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Optimal Sequence Allocation and Multi-rate CDMA Systems Krishna Kiran Mukkavilli, Sridhar Rajagopal, Tarik Muharemovic, Vikram Kanodia.

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Presentation on theme: "Optimal Sequence Allocation and Multi-rate CDMA Systems Krishna Kiran Mukkavilli, Sridhar Rajagopal, Tarik Muharemovic, Vikram Kanodia."— Presentation transcript:

1 Optimal Sequence Allocation and Multi-rate CDMA Systems Krishna Kiran Mukkavilli, Sridhar Rajagopal, Tarik Muharemovic, Vikram Kanodia

2 Motivation 4 3rd Generation Comm. Systems –Multimedia(Data, Voice, Video) –Multiple Rate Comm. 4 Multi-Rate Detection 4 Users entering/leaving the system 4 Optimal Sequence Allocation to achieve Capacity.

3 Outline 4 Conventional CDMA multiuser system 4 Discussion of multirate systems –Methods of multirate CDMA access –Performance of multiuser detectors 4 Interference avoidance 4 Application to variable number of users

4 Multi-rate CDMA systems 4 Multi code access (MC) –Give more Codes 4 Variable spreading length (VSL) –Change Spreading Length 4 Variable chip rate(VCR) –Change Chip Frequency

5 Multi code (MC) 4 Higher Rate users assigned more codes 4 Data transmitted in parallel 4 “Virtual User” Concept  Same Spreading for all users.

6 Multi Code User Rate R User Rate 2R Code 1 Code 2 Code 3 T

7 Variable spreading length(VSL) 4 Higher Rate Users allocated smaller spreading lengths 4 For detection, rate of slowest user is considered. 4 More bits of higher rate users detected per bit of lower rate users 4 For detection, put 0’s

8 Variable Spreading Length User Rate R User Rate 2R 2T T

9 Variable Chip Rate(VCR) 4 User allocated different chip rates 4 Larger Bandwidth required 4 Requires more RF hardware –Oscillators 4 Not practical for implementation

10 Variable Chip Rate User Rate R User Rate 2R T 2T

11 Implementation Aspects 4 VSL and VCR have a sparse correlation matrix 4 VCR requires larger bandwidth 4 MC requires more codes 4 VSL proposed for next generation systems

12 Multiuser Detectors 4 Maximum likelihood detector (MLD) 4 Conventional single user detector (SUD) 4 MMSE detector 4 Decorrelating detector

13 Simulations 4 Four users –2 users at rate R –2 users at rate 2R 4 Random Codes 4 Spread length 32 for low rate user 4 10000 bits 4 Channels –AWGN –Fading - Jakes Model

14 Investigate... 4 Performance of multiuser detectors 4 Near far problem in detectors 4 Performance of high rate and low rate users in MC and VSL systems –All users with equal power –Users with unequal power

15 23456789101112 10 -4 10 -3 10 -2 10 10 0 SNR BER BER comparison for different detectors in multi code system MLD MMSE Decorrelator Single user detector

16 23456789101112 10 -4 10 -3 10 -2 10 10 0 BER comparison for detectors with unequal powers SNR BER MLD Equal Power MLD Unequal Power SUD Equal Power SUD Unequal Power

17 23456789101112 10 -4 10 -3 10 -2 10 10 0 SNR BER Comparison of Different Rate Users in MC and VSL High rate MC High rate VSL Low rate MC Low rate VSL

18 VSL System 4 Virtual user from high rate user –lower spreading length –lower interference (other virtual users are orthogonal) 4 High rate user –interference from same number of virtual users with lower spread length

19 Variable Spreading Length User Rate R User Rate 2R 2T T

20 23456789101112 10 -4 10 -3 10 -2 10 10 0 SNR BER Near Far effect for Different Rate Users in MC and VSL Low rate MC Low rate VSL High rate MC High rate VSL

21 Results 4 Multi Code –High rate and low rate users have same performance (both BER and NFR) 4 VSL –Low rate users have bad BER and NFR –High rate users’ performance is similar to multicode access system.

22 Interference Avoidance in Wireless Multiuser Systems 4 Interference Avoidance  send where there is less noise 4 Fixed modulation - traditional approach –TDMA –FDMA –CDMA –CWMA 4 Future wireless systems - dynamically adapt to the changing interference pattern

23 Preliminaries for Multiuser Systems 4 System model:  Capacity region: R1R1 R2R2   X1X1 XMXM N Y (class notes pg. 5-12)

24 11.522.533.544.555.56 7 7.5 8 8.5 9 9.5 Iteration Number Total Square Correlation Total Square Correlation vs Iteration Number Total Square Correlation Optimum Lower Bound

25 Preliminaries for Multiuser Systems 4 Sum Capacity: –W : channel bandwidth – P i : power of i-th user – N 0 : noise power spectrum (class notes pg. 5-12)

26 Multiuser Spread Spectrum Systems 4 System model Y sMsM   X1X1 XMXM N   s1s1

27 Multiuser Spread Spectrum Systems 4 Sum Capacity: –Optimum sequences maximize Sum Capacity 4 Total Square Correlation (TSC): – –Max. Sum Capacity  Min. TSC

28 Eigen-Algorithm 4 Iterative reduction of TSC –User k updates his spreading sequence –Rayleigh quotient –Choose s k to be eigenvector with smallest eigenvalue

29 810121416182022 10 -4 10 -3 10 -2 10 10 0 SNR BER Performance comparison of optimal codes with random codes Random Code Allocation Optimal Code Allocation

30 678910111213141516 10 -3 10 -2 10 10 0 SNR BER BER Performance with an Incoming User Random Code to new user Iteration for new user only Optimal Code Allocation

31 Conclusions 4 Significant improvement in performance with optimal codes 4 Iterative algorithm compatible with user dynamics 4 Good sub-optimal schemes for user addition 4 Can be combined with the multi- rate schemes


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