® ® M. Viberg, Chalmers University of Technology, T. Boman, FOI, U. Carlberg, SP, L. Pettersson, FOI, S. Ali, Barcelona University (Chalmers M.Sc.), E. Arabi, University of medical science and technology, Khartoum Sudan (Chalmers M.Sc.), M. Bilal, Barcelona University (Chalmers M.Sc.), O. Moussa, KTH (Chalmers M.Sc.) Sponsored by SSF within the Strategic Center Charmant © 2008 The MathWorks, Inc. Simulation of MIMO Antenna Systems in Simulink and Embedded Matlab ® ® MIMO systems have multiple antennas at transmit and receive Basestation Wireless LAN 2 ® ® Why do we need MIMO systems? The capacity for a SISO channel was given by Shannon as C = log 2 [1 + SNR ] [bit/(Hz·s)] Future wireless (data) applications will require increasing data rates and therefore higher capacity Spectrum (bandwidth) is very expensive Increasing power (SNR) does not help much Using multiple antennas more promising way to increase capacity – ultimately linear in number of antennas! 3 ® ® What can be achieved by multi-antenna systems? Array gain: Coherent combining will increase SNR (Signal-to-Noise Ratio) Interference rejection: Spatial filtering will improve SINR (Signal-to-Interference-plus-Noise Ratio) Diversity: Receiving more than one copy mitigates fading Spatial multiplexing: Increase in data rate 4 ® ® MIMO wireless communication system Transmitter with n antennas Channel, mxn Receiver with m antennas RF part, focus of this presentation 5 ® ® Simulation of a MIMO system: flowchart Main difficulty: how to represent and compute the overall ”transfer function” (MIMO and possibly non-linear) 6 ® ® Overview of Simulink simulation model Antennas, etc. Power amp Channel 7 ® ® MIMO communication channel model Multipath model, signal received via K paths: Time-delay of path k Received signal at antenna i Response from i to j via path k Doppler shift Transmitted signal from antenna j • Transmission paths generated according to ”Random Cluster Model” • Can also use ”deterministic” parameters according to known scenario, or a measured channel 8 ® ® Simulink implementation MIMO channel implemented as an FIR filter in Embedded Matlab, and included in the Simulink model: 9 ® ® Simulation of front-end electronics Antennas, etc. Power amp Channel 10 ® ® Amplifier non-linearities: ”Memory polynomial” model Easy to fit parameters to measured data! 11 ® ® Simulation of antennas and network Antennas, etc. Power amp Channel 12 ® ® Microwave components characterized using scattering parameters − in V + Vout Antenna or other component Vin+ − Vout S11 : Reflection coefficient S 21 : Transfer function S: Scattering matrix In general, these are frequency-dependent 13 ® ® Multiple antennas: mutual coupling • Single antennas conveniently handled in Simulink’s RF Blockset • Multiple antennas cannot be represented due to the mutual coupling; need multiport representation – ”matrix of scattering matrices” 14 ® ® Modeling MIMO Network between PA and Antennas 0 0 S  S TimeDomain PA SL1 SR1 (2x2) (2x2) SM (4x4) PA Power Amplifiers mutual  S 11 11 0 TimeS 22  1S mutual0 Domain  1  0 0  0 Frequency1 SDomain   22  0 1 0 0 0  0  0 SR (4x4) SL2 SR2 (2x2) (2x2) Matching Network   0 Antennas 15 ® ® Implementation in Simulink The overall transfer function from PAs to antennas (MIMO) is computed in Embedded Matlab, then implemented in Simulink: 16 ® ® Simulation example using 2 transmit and 3 receive antennas Training signal used to estimate MIMO equalizer: 17 ® ® Transmitted, received and restored signals (I and Q channels) Tx Rx Tx 18 ® ® Bit errors rate increasing due to timevarying channel Tx Rx Errors 19 ® ® Conclusions MIMO antenna system implemented in Simulink with Embedded Matlab Can be used to test the impact of various components (antennas, PA non-linearities) on bit-error-rate etc. Future work: Include signal processing according to various standards (WCDMA, WiMax, LTE, etc.) Optimization of components (and algorithms) with ”system-level” design criteria Increase flexibility and user-friendliness Make the code publicly available Other applications (radar and other microwave sensing) 20