Channel Equalization for STBC-Encoded Cooperative Transmissions with Asynchronous Transmitters

1. Channel Equalization for STBC-Encoded Cooperative Transmissions with Asynchronous Transmitters Xiaohua (Edward) Li, Fan Ng, Juite Hwu, Mo Chen Department of Electrical and Computer Engineering State University of New York at Binghamton {xli, fanng1,jhuw1,mchen0}@binghamton.edu http://ucesp.ws.binghamton.edu/~xli

2. Summary • Equalization for STBC-encoded cooperative transmissions • Asynchronous transmitters create ISI even in flat-fading environment • ISI channels adjustable by receiver • Viterbi equalizer for near-optimal performance • Efficient linear-prediction-based equalizer • Performance of cooperative transmission studied by simulations

3. Contents • Introduction • Cooperative transmissions with asynchronous transmitters • Viterbi equalizer • Linear equalizers: linear prediction • Simulations • Conclusions

4. Introduction • Cooperative transmissions • Use STBC for diversity, power efficiency • Challenges: • Imperfect synchronization among transmitters: conventional STBC receiver not applicable • Performance degradation: compromise advantage of cooperative transmissions • Objectives: • New receiver equalization techniques • Performance comparison: asynchronous cooperative, or non-cooperative transmissions

5. 2. Cooperative transmissions with asynchronous transmitters • Assume • Transmit nodes 1 to J transmit symbols {s(n)} with STBC • No perfect synchronization in time (local clock, transmission delay, propagation delay) • Frequency synchronization not addressed, dealt with by adaptive equalizer

6. Channel model (J transmitter, a singlereceiver, flat fading)

7. 3. Viterbi equalizer • Consider J=2 and Alamouti STBC for simplicity • Receiver adjust δ: short channel, strong h2(0) Odd delay d Even delay d between transmitters

8. Channel model with uncoded symbols • Even delay d. Even/odd samples are • Odd delay d, similarly available • Viterbi equalizer available • Complexity: • With decision feedback: • Complexity reduced by adjusting δ

9. 4. Linear-prediction-based equalizer • Choose proper δ to make h1(0) dominating • Construct vector model • Special structure: H has dominating diagonal • , Good for linear prediction • Example:

10. Linear prediction: • Proposition:

11. Properties • Symbols estimated from linear prediction error y(2n) and y(2n+1) • Efficient adaptive implementation: complexity O(N), track residue carrier induced time-variation • Robust: most ill channel conditions avoided by selecting proper δ

12. Simulations Color codes: Convention STBC decoder used in asynchronous coop transmission. Non-cooperative transmission, flat fading channel Proposed Viterbi equalizer with asynchronous coop transmission Optimal STBC with perfect synchronization QPSK, J=2. d=1. VA has 128 states.

13. Viterbi equalizer with decision feedback. 4 trellis states. VA with DF, delay d=10. VA with DF, delay d=2. VA without DF, delay d=2.

14. Linear equalizers: QPSK. d=10. Equalizer length N=20. • Conventional STBC decoder used in asynchronous coop transmission • MMSE equalizer used in asynchronous coop transmission • Non-cooperative transmission, dispersive channel • Proposed linear-prediction-based equalizer • Conventional STBC with dispersive channel

15. Conclusions • Equalizers for STBC cooperative transmissions when transmitters are not synchronized • Viterbi equalizer: performance near conventional STBC, high complexity • Viterbi equalizer with feedback: slight performance loss, extremely reduced complexity • Linear prediction-based equalizer: linear complexity, performance better than non-cooperation, much worse than conventional STBC (all in dispersive channel)