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

Opportunistic Scheduling Algorithms for Wireless Networks

Vegard Hassel

CUBAN Seminar 22. April 2004

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

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.

What Is Opportunistic Scheduling?

Opportunistic: Scheduler tries to exploit channel conditions to achieve higher network performance

USER 1

USER 2

USER 3

SCHEDULER

BUFFERS

The base station serves as a scheduling agent

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

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?

Cross-Layer Design

TCP/IP-layers:• Application• Transport (TCP, UDP)• Internet (IP)• Network access (MAC, LLC)• Physical

Today: Some adaptation between neighbouring layersTomorrow: Network stack that take advantage of the interdependencies between the layers

Cross-Layer DesignVariations 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.

What Influences Scheduling?

Instantaneous channel conditions

QoS requirements

SCHEDULING

Delay Throughput

Fast fading Slow fading

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

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

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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

Algorithms That Only Consider The Channel Conditions

1. Proportional fair algorithm

2. Max SNR scheduling

3. Max SNR scheduling with a threshold

4. Opportunistic beamforming

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)

Proportional Fair Algorithm

Algorithm:

STEP 1:

At time t choose the user with the highest Ri(t)/ Ci(t):

STEP 2:Update average rate:

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Ci(t +1) = (1−1/ tC ) × Ci(t) +1/ tC × Ri(t) × I(i)€

i*(t) = argmax1≤ i≤N

Ri(t)

Ci(t)

⎛

⎝ ⎜

⎞

⎠ ⎟

Max SNR Scheduling

Proportional Fair algorithm with:• large values of tc • same for all users: Ri(t) ~ γi(t) and Ci(t) ~

The user with the largest SNR is chosenAlso called greedy algorithm Not fair if is different for different users€

i*(t) = argmax1≤ i≤N

(γ i(t))

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γ

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γ

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γ

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.€

i*(t) =argmax

1≤ i≤N(γ i(t)) if ∃ γ i(t) > γ th (t)

rand1≤ i≤N

(γ i(t)) if ∀ γ i(t) < γ th (t)

⎧ ⎨ ⎪

⎩ ⎪

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

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

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i*(t) = argmax1≤ i≤N

wi(t)Ri(t)

Algorithms That Consider QoS

Examples of QoS-requirements:

• Minimum delay requirement

• Minimum throughput requirement

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!

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i*(t) = argmax1≤ i≤N

ρ iW i(t)Ri(t){ }

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

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i*(t) = argmax1≤ i≤N

ρ iW i(t)Ri{ }

Lazy Scheduler

QuickTime™ og enTIFF (LZW)-dekomprimerer

kreves for å se dette bildet.

A scheduler that trades off delay for energy

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

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i*(t) = argmax1≤ i≤N

ρ i

W i(t)

Pi(t)Ri(t)

⎧ ⎨ ⎩

⎫ ⎬ ⎭

System Model

Buffering on both uplink and downlink

USER 1

USER 2

USER 3

SCHEDULER

BUFFERS

BUFFERS

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)

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 γ-threshold

• Max SNR scheduling with delayed CSI

• SNR scheduling with different

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γ

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)

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

Future Research

3) Other issues:

• Ad Hoc networks• Multi-hop networks• MAC and ARQ protocols• CAC: Connection Admission Control