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 i th 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, ms = 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 ??