Opportunistic Scheduling in Wireless Networks

1. Opportunistic Scheduling in Wireless Networks Mohammed Eltayeb Obaid Khattak

2. Project Outline • This report gives an overview of different scheduling algorithms, from the simple round robin algorithm, to opportunistic scheduling algorithms considering QoS, with simulation of system • capacity • feedback load and • fairness. • We divided the algorithms into fair, semi-fair and greedy algorithms. • All simulations are done with Matlab 7.0 with an average SNR of 15dB and 1000 Ts for 30 users.

3. Back Ground Theory • A scheduling system is implemented both in the mobile station (MS) and in the base station (BS). • The BS uses a TDMA scheme and during one time slot, only one user can receive or transmit, and this user is selected by the scheduler.

4. Fair Algorithms • Round Robin • The RR scheduler is the simplest scheduling algorithm, and it is not opportunistic. • When a user connects to the base station (BS), it is given a position in the queue of users, and the scheduler will iterate through the queue.

5. Fair Algorithms - RR

6. Fair Algorithms - RR

7. Fair Algorithms • Opportunistic Round Robin (ORR) • The ORR algorithm is a Round Robin scheduler. • Channel conditions are taken into account. • The scheduler iterates the list of users, and every time the best user is selected and removed from the list.

8. Fair Algorithm - ORR

9. Fair Algorithm - ORR

10. SEMI-FAIR SCHEDULING ALGORITHMS EXAMPLES AND PERFORMANCE

11. Semi-Fairness • Middle ground between Fair & Greedy • Provide Fairness in terms of scheduling outage • Feedback load not zero but not rate optimal either Example: Switched Diversity Scheduling (SDS)

12. SDS • Family of algorithms based on multi-antenna systems schemes • Specific Threshold γth is set • Scans users to find CNR > γth • If user found, selected • At each time slot, sequence may be randomized or organized in special way • Examples • Selection Combining Transmission (SCT) • SET with Post-Selection (SETps)

13. SCT • Checks ALL users, selects user with highest CNR • Fair if all users are i.i.d • Advantage • Only form of SDS which is rate optimal • Disadvantage • Normalized feedback load (NFL) unity

14. MASSE Performance of SCT

15. Throughput Fairness in SCT

16. SETps • Extension of Switch-and-Examine Transmission (SET) • First scanned user with CNR > γth selected • If no user CNR > γth User with greatest CNR selected • Combats scheduling outage • At each time slot, list randomized • Provides level of fairness

17. MASSE of SETps

18. Throughput Fairness of SETps

19. Time-slot Fairness of SETps

20. NFL of SETps

21. GREEDY SCHEDULING ALGORITHMS EXAMPLES AND PERFORMANCE

22. Greedy Algorithms • More concerned with maximizing system throughput, not fairness to individual users • Do provide fairness when all users have i.i.d. channel conditions • Rate optimal, MASSE values equal • Examples • Maximum CNR Scheduling (MCS) • Optimal Rate, Reduced Feedback (ORRF)

23. MCS • All users report their CNR to BS • User with best channel selected • Rate optimal • Large overhead in reporting CNR values • Normalized feedback load (NFL) unity • Poor throughput and time-slot fairness • Same as SCT

24. MASSE of optimal schedulers

25. Optimal Rate, Reduced Feedback (ORRF) • Scheduler decides threshold CNR • Distributed to all users • Users with CNR > Threshold reply • Best user selected • If no user replies • Scheduler requests full feedback • Every user returns CSI (Channel State Information) • After full feedback or without it, best user selected

26. NFL of ORRF

27. Time-slot Fairness of ORRF

28. Throughput Fairness

29. MASSE-based Comparison

30. NFL-based Comparison

31. References • [1] P. Viswanath, D. N. C. Tse, and R. Laroia, _Opportunistic beamforming • using dumb antennas,_ IEEE Trans. Inform. Theory, vol. 48, pp. 1277_ • 1294, June 2002. • [2] A. J. Goldsmith and P. P. Varaiya, _Capacity of fading channels with channel • side information,_ IEEE Trans. Inform. Theory, vol. IT-43, pp. 1896_ • 1992, Nov. 1997. • [3] D. Gesbert and M.-S. Alouini, _How much feedback is multi-user diversity • really worth?,_ in IEEE Int. Conf. on Communications (ICC'04), (Paris, • France), pp. 234_238, June 2004. • [4] V. Hassel, M.-S. Alouini, G. E. Øien, and D. Gesbert, _Rate-optimal multiuser • scheduling with reduced feedback load and analysis of delay effects._ • Submitted to IEEE Int. Conf. on Comm. (ICC'05), (Seoul, South Korea), • May 2005. • [5] M. Johansson, _Issues in multiuser diversity._ • http://www.signal.uu.se/Research/PCCWIP/Visbyrefs/Johansson_Visby04.pdf. • Presentation at WIP/BEATS/CUBAN workshop Wisby, Sweden, Aug. • 2004. • [6] R. Knopp and P. A. Humblet, _Information capacity and power control in • single cell multiuser communications,_ in IEEE Int. Conf. on Communications • (ICC'95), (Seattle, WA), pp. 331_335, June 1995. • [7] B. Holter, M.-S. Alouini, G. E. Øien, and H.-C. Yang, _Multiuser switched • diversity transmission._ Accepted for IEEE Veh. Tech. Conf. (VTC'04- • spring), (Los Angeles, CA), Sept. 2004.