Post Detection Signal Quality for Direct-Sequence Spread Spectrum Matthew Crane, Case Western Reserve University Faculty Research Advisor: Dr. Pursley - PowerPoint PPT Presentation

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Post Detection Signal Quality for Direct-Sequence Spread Spectrum Matthew Crane, Case Western Reserve University Faculty Research Advisor: Dr. Pursley

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Title: Post Detection Signal Quality for Direct-Sequence Spread Spectrum Matthew Crane, Case Western Reserve University Faculty Research Advisor: Dr. Pursley


1
Post Detection Signal Quality for Direct-Sequence
Spread Spectrum Matthew Crane, Case Western
Reserve UniversityFaculty Research Advisor Dr.
Pursley
Results
Abstract Post Detection Signal Quality (PDSQ)
statistics can be used to determine the presence
and magnitude of multiple access interference as
well as provide a rough estimation of overall
signal quality. PDSQ statistics can be used in
adaptive transmission protocols to help determine
optimal transmission parameters to use.
  • Mathematical Formulation of System
  • Spreading Signal
  • Spread Signal
  • Chip Matched Filter

As multiple-access interference increases, the
bit error probability also increases
significantly. The PDSQ statistic allows us to
differentiate between errors caused by an
increase in the white Gaussian noise power and
errors caused by increased multiple-access
interference.
  • Motivation
  • Adaptive transmission
  • Channels are time-dependent and the level and
    type of interference varies randomly as time
    progresses
  • Transmission parameters can be selected based on
    the type of interference present to optimize
    energy efficiency with regard to signal quality
  • Adaptive transmission protocol
  • Determine interference levels, types
  • Adapt transmission parameters based on
    interference
  • N spreading factor reduces effect of Multiple
    Access Interference
  • Eb Energy per bit reduces propagation loss
  • Selection of incorrect parameters can disrupt
    communications or waste energy

The maximum-sidelobe PDSQ statistic (P) is shown
with varying spreading factors and varying levels
of multiple-access interference. This reveals two
things The PDSQ statistic accurately reflects a
degradation of signal quality due to an increase
in multiple-access interference, and the
magnitude of change in the PDSQ statistic
reflects the effect of the multiple-access
interference on the received signal.
The sum-of-squares PDSQ statistic (Q) graph
reveals characteristics similar to the
maximum-sidelobe PDSQ statistic. As
multiple-access interference increases, the
statistic decreases however, the relationship
between the magnitude of the change in the PDSQ
statistic and the effect on the signal are not as
clear.
  • Model Description
  • Simulated using Matlab
  • Data spread using m-sequence of length 4095
  • Channel simulated AWGN with spectral density N0/2
    and multiple access interference
  • Multiple access interference is chip synchronous,
    uses a different m-sequence of length 4095 and
    power equal to the transmitted signal
  • Conclusion
  • PDSQ detects presence of multiple-access
    interference
  • Magnitude of change in PDSQ statistic reveals
    magnitude of signal quality degradation
  • Future Work
  • Model AGC and SER statistics to
  • Increase simulation realism by adding multipath
    interference and propagation loss to channel
  • Add a time dependency to the channel and use the
    adaptive transmission protocol to select
    subsequent transmission parameters. incorporating
    adaptive transmission protocol
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