The Promise of MIMO Mobile Networks: Project Overview

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The Promise of MIMO Mobile NetworksProject
Overview
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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 rHt.

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Spectral Efficiency

IEEE 802.11 (a) or Hiperlan/2
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Mobile Broadband Wireless Access Standards
(802.20)

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Spectral Efficiency
Technology Bandwidth Waveform throughput per cell spectral efficiency (b/s/Hz/cell)
WCDMA 3.84 MHz 900 kbps 0.23
cdma2000 1xEV 1.25 MHz DS SS 530 kbps 0.42
Mobile Broadband Wireless Access (802.20) 1.25 MHz Frequency Hopped OFDM gt 1.25 Mbps gt 1
Joint Tactical Radio System Narrowband WF 25 KHz 8-ary CPM 50 kbps 2
Wideband WF 150 KHz - 10 MHz OFDM 13.74 Mbps 2.7

Source Qualcomm white paper, The economics
of mobile wireless data
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OFDM Spectrum
OFDM Spectrum
Subcarrier Spectra
Frequency Synchronization No Intercarrier
Interference
Frequency Offset Intercarrier Interference
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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

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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
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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

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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

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MURI Project Team

• 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