1. DOWNLINK SCHEDULING IN CDMA NETWORKS GUIDE : Mrs. S.Malarvizhi Group : A5 G.R Brijesh (8009504) Deepu K. Pillai (8009510) Regi Thomas George (8009560) V. VijayKrishnan (8009580)

2. PROJECT ABSTRACT To implement rate adaption in code division multiple access (CDMA) systems. Accounts for traffic burstiness and time varying fading for studying the multi-access interference (MAI) Data Traffic (Users) is modeled as a self similar distribution To implement Admission Control Study the performance characteristics of the system such as throughput, interference depending on the number of ON/OFF users.

3. PROJECT MODULES Self Similar Sequence Generation Multi Access Interference Prediction Rate Control Admission Control

5. SELF SIMILAR SEQUENCE & GENERATION Why Self Similar ? –It has been proved that web traffic exhibits Self Similar Properties –It has also been proved that the use of traditional models can result in overly optimistic estimates of performance of telecommunication networks. –FGN-DW (Fractional Gaussian Noise - Daubechies wavelets ) is used to generate SS Sequence

6. Input H= 0.5Input H= 0.6 SELF SIMILAR SEQUENCE PLOT

7. ANALYSIS OF SELF SIMILAR SEQUENCE Anderson – Darling Goodness of Fit Test Sequence Plot Periodogram Plot R/S Statistic Plot Variance Time Plot Whittle’s appox. Maximum likelihood estimate

8. Estimating ‘H’ :- Variance – Time Plots Sequence divided into non-overlapping blocks of size ‘m’ For each ‘m’, Variance of the sequence is determined Graph is plotted between Var [X(m)] & m in log scale The slope of the plot = (-  ) H = 1 – (  /2)

9. Input H= 0.5Input H= 0.6 Variance – Time Plot for Estimating ‘H’

10. H Estimation : Table

12. INTERFERENCE How Does Interference Occur : –CDMA assigns Unique Codes (for spreading) for users (within a cell). These codes are reused in different cells. –PN Sequences are used as Spreading Codes as they have low cross correlation values –BTS transmits messages to all users with different power levels. –Each user receives his signal as well as the summation of all other signals thus causing interference. Use of Orthogonal Codes (having Zero Cross Correlation values) and decreasing Code Reuse factor reduces Interference

13. MAI/SINR Prediction MAI – Multi Access Interference SINR – Signal to Interference+Noise Ratio Why MAI is Important –Single Most limiting factor in CDMA Systems –MAI has to be kept under control as it can cause sudden degradation in system performance MAI = IntraCell + InterCell –IntraCell Interference : Interference due to users within the same cell –InterCell Interference : Interference due to users from other cells

14. MAI / SINR Prediction FORMULA Intra Cell Interference for ‘USER 1’ in ‘CELL J’ Is the transmission power from the base station for User k in Cell J at time t Denotes the fading coefficient from the base station in Cell J to the user under consideration Interference for User 1 in Cell J due to all other users in Cell J

15. SINR for ‘USER 1’ in ‘CELL 1’ The Product of User 1 Transmission power and his fading coefficient The interference for User 1 in Cell 1 due to all users (Intra + Inter) i.e. MAI (Multi Access Interference) is the processing gain (W/R), ambient additive white gaussian noise respectively

16. MAI :- GRAPH

17. Input H= 0.5 Input H= 0.6 Variance-Time Plots for Estimating ‘H’

18. H = 0.5 H = 0.6 The Table above shows the calculated H for MAI and SINR for Input H=0.5 & H=0.6 It is Seen that both MAI and SINR also exhibit Self Similarity H Estimation For MAI/SINR

20. RATE CONTROL Why Rate Control ? –CDMA Systems exhibit Periods of strong or weak MAI –Constant Rate System Fails To Effectively use these conditions –Rate is varied in accordance to SINR/MAI thus providing Greater Rate at periods of Lesser Interference Lesser Rate at periods of Greater Interference –It also provides a stable system with controlled interference

21. Rate Control : Algorithm Generate SS Sequence and Normalize it Perform SINR/MAI Prediction If SINR is not within threshold then –Adjust Rate to bring SINR within threshold Higher SINR – Reduce SINR thus Increasing Rate Lower SINR – Increase SINR thus Decreasing Rate –If Rate < Threshold Rate then drop users until rate meets threshold rate

22. Rate Control : Flowchart

23. Rate Control (contd) :-

24. Rate Control For H =.5

25. Graph Contd…

26. Data From Simulation Rate Control : Graphs & Analysis

28. ADMISSON CONTROL Why Admission Control ? –Inclusion of an User can degrade the system performance as MAI increases –It is very important for the system to check/predict the interference caused by the admission of each user before actually admitting him –Admission Control helps in maintaining the overall system performance.

29. ADMISSION SCHEME I The MAI/SINR due to the addition of new User is predicted over a ‘WINDOW of N Slots’. If All N slots satisfies the MAI requirement, then the user is admitted for that window and the window moves by ‘1 Slot’ If MAI requirement is not satisfied, then the User is rejected and the window moves by ‘1 Slot’

30. Diagrammatic Representation Observation Window of ‘N’ Slots : If SINR Requirement is Satisfied – Admit User :

31. If SINR Requirement is not Satisfied – Reject User (for the window): After Admit/Reject the window is slid across 1 slot :

32. Admission Scheme I :- Flowchart

33. Admission Scheme I (contd) :-

34. ADMISSION SCHEME II The MAI/SINR due to the addition of new User is predicted over a ‘WINDOW of N Slots’. If All N slots satisfies the MAI requirement, then the user is admitted for that window and the window moves by ‘N’ Slot’ If MAI requirement is not satisfied, then the User is rejected and the window moves by ‘1 Slot’

35. Diagrammatic Representation Observation Window of ‘N’ Slots : If SINR Requirement is Satisfied – Admit User :

36. The Window moves by N slots : If SINR Requirement is not Satisfied – Reject User (for the window): After Reject the window is slid across 1 slot :

37. Admission Scheme II :- Flowchart

38. Admission Scheme II (contd) :-

39. MOBILITY & LIMITED MOBILITY MOBILITY –During the entire run (simulation), the power matrix randomly changes for each run thus giving a sense of mobility to the user LIMITED MOBILITY –A Constant power matrix is first generated for different power levels and the matrix remains constant for each run

40. ANALYSIS : Graphs & Tables LIMITED MOBILITY

43. ANALYSIS : Graphs & Tables MOBILITY

46. FUTURE ENHANCEMENTS Include Different Fading Models for accurate MAI Prediction Different SS generations models can be used Concepts of Congestion and Priority Control can be taken into account Concept of Data as Packets of Bits can be introduced with BER as system performance criteria

48. REFERENCES JOURNALS:- M. E. Crovella and A. Bestavros, ”Self-similarity in World Wide Web traffic: Evidence and possible causes”, IEEE/ACM Transactions on Networking, Vol. 5, pp. 835–846, 1997. Jeong, H.-D., McNickle, D., and Pawlikowski, K., “ A Generator Of Pseudo- random Self-Similar Sequences Based on SRA.”, Tech. Rep. TRCOSC 9/98, New Zealand, 1998. Jeong, H.-D.J., McNickle, D., and Pawlikowski, K., “Fast Self-Similar Teletraffic Generation Based on FGN and Wavelets”, In Proceedings of IEEE International Conference on Networks (ICON'99), Brisbane, Australia, (In press), 1999.

49. Jeong, H.-D., McNickle, D., and Pawlikowski, K., “ Generation of Self-Similar Time Series for Simulation Studies of Telecommunication Networks.”, In Proceedings of the First Western Pacific and Third Australia-Japan Workshop on Stochastic Models in Engineering, Technology and Management, New Zealand, pp. 221-230, 1999. Jon M. Peha and Arak Sutivong, “Admission Control Algorithms for Cellular Systems”, ACM/Baltzer Wireless Networks, 1999. Junshan Zhang, Ming Hu, and Ness B. Shroff, “Bursty Data Over CDMA: MAI Self Similarity, Rate Control and Admission Control”, IEEE, 2002. Leland, W., Taqqu, M., Willinger, W., and Wilson, D., “On the Self-Similar Nature of Ethernet Traffic (Extended Version). IEEE/ACM Transactions on Networking, pp. 1-15, 1994. Mandelbrot, B., “ A Fast Fractional Gaussian Noise Generator.”, Water Resources Research 7,pp. 543-553, 1971.

50. W. Willinger, M. S. Taqqu, R. Sherman, and D. V. Wilson, “Self similarity through high-variability: Statistical analysis of Ethernet LAN traffic at the source level”, IEEE/ACM Transactions on Networking, Vol. 5, pp. 71–86,1997. J. Zhang and T. Konstantopoulos, “Self-similarity of multi-access interference processes in bursty data CDMA networks”, submitted to ISIT 2002. BOOK:- T. S. Rappaport, Wireless Communications: Principles and Practice. New Jersey: Prentice Hall, 1996.