1 / 17

Turbo Multiuser Detection

Turbo Multiuser Detection. Group Members: -Bhushan G. Jagyasi -Himanshu Soni. Single user detection. Modula- -tion. Modula- -tion. b 1 (.). Decision. AWGN. g1. b^ 1 (.). g1. b 2 (.). Decision. g2. b^ 2 (.). r(t). g2. Noisy Channel. Decision.

elindley
Télécharger la présentation

Turbo Multiuser Detection

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Turbo Multiuser Detection Group Members: -Bhushan G. Jagyasi -Himanshu Soni

  2. Single user detection Modula- -tion Modula- -tion b1(.) Decision AWGN g1 b^1(.) g1 b2(.) Decision g2 b^2(.) r(t) g2 Noisy Channel Decision bn(.) b^n(.) gn gn Reciever / Detector Received signal , r(t) = b1g1f1+ b2g2f2+ ……bngnfn + n(t) After detector y(t)=b^1(t) = b1.g1.g1. f1+ b2.g2.g1.f2+ ……bn.gn.g1.fn + no.g1

  3. Received signal for jth user

  4. Turbo Principal • Concatenated coding and iterative decoding. Encoder Decoder

  5. ’Turbo’ in turbo-codes, does not apply to the code itself, but to the iterative way of decoding. The title Turbo is taken from the principle of the turbo engine. • Iterating the soft output of the convolutional code decoder back to the multiuser detector, a turbo multiuser detector architecture is created

  6. Transmitter block diagram [ From reference 3]

  7. Receiver block diagram (Turbo MUD) r(t) [ From reference 3]

  8. SISO block diagram [ From reference 3]

  9. SISO implementation • Output of Soft interference cancellation, yk(i), where,

  10. MMSE filtering output, Where,

  11. Log likelihood calculation Conditional Probability Log likelihood ratio where, It is used as feedback for soft interference canceller

  12. Results • Results are for following parameters • No of users=5 • No. of user bits =1000 • Channel noise= 16 db for all users • Receiver noise = 20 db • No. of iterations= 5

  13. Constellation plots (User 1)

  14. BER Plot (For one sample path ) For 5 user Signal to channel Noise ratio in dB----

  15. Average BER Plot (For ten sample path ) For 10 user BER Signal to channel Noise ratio in dB----

  16. Average BER Plot For 5 user BER Signal to channel Noise ratio in dB----

  17. References 1] Shimon Mosavi, “ Multiuser detection for DS-CDMA Communications”, IEEE Communication Magazine, pp124- 136,Oct.1996. 2] H.Vincent Poor, “Turbo Multiuser Detection: An Overview”,IEEE 6th international symposium on spread-spectrum technology and appliation, pp 583-588,Sept 6-8, 2000. 3] Gebrben Heinen, “ Turbo multiuser Detection Architectures”, M.Sc. Thesis, Dec. 2003 4] Xiaodong Wang and H.Vincent Poor, “Iterative (Turbo) Soft Interference cancellation and Decoding for Coded CDMA”, IEEE transaction on communications, vol.47, No.7, July 1999, pp1046- 1061,

More Related