Xiaohua (Edward) Li Department of Electrical and Computer Engineering

1. Blind Channel Identification and Equalization in Dense Wireless Sensor Networks with Distributed Transmissions Xiaohua (Edward) Li Department of Electrical and Computer Engineering State University of New York at Binghamton xli@binghamton.edu http://ucesp.ws.binghamton.edu/~xli

2. Outline • Introductions on sensor network and blind equalization • Cross-correlation-based blind equalization: a cooperative communication approach • Cross-correlation and finite sample properties • Simulations • Conclusions

3. 1.1 Introduction: Sensor Network • Wireless sensor network: dense, cooperative • Sensor data and transmitted signals: highly cross-correlated • Cooperation: enhance cross-correlation Multi-hop Wireless Sensor Network

4. 1.2 Introduction: Blind Equalization • Blind channel identification and equalization in sensor networks • Mitigate multipath fading, inter-symbol interference • Remove training: save transmission energy and bandwidth, design convenience • Especially helpful in wideband sensor networks, e.g., acoustic, video • Need to compete with training-based methods in computational efficiency and robustness • Traditional blind methods not desirable • Need new blind methods

5. 1.3 Cooperative Equalization • Observe: • Traditional blind methods: signals from different users are un-correlated • Sensor networks: signals among sensors are highly cross-correlated • Can we utilize cross-correlation to assist blind equalization? • A new way of blind equalization based on cooperative communications • Passive cooperation: transmitting nodes do not cross-talk • Useful for general distributed networks

6. 2.1 System Model Sensor network Transmission block diagram of each sensor

7. 2.2 Cross-Correlation Assumption • Source sequence cross-correlation  symbol sequence cross-correlation • By scrambling, cross-correlation among transmitted signals becomes highly structural: only one non-zero cross-correlation coefficient • Result: efficient/robust blind algorithms

8. 2.3 Received Signal Model • A receiving node receives un-overlapped signals from transmitting sensors

9. 2.4 Blind Channel Estimation • Computationally efficient, robust to ill-conditioned channels, optimal utilization of all received signals

10. 2.5 Blind Equalization • Computationally efficient (linear), robust to ill-conditioned channels, fast convergence

11. 3.1 Cross-Correlation Property • Find relation between (analog) source signal cross-correlation and (digitized) binary sequence cross-correlation • Major results and simulation verification

12. 3.2 Finite Sample Effect • Samples may be limited, samples contributing to cross-correlations are even more limited • Find the relation among symbol amount, cross-correlation, and channel estimation MSE

13. 4.1. Channel Estimation Simulation • Short Data Record • Proposed: J=10 sensors. One packet (260 symbols) • Training : 20% symbols for training • Proposed blind method has near-training performance

14. 4.2 Blind Equalization

15. 4.3 Blind Channel Estimation • Long Data Record • Proposed: 10 sensors. 20dB SNR. 260 symbols/packet • Training : 20% symbols for training • New algorithms both have near-training performance

16. 4.4 Blind Equalization

17. 4.5 Convergence Property

18. 5. Conclusions • Propose a new blind channel identification and equalization scheme for wireless sensor networks • Utilize cross-correlation among sensor signals • Have near-training performance, computation efficiency, and robustness to ill-conditioned channels • A general approach of exploiting (passive) cooperative communications in distributed networks