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The Promise of MIMO Mobile Networks: Project Overview

The Promise of MIMO Mobile Networks: Project Overview. channel: H. Tx. Rx. SVD. V H. U. Tx. Rx. MIMO. Why MIMO? Potential for significantly increased channel capacity With rich scattering, parallel spatial channels increase the effective bandwidth and hence achievable bit rate.

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The Promise of MIMO Mobile Networks: Project Overview

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  1. The Promise of MIMO Mobile Networks:Project Overview

  2. channel: H Tx Rx SVD VH U Tx Rx MIMO • Why MIMO? • Potential for significantly increased channel capacity • With rich scattering, parallel spatial channels increase the effective bandwidth and hence achievable bit rate. • Mathematically, this is visible through the SVD (singular value decomposition) of the spatial channel matrix H where r=Ht.

  3. Mode Modulation Coding Rate Bit Rate (Mbit/s) Bandwidth (MHz) Spectral Efficiency (bit/s/Hz) 1 BPSK 1/2 6 16.6 0.36 2 BPSK 3/4 9 0.54 3 QPSK 1/2 12 0.72 4 QPSK 3/4 18 1.08 5 16QAM 9/16 27 1.62 6 16QAM 3/4 36 2.17 7 64QAM 3/4 54 3.25 Spectral Efficiency IEEE 802.11 (a) or Hiperlan/2

  4. Characteristic Target Value (FDD, 2 x 1.25MHz) Mobility Up to 250 km/hr Sustained spectral efficiency (DL) > 1 bit/s/Hz/cell Peak User Data Rate (DL) > 1 Mbps Peak User Data Rate (UL) > 300 kbps Peak aggregate data rate per cell (DL) > 4 Mbps Peak aggregate data rate per cell (UL) > 800 kbps Cell sizes Appropriate for MAN networks Spectrum Licensed bands < 3.5 GHz Mobile Broadband Wireless Access Standards (802.20)

  5. Spectral Efficiency *Source: Qualcomm white paper, “The economics of mobile wireless data”

  6. OFDM Spectrum OFDM Spectrum Subcarrier Spectra Frequency Synchronization: No Intercarrier Interference Frequency Offset: Intercarrier Interference

  7. VH Tx Rx U MIMO Implementations • Previous research on transmitter subspace tracking approaches, open and closed loop feedback techniques, space time coding • One example: stochastic gradient approach using feedback from the receiver to give the transmitter channel state information • (Banister & Zeidler, IEEE JSAC Special Issue on MIMO, April 2003, IEEE Trans. Signal Processing, March 2003, IEEE Trans. on Wireless Comm, in press) • Approximates water-filling: extracts best channels but uses equal power/rate allocation to maximize power delivered to receiver • Coding can be applied to each antenna element • Space-Time coding

  8. Increased Capacity/Reduced Dectectability Using Multiple Antennas Mean Capacity (bits/sec/Hz) MIMO Optimal Water Filling MIMO Optimal Subspace Tracking MIMO Gradient Adp. Subsp. Trkg (V. 1) MIMO Gradient Adp. Subsp. Trkg (V. 2) MIMO Blind Transmission Single Input/Multiple Output (SIMO) Single Input/Single Ouptput(SISO) SISO AWGN Shannon limit for SISO Normalized (dB) Capacity vs. Energy Per Bit, 8 Transmit and 2 Receive Antennas

  9. Current Research Issues • Determining Channel State Information in Mobile Networks • Open/Closed Loop Implementations • Performance in Multi-User Networks • Performance with Multi-Cellular Networks • Performance in Ad-Hoc Networks

  10. MURI BAA for “Space Time Processing for Tactical Mobile Ad-Hoc Networks” • Objective: • “ Develop cross-layer, energy-efficient MIMO signal processing • algorithms for mobile, multi-user ad-hoc networks employing • directional antenna arrays and STC for tactical applications ” • Physical Layer: • Medium Access Control (MAC) Layer: • Networking Layer: • Signaling issues • BF vs. STC tradeoff • CSI estimation in interference-limited environment • MIMO CSI in MAC protocols • Transmission-rate adaptability, beamforming, location info • Transmission scheduling in context of STC • Energy efficiency • MAC scheduling, generated traffic, STC/BF to reduce signaling overhead, improve robustness and probability of intercept

  11. University of California, San Diego James Zeidler (PI), Larry Milstein, Rene Cruz, John Proakis, Bhaskar Rao, Michele Zorzi University of Califnia, Irvine Hamid Jafarkhani University of California, Santa Cruz JJ Garcia-Luna University of California, Riverside Srikanth Krisnamurthy, Yingbo Hua Brigham Young University Lee Swindlehurst, Mike Jensen McMaster University Simon Haykin MURI Project Team

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