MULTICELL UPLINK SPECTRAL EFFICIENCY OF CODED DS- CDMA WITH RANDOM SIGNATURES

1. MULTICELL UPLINK SPECTRAL EFFICIENCY OF CODED DS- CDMA WITH RANDOM SIGNATURES By: Benjamin M. Zaidel, Shlomo Shamai, Sergio Verdu Presented By: Ukash Nakarmi University of Houston Academic Advisor: Dr. Zhu Han

2. Outline: • Introduction • System Model • Spectral Efficiency • Power Allocation Policies • Conclusion

3. INTRODUCTION • Multi Cell Uplink communication Model is Suggested • Comparative study of Spectral efficiency for Different Multi User detection • Compares Four Detection Techniques : • Matched filter detection • Single Cell Optimum Detector • MMSE • MMSE- Successive Interference Cancellation( MMSE-SC)

4. Under Three Power Allocation Models: • Equal Power Policy • Equal rate Policy • Maximum Spectral Efficiency Policy • CDMA System with Random Spreading Sequences is Examined • Scenario: • Linear Cell Array Model • Number of users and Processing gain Goes to Infinity • System Load: finite Constant

5. Where Do we Use Random Matrix Theory ? • In the Performance Measurement • Signal to Interference Plus noise ratio • Spectral Efficiency • Converge to Deterministic Value • Performance Measurement are function of Eigen values Distribution Of Random Matrices • Converges with our Assumption of Numbers of User going to Infinity. • Uses Stieltjes Transform

6. SYSTEM MODEL • Assumes linear Cell Array Model • Given by Wyner’s Cell Model Fig: Linear Cell Array Model

7. Yi=Signal vector received at Arbitrary cell at Discrete time related to ith symbol • Xi= [x1i, ……………, xKi] • Denotes Vectors of Symbol from the Users Operating in Adjacent Cells. • The symbols are iid Gaussian : Capacity Achieving parameters:

8. S are the NxK Matrix • Columns are N chip long Random spreading signature • K is the Number of Users in the Cell considered • Power Allocation: • Same power Allocation Policy is Applied to All Cell. • Power Assignment Function: Constraints:

9. SPECTRAL EFFICIENCY • Performance Measure parameter: Spectral Efficiency gk is Total number of bits per chip for k user. As: K-> infinity, g(k/K)-> g(x)

10. Matched Filter • Passes Received Signal • Treats all Interfering Signal as AWGN • Converges to our Assumption K,N-> Infinity, B= Constant • Multiuser Efficiency: Where E{ H(p)} is the Expectation of Limiting function H(P) to which Distribution Of Received Power Converges. Spectral Efficiency:

11. Minimum Mean Square Error Detector • Passes Received Signal • Minimizes Mean Square Error • With same K, N Assumption: Where, ȵms = Multiuser Efficiency for MMSE

12. SINGLE CELL OPTIMUM DETECTOR • Uses relation Between Optimum Multiuser detector and Linear MMSE

13. MMSE Successive Interference Cancellation: • Has Linear MMSE at Each Stage • From First User in Cell keeps on Cancelling Interferences • So Multi User Efficiency for Each User within Cell also Differs • Spectral Efficiency:

14. POWER ALLOCATION POLICIES • EQUAL POWER : For Matched Filter: FOR SCO:

15. Fig: Spectral Efficiency with Equal Power • MMSE-SC is has Optimal Efficiency • As E/N increases MMSE surpasses SCO • But if Codes of Adjacent Cells are known , Then MMSE- SC is no more Optimum.

16. Fig: Comparison Of Equal Power and Equal rate Allocation • For Low E/N Ratio Equal Power and Equal rates have comparable Spectral Efficiency

17. Fig: Spectral Efficiency: for Optimum For Low Eb/No Matched Filter and SCO has More Spectral Efficiency than MMSE AND MMSE- SC

18. CONCLUSION • Analyzed Spectral Efficiency of Four Multi User Detector • Comparison Of Power Allocation policy • Considers Simple Linear Array Cell • Can be implemented for two dimensional Hexagonal, Multi Cell Model

19. QUESTIONS ?? THANK YOU