Compressed Sensing Based munication System基于压缩感知的超宽带通信系统.ppt

Compressed Sensing Based UWB System
Peng Zhang
working 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
Sparse signal can be reconstructed from random measurements
However…
Matrices are non-causal
Causal system is more common munications
Filter Based CS (LTI System)
Filter based structure is more appropriate to munication system
Causality
Quasi-toeplitz matrix
p is quasi-toeplitz
If…
p satisfied RIP
a is sparse
Then…
CS will work!
=
X
a
p
y
CS Based UWB System
Proposed system
Channel estimation
Signal reconstruction
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 Estimation
Architecture
CS Based Channel Estimation
Condition
Channel is sparse in time domain
Yes!
PN matrix satisfied RIP
Yes!
Sufficient measurements
SNR
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
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)