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Impact of Channel Estimation Errors on the Performance of DFE equalizers with Space-Time Block Codes in Wideband Fading Channels Mohamed B Noune and Prof.

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Presentation on theme: "Impact of Channel Estimation Errors on the Performance of DFE equalizers with Space-Time Block Codes in Wideband Fading Channels Mohamed B Noune and Prof."— Presentation transcript:

1 Impact of Channel Estimation Errors on the Performance of DFE equalizers with Space-Time Block Codes in Wideband Fading Channels Mohamed B Noune and Prof. Andrew Nix Future Generation Communications Must offer a wide range of services any time, any place and at low cost. Exploit different transmission standards and technologies: SDR, All IP system,…etc. e.g. 3GPP LTE design includes: oDifferent Multiple Access Systems. oSoftware Defined Radio. oMIMO technology. oIP-v6. Introduction There is a strong demand for high capacity and high speed wireless data transfer rates. Outdoor communications systems operate with limited power and bandwidth. Various solutions currently exist to enhance the performance of wireless communications systems: oMultiple Access: TDMA, FDMA, CDMA. oModulation: GMSK, Adaptive Modulation. oSingle Carrier Vs. Multiple Carrier. Why go MIMO? Suitable for Non-LoS. Robustness. Increased Capacity. Increased Coverage. Scalability. Multi-Carrier Techniques: Suitable for large cells with high data rates. Efficient reuse of adjacent channels. Scalable Frequency Domain Equalizer (FDE). Advancements in FPGA technology permits low cost and low complexity transceivers. MCT suffers from high PAPR, which limits PA efficiency and mean output power No frequency diversity at symbol-level. General setup for an N T -by-N R STBC MIMO system Assessing the performance of DFE Equalizers for MIMO systems: Assumptions: High data rate picocell communications. 2-by-1 Alamouti system. Receiver Requires Channel Estimation. Channel estimation errors assumed to be zero mean, normally distributed. Transmission channels based on time varying Rayleigh fading taps ( Jakes model). White noise input data signal. Receiver structures STBC ML-receiver: DFE equalizer: consists of a Feedforward filter and a Feedback filter. The equalizers output is The DFE is synchronized to the last tap. DFE Receiver: Given the analysis in [7], if the input autocorrelation matrix is and the noise autocorrelation matrix is:, then the receiver input autocorrelation is The mean square error performance is. This translates to and Conclusions and Future Direction: Single carrier MIMO systems are well suited to the uplink transmission in a cellular picocell. The performance of the STBC receiver degrades as a result of mobility and channel estimation errors. DFEs can outperform the computationally demanding ML receiver in the case of high channel estimation error. A comparison needs to be established between the complexity of FDE, ML and DFE techniques. A study to determine how channel coding improves the error performance of DFEs is required. Results and Discussion: The ML detector is better than the DFE in terms of BER performance in the mobile case. The performance of the DFE approaches that of the ML detector when channel estimation error is included. The limitation in the BER performance of the DFE can be compensated by using multiple receiver antennas.


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