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 email: [e-mail], [e-mail]

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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. Andrew Nixemail
Mohamed.Noune_at_bris.ac.uk, Andy.Nix_at_bris.ac.uk

• 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
• Multiple Access TDMA, FDMA, CDMA.
• Modulation GMSK, Adaptive Modulation.
• Single Carrier Vs. Multiple Carrier.

• 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
• Different Multiple Access Systems.
• Software Defined Radio.
• MIMO technology.
• IP-v6.

• 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.

• Why go MIMO?
• Suitable for Non-LoS.
• Robustness.
• Increased Capacity.
• Increased Coverage.
• Scalability.

General setup for an NT-by-NR 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.

• 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.

• 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.

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