ITERATIVE CHANNEL ESTIMATION AND DECODING OF TURBO/CONVOLUTIONALLY CODED STBC-OFDM SYSTEMS

1. ITERATIVE CHANNEL ESTIMATION AND DECODING OFTURBO/CONVOLUTIONALLY CODED STBC-OFDM SYSTEMS Hakan Doğan1, Hakan Ali Çırpan1, Erdal Panayırcı2 2 Kadir Has University Electrical&Electronics Engineering Department 1 Istanbul University Electrical&Electronics Engineering Department

2. Outline • Introduction • STBC-OFDM with outher channel coding • Represantation of the Channel  KL Expansion • MAP-EM Channel Estimation • Performance Analysis • Simulations Results • Conclusion • Acknowledgement

3. Introduction Why do we use STBC-OFDM systems with outher channel encoder • Space-time block coding (STBC) has been proposed by Alamouti, and later generalized by Tarokh et al. as a effective transmit diversitytechnique formitigating the detrimental effects of channel fading. • Unfortunately, their practical application canpresent a realchallenge to channel estimation algorithms, especially when thesignal suffersfrom frequency selective multipath channels. • One of the solutions alleviating the frequencyselectivity is the use of OFDM together with transmit diversity which combats long channelimpulse response by transmitting parallel symbols over many orthogonal subcarriers yielding aunique reduced complexity physical layer capabilities. • STBC are not designed to achieve an additional coding gain. Therefore, an outer channel codeis applied in addition to transmit diversity to further improve the receiver performance.

4. Introduction The goal of this study • We propose Expectation Maximization (EM)-based Maximum A Posterior (MAP)channel estimation algorithm for space-time block coded orthogonal frequencydivision multiplexing(STBC-OFDM) systems with outer channel coding in unknown wireless dispersive channels. • The proposed channel estimation approach employs a convenient representation of the discretemultipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansionand finds MAPestimates of the uncorrelated KL series expansion coefficients. • Based on such an expansion, no matrixinversion is required in the proposed MAP estimator. • Moreover, optimal rank reduction is achieved byexploiting the optimal truncation property of the KL expansion resulting in a smallercomputational loadon the iterative estimation approach.

5. Introduction The goal of this study • It is clear that good channel codes are more sensitive to the poorly estimated channel.With high correlation between the coded bits, a well designedchannel code is more sensitive to channel estimation errorswhich might cause severe error propagation in the decodingprocess. • To understand the behavior of different channel encoders, we therefore consider both turbo and convolutionallycoded systems.

6. Transmitter Channel encoded and interleaved symbols yield an independent symbol stream Resorting subchannel grouping, X(n) is coded into two vectors Tx1 O F D M Channel Encoder STBC Encoder  Tx2 O F D M

7. Received Signal Model • To simplify theproblem, we assume that the complex channel gains remainconstant across two consecutive STBC-OFDM blocks.

8. Channel Modelling (KL expansion) An orthonormal expansion of the Hinvolves expressing the Has a linear combination of the orthonormalbasis vectors Weights ofthe expansion The autocorrelation matrix can decomposed as Orthonormal basis vectors

9. Normalized discrete channel-correlations for differentblocksand subcarriersof the channel model

10. Receiver structure Rx Channel Estimation & STBC Decoder OFDM Demodulator MAP Decoder -1 Nonlinear function  • How it Works • First iteration • EM based channel estimator computes channel gains according to pilot symbols • Output of channel estimator is used STBC demodulator • Equalized symbol sequence is passed through a channel interleaver and MAP decoder module • LLR of coded and uncoded bits are yielded • Next iteration • LLRs of coded bits are reinterleaved and passed through a nonlinearity (soft values calculated) • MAP-EM channel estimator iteratively estimate channel by taking received signal and interleaved soft value of transmitted symbols which are computed bu outher channel decoder in the previous iteration.

11. MAP channel estimation • Directly solving this equation is mathematically intractable.However, the solution can be obtained easily by means of theiterative EM algorithm. After long algebraic manipulations the expression of the reestimate of • No matrix inversion • Moreover, by selecting r orthonormalbasis vectors among all basis vectors truncation property could be employed

12. Simulations Parameters

13. Simulations MSE performance of EM-MAP channel estimator for Turbo coded STBC-OFDM systems, (fd = 50,PIR = 1 : 8)

14. Simulations BER performance of Turbo coded STBC-OFDM Systems according to number of used KL coefficients, (fd =50,PIR = 1 : 8)

15. Simulations EM-MAP channel estimator BER performance of theTurbo/Convolutional coded STBC-OFDM systems as afunction of PIRs for fd = 50 More sensitive to channel estimation errors

16. Conclusion • It is observed that the proposed EM-MAP outperforms the EM-ML as well as PSAM techniques. Based on such representation, we show that no matrix inversion is needed in the EM based MAP channel estimation algorithm. Moreover, a simplified approach (truncation property) with rank reduction is also proposed. • Turbo coded receiver structure more sensitive to channel estimation errors than convolutional coded receiver structure was shown. It has been demonstrated that receiver with turbo codes perform outperforms convolutional coded receiver structures assuming channel estimation performance is sufficient.

17. ACKNOWLEDGEMENT This research has been conducted within theNEWCOM Network of Excellence in Wireless Communications fundedthrough the EC 6th FrameworkProgramme. This work was alsosupported in part by the Turkish Scientific and Technical ResearchInstitute (TUBITAK) under Grant 104E166.

18. Thank You If you have any question/suggestion please contact: hdogan@istanbul.edu.tr