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Real World MIMO Systems: Design and Performance

Real World MIMO Systems: Design and Performance. P M Grant & J S Thompson Joint Research Institute in Signal and Image Processing Edinburgh/Heriot Watt Universities. Overview of Talk. Theoretical introduction to Multiple input-multiple output (MIMO) systems

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Real World MIMO Systems: Design and Performance

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  1. Real World MIMO Systems: Design and Performance P M Grant & J S Thompson Joint Research Institute in Signal and Image Processing Edinburgh/Heriot Watt Universities

  2. Overview of Talk • Theoretical introduction to Multiple input-multiple output (MIMO) systems • Real world MIMO channel measurements • Advanced receiver design • Conclusions & acknowledgments

  3. Comms Channel Capacity Reminder • At low SNRs, capacity proportional to SNR • At higher SNRs, increasing capacity becomes harder • 4-5 bits/s/Hz is the maximum practical limit for simple signals • More complex signals (QPSK, QAM) needed but SNR penalty Maximum Spectral Efficiency (bit/s) Typical Spectral Efficiency Capacity vs SNR (W=1 Hz)

  4. Increasing Capacity with MIMO • Consider a MIMO system with N TX antennas and N RX antennas – The so-called N x N system • Transmit power at TX is fixed to P, regardless of N • The Shannon capacity can be upper-bounded as: • Capacity C increases linearly with N despite TX power constraint to deliver mobile broadband user capability! Scaling due to multiple RX antennas Scaling due to Tx Power Constraint

  5. Where Does the Capacity Come From? Exploit multipath to permit different antennas to be separated at the RX antennas Separate out transmitted packets here using one beam pattern per TX antenna Transmit multiple data packets in parallel here Spatial processing allows each radio frequency to be re-used several times!

  6. Best Case Channel Capacity Predictions W=1 Hz (bits/s) • Potentially enormous increases in capacity possible but dependent on propagation channel conditions

  7. 10bits/s/Hz 5bits/s/Hz 2.5bits/s/Hz Multiple-Input Multiple-Output (MIMO) This novel wireless communication architecture is often referred to as ‘Multiple-Input Multiple-Output’ or MIMO, since use is made of multipath signal propagation in order to convey information in parallel spatial channels in order to increase spectrum efficiency over signal antenna solutions

  8. MIMO Systems In Practice • Now see MIMO techniques being introduced into wireless standards: • Alamouti space-time codes used for transmit diversity in 3GPP standards • MIMO methods being considered by the 802.11n Wireless LAN study group • Spatial multiplexing techniques also standardised in 802.16 Wimax systems MIMO Wireless LAN Router

  9. Benefits of MIMO Systems • Increase capacity of a wireless system significantly • Improvement depends on number of antennas • But there are many practical issues: • Can we build and operate MIMO systems on real radio channels? • How do we perform accurate channel estimation and tracking in high Doppler? • How much correlation exists between separate received signal paths/antenna signals? • What is the performance trade-off in number of deployed T/R antennas? • Can we design/operate the hardware at sufficient speed?

  10. Overview • Theoretical introduction to multiple input-multiple output (MIMO) systems • Real world MIMO channel capacity measurements • Mobile VCE Results from Bristol/Edinburgh Univ • Channel Measurements at TU Ilmenau • Advanced receiver design • Conclusions

  11. Basestation / Access point Angle of Departure Angle of Arrival Mobile Terminal Understanding this new Wireless Medium • The design of equipment which can fully exploit MIMO channels necessitates the need to characterise the new ‘Double Directional Channels’ • Research by Bristol & Edinburgh to model accurately this time varying channel.

  12. Typical Indoor channel measurement • Single snapshot of time and angle of arrival of observed multipath components. • Clusters are identified with a KDE clustering algorithm, typically find 20-30 unique components.

  13. 2 GHz MIMO Measurements & Analysis University of Bristol • Objectives • Capture the dynamics of the wideband 4x4 MIMO channel in an urban macrocell • Compare realistic laptop and PDA terminal antenna configurations, including user effects • Compare channel statistics at 2 and 3.5 GHz using ray-tracing tools with 20 MHz sounder • Assess feedback requirements for closed loop MIMO schemes using measured channel dynamics

  14. Measurement Area in Bristol • 58 locations (standing and walking) • 10 Drive tests routes – 140 GB of channel data

  15. Measurements in Progress

  16. Measurements in Progress

  17. Example Results: Channel Capacity Outage capacity with Laptop and Reference Dipoles Outage capacity with PDA and Reference Dipoles • Observations: • Capacity results double as array size doubles (12, 2 4) • PDA results show more variability due to smaller size of the device

  18. Rx Tx Example of Broadband MIMO Measurement Data Ilmenau MIMO channel sounder www.channelsounder.de • Up to 240 MHz bandwidth, 5.2 GHz • Real-time MIMO measurements Ilmenau city center, urban hot spot : Receive antenna • 8 elements uniform linear patch array (ULA) with separate ports for horiz, and vert. Polarization, only vert. polarization measured • element separation: 0.4943 λ Transmit antenna • omnidirectional 16 element uniform circular array (UCA), vertical polarization • element separation: 0.38 λ

  19. Rx Tx Propagation Characterisation MIMO channel sounder • 120 MHz bandwidth, 5.2 GHz • Real-time MIMO measurements • www.channelsounder.de : Receive antenna • 8 elements uniform linear patch array (ULA) with separate ports for horiz, and vert. Polarization, only vert. polarization measured • element separation: 0.4943 λ Transmit antenna • omnidirectional 16 element uniform circular array (UCA), vertical polarization • element separation: 0.38 λ

  20. Rx Tx Propagation Characterisation & Capacity :

  21. Overview • Theoretical introduction to multiple input-multiple output (MIMO) systems • Real world MIMO channel measurements • Advanced Receiver Algorithm Design • Conclusions

  22. Outdoor Setup (TU Vienna) Transmitter • Roof • Patch Antennas • 20dBm/antenna • 2.5GHz

  23. Indoor Setup (TU Vienna) Receiver • 4 Monopoles • 3000 channel measurements assessed over a 4x4 lambda area

  24. TU Vienna Prototype Example: WLAN • OFDM based wireless systems like WLAN and WiMAX are currently a hot topic for standardization, working at 2.4, 3.5, 5.2, 5.8 and 11.0GHz. • Flexible hardware prototypes are required not only for testing the anticipated behavior in physical wireless channels but also to gain experience in building such devices. • TU-Vienna has developed OFDM rapid prototyping environment. • Scalable 802.11n WLAN MIMO system • Scalable 802.16 WiMAX MIMO system

  25. TU Vienna Prototype Example: WLAN Performance Curves for WLAN MIMO Testbed System operating in a I-Metra channel

  26. Algorithm Design (Univ of Edinburgh) • Optimum MIMO detector is Maximum Likelihood • Examine all possible received signals to find best fit • Complexity increases exponentially with array size • Simpler solution uses Sphere Decoder • Places sphere around received signal point • Achieves optimum performance with much lower complexity than the maximum likelihood decoder • Investigating fixed Sphere Decoder design Maximum Likelihood vs Sphere Decoding

  27. ETHz no 1

  28. ETHZ no 5

  29. Fixed Sphere Decoder Performance • A key feature of the fixed sphere decoder is the ordering of antennas used in detection • This allows close to maximum likelihood performance but with fixed complexity Comparison of FSD with Sphere Decoder/Maximum Likelihood

  30. Hardware Implementation • Used Xilinx DSP System Generator to map design easily and rapidly to FPGA from MATLAB • Alpha Data test board allows efficient hardware-in-the-loop algorithm simulation within MATLAB

  31. FPGA Performance Evaluation • Our sphere decoder (SD) did not implement efficiently on FPGA due to sequential tree search • Fixed sphere decoder (FSD) performs much better • Much shorter design time for FPGA than ASIC * * - Number in brackets shows throughput for optimized FPGA design

  32. Conclusions • MIMO systems offer significant performance advantages in data throughput • Modelling and measurement of channels has shown realisable real world expectations • System designers have developed MIMO smart antenna rapid prototyping testbed technology • BER results confirm initial theoretical predictions & offer significant MIMO performance enhancements

  33. Acknowledgments • Mark Beach at Bristol for channel response and capacity measurements • Ralf Schneider at Ilmenau for access to MIMO data throughput trials • Helmut Boicskei at ETHz for Sphere decoding

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