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Opportunistic Scheduling in Wireless Networks

Opportunistic Scheduling in Wireless Networks. Mohammed Eltayeb Obaid Khattak. 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

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Opportunistic Scheduling in Wireless Networks

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  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.

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