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Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
Multiuser Pre-equalization for Pre-Rake DS-UWB Systems
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Multiuser Pre-equalization for Pre-Rake DS-UWB Systems

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Design of Multiuser Pre-Rake Systems for Reliable Ultra-Wideband Communications

Design of Multiuser Pre-Rake Systems for Reliable Ultra-Wideband Communications

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  • 1. Design of Multiuser Pre-Rake Systems for ReliableUltra WidebandUltra-Wideband Communications ZAHRA AHMADIAN MICHAEL B. SHENOUDA LUTZ LAMPE
  • 2. Overview I t d ti Introduction to P R k UWB t Pre-Rake System Model Problem Formulation Convex Problem Formulation Results Conclusion
  • 3. Introduction to Pre-Rake UWB 1/2 Traditional Rake Receiver  Pre-Rake Transmission P E Pre-Equalized P R k li d Pre-Rake
  • 4. Introduction to Pre-Rake UWB 2/2 MISO Pre-Equalized Pre-Rake (Torabi et al. 2009) Multiuser MISO Pre-Equalized Pre-Rake
  • 5. System ModelTransmitter Receiver• Multiple Antenna Central transmitter • Single Antenna Receiver• Pre-Equalizing Filter • De-Spreading Filter• Up Sampler • Down Sampler• Spreading Code • Equalizing Gain• Pre-Rake Filter • Demodulator
  • 6. System Model Channel impulse responses generated according to 802.15.4a 802 15 4a model Discrete time baseband channel impulse response root-raised cosine pulse shaping filters p p g
  • 7. System Model Discrete-time complex base-band received signal p g
  • 8. Problem FormulationWhere, is the total transmit power and σu2 is the mean-square error.
  • 9. Convex Problem Formulationwhere .
  • 10. Results 1/3 26  = 0.08 24  = 0.1 22 20 18 [dB] 16 14 12 10 8 5 10 15 20 Lq
  • 11. Results 2/3 CM2 CM6 0.3 0.3 Lq = 10 Lq = 10 Lq = 20 Lq = 20 0.25 0.25 0.2 0.2 0.15 0.15  0.1 0.1 0.05 0.05 0 0 5 10 15 20 25 5 10 15 20 25  [dB]  [dB]
  • 12. Results 3/3 350  = 0 08 CM2 0.08 300  = 0.10 CM2  = 0.08 CM6  = 0.10 CM6 250# of channels with  < X 200 150 f 100 50 0 5 10 15 20 25 30 35 40 X [dB]
  • 13. Conclusion We have proposed the use of multiuser pre-equalization filters to mitigate the effect of ISI and MUI. We have formulated the filter optimization problem pertinent for the operation of such UWB systems systems. We have shown that this problem is equivalent to a convex QCQP problem The numerical results illustrated the efficacy of multiuser pre-equalization to achieve quality of service constraints with a minimal power budget budget. Future work• Design of multiuser pre-equalization filters subject to pre equalization spectral mask constraints.
  • 14. Thank you QUESTIONS ?

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