Opportunistic Scheduling Algorithms for Wireless Networks

1. Opportunistic Scheduling Algorithms for Wireless Networks Vegard Hassel CUBAN Seminar 22. April 2004

2. Agenda • What is scheduling/opportunistic scheduling? • Cross-layer design • Different types of channel models • Fairness • Algorithms that only consider the channel conditions • Algorithms that consider QoS • Algorithms that consider power consumption • Current & future research

3. What Is Scheduling? Scheduling policy: -a rule that specifies which user is allowed to transmit and which user is allowed to receive at each timeslot Uplink (user transmits) and downlink (user receives) at different frequencies.

4. What Is Opportunistic Scheduling? Opportunistic: Scheduler tries to exploit channel conditions to achieve higher network performance SCHEDULER USER 1 USER 2 BUFFERS USER 3 The base station serves as a scheduling agent

5. Qualcomm Example (1) • The channel conditions for each user is independent • The channel is GOOD 50% of the time and BAD 50% of the time • Two users with bitrates: • User 1: 200Mbit/s or 400Mbit/s • User 2: 400Mbit/s or 800Mbit/s • The users cannot be active at the same time • Round robin algorithm without opportunistic scheduling: • R1=0.5*(0.5*200Mbit/s+0.5*400Mbit/s)=150Mbit/s • R2=0.5*(0.5*400Mbit/s+0.5*800Mbit/s)=300Mbit/s

6. Qualcomm Example (2) • With opportunistic scheduling, the user that has the GOOD channel condition is chosen: • 25% of the time both users have BAD channel • 50% of the time one user has GOOD channel • 25% of the time both users have GOOD channel • The user with the relatively best channel is chosen • Round robin with opportunistic scheduling: • R1=0.5*(0.25*200Mbit/s+0.75*400Mbit/s)=175Mbit/s • R2=0.5*(0.25*400Mbit/s+0.75*800Mbit/s)=350Mbit/s • Opportunistic scheduling gives a 17% capacity gain But what if both users need 200Mbit/s?

7. Cross-Layer Design TCP/IP-layers: • Application • Transport (TCP, UDP) • Internet (IP) • Network access (MAC, LLC) • Physical Today: Some adaptation between neighbouring layers Tomorrow: Network stack that take advantage of the interdependencies between the layers

8. Cross-Layer Design Variations follow different time scales: • SNR variations ~ microseconds • Congestion of packets ~ seconds? • Cumulative user traffic ~ 10-100 seconds Goldsmith: Because of the different timescales, adaptation between layers is reasonable only if problems cannot be fixed locally within a layer.

9. What Influences Scheduling? QoS requirements Delay Throughput SCHEDULING Fast fading Slow fading Instantaneous channel conditions

10. Restrictions From Other Layers Physical layer: • Adaptive coding and modulation (MQAM) • TDMA or CDMA • TDMA: The channel should be constant within a time-slot Higher layers: • Throughput requirements • Delay requirements

11. Channel Models • Two-state Markov: GOOD/BAD • Slow fading: log-normal distribution • Fast fading: • Rice (LOS) • Rayleigh (LOS blocked) • Nakagami (general) • The average SNR, , for the fast fading models is influenced by slow fading • Slow scheduling: parameters changes slowly • Fast scheduling: parameters changes fast

12. Fairness • Choosing the best user with regard to the channel can lead to starvation of some users • Fair algorithms assign a guaranteed time or throughput to the users (Robin Hood) • Fairness not so important in a fast fading environment

13. Algorithms That Only Consider The Channel Conditions • Proportional fair algorithm • Max SNR scheduling • Max SNR scheduling with a threshold • Opportunistic beamforming

14. Proportional Fair Algorithm • Proportional fair if: • increasing the current throughput by x% for one user leads to a cumulative throughput decrease for the other users of more than x% • Maximises the product of the throughputs • The user with the relatively best channel is chosen • Starvation is avoided • Used by Qualcomm/HDR (IS-856)

15. Proportional Fair Algorithm Algorithm: STEP 1: At time t choose the user with the highest Ri(t)/ Ci(t): STEP 2: Update average rate:

16. Max SNR Scheduling Proportional Fair algorithm with: • large values of tc • same for all users: Ri(t) ~ i(t)andCi(t) ~ • The user with the largest SNR is chosen • Also called greedy algorithm • Not fair if is different for different users

17. Max SNR Scheduling With Threshold The user with the largest SNR above a threshold is chosen: Reduces the amount of feedback from the users. Channel state information is only fed back if SNR is above the threshold.

18. Opportunistic Beamforming Induce channel fluctuations in a slow fading environment: • MS’s with multiple antennas • Antennas are fed with random phase and amplitude • The overall induced SNR of a user is fed back to the BS • The BS schedules proportionally fair according to the different SNR values

19. Algorithms That Consider QoS • FUNDAMENT: Revenue-based algorithm • This algorithm maximizes the throughput with regard to QoS requirements: wi(t): weight assigned to a user to include the QoS requirements

20. Algorithms That Consider QoS Examples of QoS-requirements: • Minimum delay requirement • Minimum throughput requirement

21. M-LWDF Modified Largest Weighted Delay First i: constant for controlling delay distributions Wi(t): head-of-the-line packet delays This simple algorithm is throughput optimal!

22. M-LWDF: Throughput Guarantees Guarantees minimum throughput Ri: Ri: constant token arrival rate in bucket i Wi(t): delay of the longest delay token in bucket i: constant for controlling time-scale on which throughput guarantees are provided

23. Lazy Scheduler A scheduler that trades off delay for energy

24. M-LWDF: Delay vs. Power? i: constant for controlling trade-off between power and delay Wi(t): head-of-the-line packet delays Pi(t): power that has to be provided to user i

33. System Model Buffering on both uplink and downlink SCHEDULER USER 1 USER 2 BUFFERS USER 3 BUFFERS

34. Current Research Have investigated buffering between wired and wireless (Rayleigh) networks using optimal SNR scheduling. New expression for overflow probability when the rate into the memoryless buffer is constant. The corresponding expression has been found for a queue with Poisson distributed traffic from the wired network (M/G/1-queue)

35. What Will I Do Next? • Investigate max SNR Scheduling with both power and rate adaptation for M-QAM • Investigate the effects of Dopplerspread, coherence time and avg fade duration • Spectral efficiency and BER for a scheduling algorithm using a -threshold • Max SNR scheduling with delayed CSI • SNR scheduling with different

36. Future Research 1) Physical layer issues: • Optimising adaptive coding/modulation to the scheduling algorithm in use • Scheduling for slow fading channels (fairness!) • Evaluating channel information inaccuracy • Analysing interference/multicell issues • Combining MIMO with scheduling • Combining OFDM with scheduling • Energy efficient scheduling (with Sébastien)

37. Future Research 2) QoS issues: Must look at algorithms that: • Provide QoS differentiation between users • Maximise the number of users that can be supported with the desired QoS • Provide minimum flow guarantees? • Gives minimum buffer overflow

38. Future Research 3) Other issues: • Ad Hoc networks • Multi-hop networks • MAC and ARQ protocols • CAC: Connection Admission Control