compressed sensing based uwb system
Download
Skip this Video
Download Presentation
Compressed Sensing Based UWB System

Loading in 2 Seconds...

play fullscreen
1 / 24

Compressed Sensing Based UWB System - PowerPoint PPT Presentation


  • 96 Views
  • Uploaded on

Compressed Sensing Based UWB System. Peng Zhang Wireless Networking System Lab WiNSys. Outline. Quick Review on CS Filter Based CS CS Based Channel Estimation CS Based UWB System Simulation Results Issues and Conclusions. RIP. Quick Review of CS.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Compressed Sensing Based UWB System' - garrison-pearson


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
compressed sensing based uwb system

Compressed Sensing Based UWB System

Peng Zhang

Wireless Networking System Lab

WiNSys

outline
Outline
  • Quick Review on CS
  • Filter Based CS
  • CS Based Channel Estimation
  • CS Based UWB System
  • Simulation Results
  • Issues and Conclusions
quick review of cs

RIP

Quick Review of CS
  • Sparse signal can be reconstructed from random measurements
  • However…
    • Matrices are non-causal
    • Causal system is more common in communications
filter based cs lti system

y

p

a

=

X

Filter Based CS (LTI System)
  • Filter based structure is more appropriate to model communication system
    • Causality
    • Quasi-toeplitz matrix
  • p is quasi-toeplitz
  • If…
    • p satisfied RIP
    • a is sparse
  • Then…
    • CS will work!
cs based uwb system
CS Based UWB System
  • Proposed system
    • Channel estimation
    • Signal reconstruction
cs based channel estimation
CS Based Channel Estimation
  • Goal:
    • Estimate the 5 GHz bandwidth channel impulse response at 500 Msps rate
    • Use the result in reconstruction matrix
cs based channel estimation2
CS Based Channel Estimation
  • Condition
    • Channel is sparse in time domain
      • Yes!
    • PN matrix satisfied RIP
      • Yes!
    • Sufficient measurements
    • SNR
cs based channel estimation3
CS Based Channel Estimation
  • Sufficient measurements
    • Not all samples have contribution
    • To get sufficient measurements
      • Long signal duration at RX
      • Higher sampling rate at RX
    • Sampling rate can be low if
      • Signal has longer duration
        • Longer PN sequence or
        • Longer channel delay spread
channel estimation simulation
Channel Estimation Simulation
  • Get the original indoor channel under estimation:
    • 3 GHz~ 8 GHz VNA data
    • Use matching pursuit with SINC function as basis to get TDL model
    • Time domain resolution = 50 ps (20 Gsps)
channel estimation simulation1
Channel Estimation Simulation
  • Estimation
    • PN length = 1024 ns, PN rate = 20 Gsps
    • Receiver sampling rate = 500 Msps
    • Use BPDN, SNR / Sample at RX= 10 dB
channel estimation simulation2
Channel Estimation Simulation
  • How to evaluate the result?
    • Mean square errors?
    • Supports?
  • Though the result is not accurate, we found that it performs good in CS-based UWB system
cs based uwb system1
CS Based UWB System
  • Goal:
    • Reconstruct transmitted sequence with sub-GHz sampling rate
system configuration

……

System Configuration
  • Symbol based bit sequence
    • 256 bins per symbol
    • Bin width = 1ns
    • 1 position is occupied in each symbol
  • Pulse generator
    • 3~8 GHz Gaussian pulse
    • Shapes the spectrum
system configuration1
System Configuration
  • Incoherent filter
    • FIR filter using PN sequence
    • PN sequence length = 128 ns
    • Bandwidth of the transfer function: 3~8 GHz
  • Channel
    • Real TDL channel model
    • Same as previous one
  • No down-conversion at RX
system configuration2
System Configuration
  • Reconstruction
    • Sampling rate:
      • 500 Msps, << 16 Gsps, Nyquist rate
      • Measurement duration = 512 ns
        • no ISI between measurements
    • Basis pursuit de-noise (BPDN)
      • We use the estimated channel to form
simulation results
Simulation Results
  • Simulation configuration
    • System sampling rate: 20 Gsps
    • Block error rate VS SNR / sample at receiver
    • Perfect synchronization
    • 2000 simulations for each plot
    • Perfect/Imperfect channel estimation
    • Various sub-Gsps sampling rate
      • 125 Msps, 250 Msps, 500 Msps
    • Use Sparselab to perform BPDN
simulation results2
Simulation Results
  • 500 Msps performs good
    • Error free for 2000 simulations at -5 dB
  • Estimated channel has similar performance
  • Higher sampling rate has better performance
    • More sufficient measurements
other issues
Other Issues
  • Does PN + Channel fits RIP?
  • Efficient BPDN algorithm for hardware?
    • Now each run for BPDN is about 0.1 s on Intel Core 2
    • Matrix size is 256*5120
  • Synchronization
    • Get the right matrix for reconstruction
  • Data rate
    • Now only use 1 position in 256 bins
    • Data rate = 16 Mb/s
  • Tractable performance
    • BPDN performance varies sharply with different parameters
conclusion
Conclusion
  • CS computation complexity trades for hardware complexity
    • No down-conversion, no Nyquist rate sampling
      • Only 1/20 of Nyquist rate
      • Even slower for high SNR
    • Huge size matrix, computation complexity and synchronization would be big problems for processing
references
References

[1] Emmanuel Candès, “Compressive Sampling”, in Int. Congress of Mathematics, 3, pp. 1433-1452, Madrid, Spain, 2006.

[2] Joel Tropp, Michael Wakin, Marco Duarte, Dror Baron, and Richard Baraniuk, “Random Filters for Compressive Sampling and Reconstruction”, in IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, France, May 2006.

[3] Richard Baraniuk and Philippe Steeghs, “Compressive radar imaging”, in IEEE Radar Conference, Waltham, Massachusetts, April 2007.

[4] Scott Shaobing Chen ,David L. Donoho ,Michael A. Saunders, “Atomic Decomposition by Basis Pursuit”, SIAM Journal on Scientific Computing, pp. 33-61, 1998.

ad