multicell uplink spectral efficiency of coded ds- cdma with random signatures by: benjamin m....
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MULTICELL UPLINK SPECTRAL EFFICIENCY OF CODED DS- CDMA WITH
RANDOM SIGNATURES
By: Benjamin M. Zaidel, Shlomo Shamai, Sergio Verdu
Presented By: Ukash NakarmiUniversity of Houston
Academic Advisor: Dr. Zhu Han
Outline:• Introduction• System Model• Spectral Efficiency• Power Allocation Policies• Conclusion
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)
• 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
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
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SYSTEM MODEL
• Assumes linear Cell Array Model• Given by Wyner’s Cell Model
Fig: Linear Cell Array Model
• 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:
• 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:
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)
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:
Minimum Mean Square Error Detector
• Passes Received Signal• Minimizes Mean Square Error• With same K, N Assumption:
Where, ȵms = Multiuser Efficiency for MMSE
SINGLE CELL OPTIMUM DETECTOR
• Uses relation Between Optimum Multiuser detector and Linear MMSE
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:
POWER ALLOCATION POLICIES
• EQUAL POWER :
For Matched Filter:
FOR SCO:
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.
Fig: Comparison Of Equal Power and Equal rate Allocation
• For Low E/N Ratio Equal Power and Equal rates have comparable Spectral Efficiency
Fig: Spectral Efficiency: for Optimum
For Low Eb/No Matched Filter and SCO has More Spectral Efficiency than MMSE AND MMSE- SC
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
• QUESTIONS ??
THANK YOU