Data Detection for Cooperative Vehicular Communication Systems with Unknown Channels

1. Data Detection for Cooperative Vehicular Communication Systems with Unknown Channels Lanlan He, Shaodan Ma, Yik-Chung Wu, Tung-Sang Ng Dept. of EEE, The University of HongKong 13th Dec., 2010

2. Cooperative Vehicular Communication Systems • Advantages of Cooperative System: • Link reliability • Spectral efficiency • Fixed infrastructure not required • Challenges of Cooperative Vehicular System: • High mobility -> time-varying channels-> ICI • Existing work [5]: • A special case : phase changes only • Require all the data to be known • One relay Fig.1. A typical cooperative vehicular communication system [5] E. Onen, N. Odabasioglu, and A. Akany, “Time-frequency based channel estimation for high-mobility OFDM systems - part II: cooperative relaying case,” EURASIP J. Advances on Signal Processing, 2010 [Online]. Available at http://www.hindawi.com/journals/asp/aip.973286.pdf

3. Multi-path Time-varying Channels • Features: • Rayleigh distributed; • independent taps; • Auto-correlation of the l-th tap follows the classical Jakes’ model: : average power of the l-th tap : zero-order Bessel function of the first kind : Doppler shift between source and the k-th relay normalized by the subcarrier spacing : sample interval

4. Amplify-and-Forward cooperative scheme • Amplify-and-Forward (AF) scheme: relays simply amplify the signals from the source and forward them to the destination. • Decode-and-Forward (DF) scheme : relays first fully decode the transmission from the source and then forward that information to the destination. • AF VS DF: • low complexity at relays • low power consumption at relays • low time delay of the whole system • complex receiver design required

5. System Model • Transmitted signal at source: • Signal processing at relays: • Received signals: • Transmitted signals: • Received signal at destination: • Compact expression:

6. Reformulation with Basis Expansion Model (1) • Generalized complex exponential basis expansion model (GCE-BEM): • Equivalent system model after using GCE-BEM: • are diagonal matrices • are Toeplitz matrices Reformulation again

7. Reformulation with Basis Expansion Model (2) represents the combined effects of represents the combined effects of Merge K relay into an equivalent one Compact the expression by combining the same basis:

8. Channel estimation and data detection - channel estimation (1) • Stack all received signals corresponding to pilot subcarriers: • In matrix form: Key word:

9. Channel estimation and data detection - channel estimation (2) • has high probability to be rank deficient • Tikhonov regularization method: : regularization matrix (i.e., identity matrix for minimum energy, a banded matrix for maximum flatness.) : regularization parameter balancing minimization of the two terms in the function (chosen according to L-curve) • Lease square solution with Tikhonov regularization:

10. Channel estimation and data detection - data detection (3) • Rewrite system model as: • LMMSE solution with :

11. Simulation results (1)

12. Simulation results (2) • The proposed scheme performs better than that without considering the mobility. • The performance of the proposed scheme improves significantly when more pilots are inserted. This is not the case without considering mobility compensation. Fig.2. Data detection performance of the proposed scheme.

13. Conclusions • Data detection for an OFDM-based AF cooperative vehicular communication system with unknown channels has been investigated. • By reformulating the system model using the GCE-BEM, a channel estimator has been developed based on the Tikhonov regularization method ; A data detection scheme has also been proposed based on the LMMSE criterion. • Simulation results have shown that, by taking the time-variation of the channels into account, the proposed scheme significantly outperforms that without considering the time-variation of the channels.

14. Q&A