James Zeidler, PI Haichang Sui, Jittra Jootar and Adam Anderson, GSRs

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James Zeidler, PIHaichang Sui, Jittra Jootar
and Adam Anderson, GSRs
Quantifying Performance Improvements Due to
Spatial-Temporal Diversity in MIMO
Spread-Spectrum Mobile Ad-hoc Networks
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Summary of the Main Results

• Coherent systems
• We studied the effect of Doppler and
  multipath/spatial diversity in DS-CDMA systems
  with time-varying channels and noisy CSI
• Trade-off among various system parameters are
  analyzed
• Critical Doppler spread and pilot power are
  obtained to characterize when noncoherent
  signaling is preferable.
• Noncoherent systems
• The performance of Differential Unitary
  Space-Time Modulation (DUSTM) in time-varying
  channels with advanced detection is analyzed.
• DUSTM with offset modulation is studied.
• A coded Frequency Hopping Spread Spectrum system
  based on DUSTM is proposed. Erasure insertion is
  studied to alleviate PBI/MAI in the proposed
  system.
• Experimental results based on data from BYU

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Publications

• J. Jootar, J. R. Zeidler, and J. G. Proakis,
  "Performance of Convolutional Codes with
  Finite-Depth Interleaving and Noisy Channel
  Estimates," submitted to IEEE Transactions on
  Communications, April 2005
• J. Jootar, J. R. Zeidler, and J. G. Proakis,
  Performance of Alamouti Space-Time Code in Time
  Varying Channels with Noisy Channel Estimates,''
  in Proceedings of the IEEE WCNC (New Orleans),
  pp 498-503, Mar. 2005
• J. Jootar, J. R. Zeidler, and J. G. Proakis,
  Performance of Finite-Depth Interleaved
  Convolutional Codes in a Rayleigh Fading Channel
  with Noisy Channel Estimates,'' in Proceedings of
  the IEEE 61st Vehicular Technology Conference
  (Stockholm), June 2005
• A. Anderson, J. R. Zeidler, and M. A. Jensen,
  "Differential Space-Time Coding with Offset
  Quadrature Phase-Shift Keying",  Proceedings of
  the IEEE  Workshop on Signal Processing Advances
  in Wireless Communications (New York, N. Y.),
  June 2005
• H. Sui and J.R. Zeidler, "Erasure Insertion for
  Coded MIMO Slow Frequency-Hopping Systems in the
  Presence of Partial Band Interference", accepted
  by IEEE Globecom, December 2005
• H. Sui and J. R. Zeidler, "An explicit and
  Unified Error Probability Analysis of Two
  Detection Schemes for Differential Unitary
  Space-Time Modulation", submitted to the IEEE
  Asilomar Conference, November 2005
• H. Sui and J. R. Zeidler, Erasure Insertion for
  Coded MIMO Slow Frequency-Hopping Multiple-Access
  Networks, in preparation

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Coherent Systems

• Assumptions
• We focus on DS-CDMA (channel estimation is harder
  in FH-CDMA due to hopping).
• Time-varying channel.
• Pilot signal is used to estimate the channel.
• Noisy CSI estimates.
• Scenarios
• 1) Convolutional codes with finite-depth
  interleaving.
• 2) Alamouti space-time codes.

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Research Background (coherent systems)

• Diversity from space/multipath/Doppler can be
  jointly exploited (e.g. Giannakis et al 03,05 for
  a receiver with perfect CSI and block ML
  detection).
• Estimation-Diversity trade-off in block-fading
  channel is studied by Stark et al from an
  information-theoretic viewpoint
• We study this trade-off under the following
  assumptions
• The CSI is estimated from pilots (cf. J. K. Caver
  et al)
• Continuously time-varying channel instead of
  block fading channel
• Convolutional codes with finite interleaving
  depth are accounted. Previous work assumes either
  perfect interleaving or perfect CSI.
• We consider Direct Sequence spread spectrum since
  it allows simpler channel estimation than
  Frequency Hopping spread spectrum

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Coherent Systems Scenario 1(Convolutional
Codes with FD Interleaving)

• System Model
• DS-CDMA with BPSK modulation.
• Pilot and data channels are transmitted with
  different orthogonal codes.
• Channel estimator is an FIR filter.
• The effect from interleaving is approximated as
  separations of consecutive error symbols by
  interleaving depth I.

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Coherent Systems Scenario 1(Convolutional
Codes with FD Interleaving)

• Results

Pairwise error probability as a function of pilot
SNR for various values of Doppler spread and
interleaving depth
Data SNR 2.22 dB, pilot SNR 0.97 dB, 11-tap
FIR filter, interleaving depth 23, code rate
1/3, dmin 18, 220 info bits per block
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Coherent Systems Scenario 1(Convolutional Codes
with FD Interleaving)

• Comparison of between perfect CSI, perfect
  interleaving and realistic cases when both are
  imperfect

• Effects of pilot SNR, interleaving depth, and
  Doppler frequency can be observed
• Curves are close to perfect CSI performances at
  moderate SNR (10dB) and low Doppler frequency
• Curves converge to perfect interleaving at high
  Doppler frequency, even if the interleaving depth
  is low.

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Coherent Systems Scenario 1(Convolutional Codes
with FD Interleaving)

• Conclusions
• System performance has been shown to be a
  function of
• Autocorrelation function of the fading
  coefficients
• Multi-path profile
• Pilot to noise ratio
• Data to noise ratio
• Parameters of the channel estimator (taps, tap
  coefficients)
• Interleaving depth
• Coding characteristic
• The optimal Doppler spread which gives the best
  combination of diversity and CSI accuracy has
  been determined as a function of the above
  parameters.

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Coherent Systems Scenarios 2 (Alamouti
Open Loop STC)

• System Model
• DS-CDMA system with BPSK modulation
• Two pilot channels (one from each transmit
  antenna) use different orthogonal codes.
• Two data channels use the same orthogonal code,
  thus, the signals are combined at the receiver.
• The channel estimators are FIR filters.
• Alamouti space-time codes
• Decoding scheme
• Linear combining scheme space-time decoder
• ML space-time decoder

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Coherent Systems Scenario 2 (Alamouti
Open Loop STC)

• Results

Sequence error probability when the
linear combining scheme is used (circles are
simulation results)
Sequence error probability when the ML space-time
decoder is used (circles are simulation results)
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Coherent Systems Scenarios 2(Alamouti Open
Loop STC)

• Comparison between no transmit diversity, and
  Alamouti STC with the linear combining scheme
  when CSI is noisy and channels are time-varying

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Coherent Systems Scenarios 2

• Conclusions
• The linear combining scheme, which is the simple
  receiver suggested by Alamouti, performs well
  when the CSI is accurate and the channels are
  quasi-static.
• When the CSI is not accurate or the channels are
  fast fading, the linear combining scheme may be
  outperformed by the system without transmit
  diversity.
• ML space-time decoder is much more robust at
  large Doppler than the linear combining scheme
  space-time decoder.

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Noncoherent Space-time Signaling

• The study on coherent systems suggests that when
  the channel has high time-variation or the pilot
  is weak, we have to consider more robust systems
  by using noncoherent signaling.
• Two forms of noncoherently detectable space-time
  signaling are Unitary ST Modulation (USTM) and
  Differential Unitary ST Modulation (DUSTM). Both
  can offer full spatial diversity, if properly
  designed
• The USTM is designed for channels varying from
  block to block independently
• The DUSTM is appropriate for continuously
  time-varying channels
• Our focus is on DUSTM.

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Research background(Noncoherent ST signaling)

• Traditional DUSTM design is based on the
  assumption that the current and the previous
  received space-time signals experience the same
  channel. Also, in previous studies, only linear
  modulations are considered for symbols.
• We extend the investigation of DUSTM in two
  aspects
• We obtain closed-form expressions for the
  performance of DUSTM signals in the general
  time-varying channels with multiple-symbol
  decision feedback detection. The traditional
  design criteria is validated in this general
  setting.
• We study the use of offset modulation for symbols
  in DUSTM signals. Offset modulation avoids
  180degree phase transition in the transmitted
  signal and also achieves additional advantage in
  rate or diversity over non-offset DUSTM.

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Noncoherent System

• We study a Frequency-Hopping Spread Spectrum
  system with DUSTM (DUSTM-FHSS) as a possible
  physical layer for tactical ad hoc networks
• FHSS is relatively insensitive to the near-far
  problem and more easily operated in
  non-continuous spectrum than DS-CDMA
• Frequency diversity is achieved by hopping under
  proper coding and interleaving
• Channel estimation is hard in FHSS and
  noncoherent modulation is more practical

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Research background (DUSTM-FHSS system)

• We study the erasure insertion decoding at the
  receiver for a Reed-Solomon coded DUSTM-FHSS
  system. This extends previous work (e.g. Pursley
  et al) on RS-coded FSK-FHSS systems
• DUSTM can offer higher spectral efficiency than
  FSK and spatial diversity
• Acquiring and tracking the time-varying
  statistics of both channels and asynchronous
  interferences are studied. Those statistics are
  often assumed constant and known for each dwell
  in current literature.

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System Model
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Receiver Design

• Goal To reduce decoding error probability
• Basic idea Erasure insertion
• Block ECC can correct twice as many erasures as
  errors ( )
• Demodulator outputs an erasure when the ML
  estimate is regarded as unreliable (e.g. when a
  dwell is hit by strong PBI/MAI or experiences
  deep fade)
• Can be viewed as a simple, hard-decision based
  joint demod/decoding
• Approaches
• Bayesian erasure insertion optimal
• Likelihood Ratio Test (LRT) suboptimal,
  low-complexity

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Simulation Results

• Setting Jakes model the
  noise consists of thermal noise ( )
  and PBI, which is present with probability and
  distributed as
• two Tx antenans and two Rx antennas,
  RS(16,4) code

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BYU Data
Use the statistics to find the system performance
Find channel statistics
Compare analytical and simulation results
BYU data
Use the fading coefficients in Monte Carlo
simulations
Performance is found through simulations

• Experimental data can be approximated as Gaussian
  random variables with time-varying means.
• Prior to analysis and simulation, the
  time-varying means are found and removed from the
  experimental data.
• The real and imaginary parts of fading
  coefficients are correlated and do not have
  identical distributions. Therefore, the analysis
  was modified to take into account these
  behaviors.

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BYU Data

• Results

Comparison between analytical and simulation
results using BYU experimental data
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QualNet Simulator
Application
Source
Real network data
Transport

• Modify QualNet to
• Allow insertion of network layer solutions
• Accurately simulate time-varying channel
• Perform bit-level PHY layer operations

Network
Routing
MAC
PHY
Zeidler PHY layer diversity
Swindlehurst Optimal training for CSI
Channel
Jensen Time-varying channel data and models
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Conclusion

• The available physical layer diversity depends on
  the availability of CSI. We have studied systems
  where the receiver has noisy CSI or no CSI.
• The trade-off between estimation errors and
  space/time/frequency diversity are studied in
  detail. Critical Doppler spread and pilot power
  beyond which noncoherent transceiver is
  preferable are characterized.
• DUSTM in continuously-varying channels with
  advanced detection is analyzed and being extended
  to offset modulation.
• A coded DUSTM-FHSS system is proposed for
  tactical ad hoc networks physical layer. Erasure
  insertion decoding technique is studied for
  interference rejection.
• Some analysis results are verified using data
  collected at BYU.

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Future Work

• Closed-loop transmit diversity (or feedback
  beam-forming) in addition to Alamouti STC.
• Extend the non-convolutionally coded analysis of
  Alamouti STC and CLTD to convolutionally coded
  analysis to take into account the effect of
  channel variation, interleaving depth, pilot SNR,
  data SNR, channel estimator, and coding
  characteristic.
• Compare using multiple antennas for diversity and
  for multiplexing in a FHMA network.
• Protocol design and throughput analysis for ad
  hoc network based on FHSS.
• Determine the relative effectiveness of
  beamforming and STC in various ad-hoc networking
  environments
• Further exploitation of channel modeling with the
  help of BYU.
• Cross-layer simulation environment.