for full-complexity system Antenna selection for H-S/MRT Create submatrices by striking rows Compute capacity according to Foschini equation Search the submatrix that gives largest capacity A. F. Molisch et al, MERL

Capacity – spatial multiplexing IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Capacity – spatial multiplexing 3 transmit antennas, 20dB SNR A. F. Molisch et al, MERL

Contents System model Performance analysis IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Contents System model Performance analysis Antenna selection algorithms Effect of nonidealities RF preprocessing Summary and conclusions Now that we know how to measure, and seen some examples, we want to investigate how to create channel models from those results. We will discuss the three basic modelling appraoches...... A. F. Molisch et al, MERL

Antenna selection algorithms September 2003July 2004 Antenna selection algorithms truly optimum selection: exhaustive search effort proportional to approximate methods: power-based selection: works well for diversity antennas, but not for spatial multiplexing selection by genetic algorithms maximize mutual information between antenna elements Gorokov’s method A. F. Molisch et al, MERL

Maximization of mutual information September 2003July 2004 Maximization of mutual information A. F. Molisch et al, MERL

Contents System model Performance analysis IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Contents System model Performance analysis Antenna selection algorithms Effect of nonidealities channel correlation channel estimation errors hardware RF preprocessing Summary and conclusions Now that we know how to measure, and seen some examples, we want to investigate how to create channel models from those results. We will discuss the three basic modelling appraoches...... A. F. Molisch et al, MERL

Channel correlation - diversity September 2003July 2004 Channel correlation - diversity A. F. Molisch et al, MERL

Channel estimation error - diversity September 2003July 2004 Channel estimation error - diversity A. F. Molisch et al, MERL

Hardware aspects attenuation of switches September 2003July 2004 Hardware aspects attenuation of switches either decrease the effective SNR, or LNA has to be before switchrequires more LNAs switching time: has to be much smaller than duration of training sequence accuracy of switch: transfer function has to be same from each input to each output port A. F. Molisch et al, MERL

Contents System model Performance analysis IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Contents System model Performance analysis Antenna selection algorithms Effect of nonidealities RF preprocessing Summary and conclusions Now that we know how to measure, and seen some examples, we want to investigate how to create channel models from those results. We will discuss the three basic modelling appraoches...... A. F. Molisch et al, MERL

RF Pre-Processing Followed by Selection IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 RF Pre-Processing Followed by Selection . Down Conv LNA A/D Sig Proc 1 L 2 Nr S W I T C H Baseband Demodulator Pre- (M) RF Content Slide Diversity gain AND Beamforming gain maintained Beam selection Vs Antenna Selection Can be implemented using variable phase-shifters A. F. Molisch et al, MERL

What is the Optimal RF Pre-Processing?? IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 What is the Optimal RF Pre-Processing?? 1 2 Instantaneous Channel Information Orient the beams with the angle of arrival of the incoming rays Require continuous updating of entries of pre-processor No Channel Information FFT based Pre-Processing Simple Beam pattern cannot adapt to the angle of arrival Content Slide A. F. Molisch et al, MERL

Channel Statistics-Based Pre-Processing IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Channel Statistics-Based Pre-Processing Pre-processor depends on channel statistics, Orients the beam with the mean angle of arrival Optimal Solution performs principal component decomposition on columns of H Advantages Continuous updating of entries of M not required Content Slide A. F. Molisch et al, MERL

FFT-Based Pre-Processing IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 FFT-Based Pre-Processing . . . . . . . . Good gains if AoA is 0,60, 90, 300, 270 Static beam pattern: Cannot adapt to the mean AoA Content Slide Beam Pattern as function of Azimuth angle A. F. Molisch et al, MERL

Channel Statistics-Based Pre-Processing IEEE 802.11-03/0682-00-000k September 2003 September 2003July 2004 Channel Statistics-Based Pre-Processing Content Slide Beam Pattern as function of Azimuth angle for mean AoA of 60 degrees Beam Pattern as function of Azimuth angle for mean AoA of 45 degrees A. F. Molisch et al, MERL

Summary and conclusions September 2003July 2004 Summary and conclusions antenna selection retains the diversity degree, but SNR penalty for spatial multiplexing, comparable capacity if LrNt optimum selection algorithms have complexity N!/(N-L)!; however, fast, good selection algorithms exist for low-rank channels, transmit antenna selection can increase capacity channel estimation errors do not decrease capacity significantly frequency selectivity reduces effectiveness of antenna selection RF preprocessing greatly improves performance, especially in correlated channels Covariance-based preprocessing especially suitable for frequency-selective channels switches with low attenuation required both for TX and RX antenna selection is attractive for reducing hardware complexity in MIMO systems A. F. Molisch et al, MERL

September 2003July 2004 References A. F. Molisch et al, MERL