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 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 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) 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! = 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)
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