Scheduling and Optimization

1
Scheduling and Optimization

• Criteria and Algorithms for Scheduling of Packet
  Data over Wireless Channels

Nilo Casimiro Ericsson, Signals Systems,
Uppsala University
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Outline

• Introduction, background
• Scheduling for spectral efficiency
• Latest scheduling insights
• No need for complex optimization
• Provide an average throughput
• Adaptive criteria simulation results
• Conclusion

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Packet data over fading channels
Avoid fading dips!
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Scheduling of OFDM bins

• Perform scheduling based on predicted average SNR
  in time-frequency bins
• ?
• For each bin let the best user transmit use
  adaptive modulation and ARQ

1
4
3
5
2
user
freq
time
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What the scheduler does
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Scheduling algorithms

• Simple linear maximization
• Best First
• Maximum Allocation
• Robin Hood
• Exact buffer-matching
• Controlled Steepest Descent
• Exhaustive search

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Complexity (25 bins)
two-step
one-step swap
one-step
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But, is the criterion right at all?

• Buffer content minimimization at each scheduling
  instant seems short-sighted
• Search algorithms allocate resources to match
  buffer content as exactly as possible
• Sum-of-squares criteria
• Uncertain predictions
• Academic interest, off course
• Instead Maintain a (constant?) average (over
  time) throughput for each active stream
• Based on maximized linear criteria
• If necessary re-allocations from
  over-provisioned streams

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Traffic adaptive criteria

• Previously in Robin Hood (Coarse adaptivity)
• Three features compared in some order
• Modulation, Priority, SNR
• If two have equal Modulation gt compare
  Priority, etc
• Can change order to (adaptation to traffic
  situation)Priority, Modulation, SNR
• New Quantize features into (e.g.)
• Modulation 3 bits m1,m2,m3
• Priority 2 bits p1,p2
• SNR 2 bits s1,s2 (explain!)
• The new feature m1,m2,m3,p1,p2,s1,s2
• But also m1,p1,p2,m2,m3,s1,s2

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Adaptive criteria example
3 bits for Modulation (0-7) 2 bits for Priority
(0-3) 0 bits for SNR (omitted)

1. mmmpp
2. mppmm

• User 1
• M 6 (64QAM), mmm 1102
• P 1 (medium low), pp 012
• A) mmmpp 110012 2510
• B) mppmm 101012 1910

User 2 M 5 (32QAM), mmm 1012 P 2 (medium
high),pp 102 A) mmmpp 101102 2210 B)
mppmm 110012 2510
gt
lt
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Simulation of scheduler

• 25 OFDM bins per schedule
• 5 MHz carrier _at_ 1900 MHz
• Time-frequency bin size 0.667 ms x 200 kHz
• 108 payload symbols per bin
• 12 users
• 8 modulation levels (3 bits)
• 0-7 (quiet-128QAM)
• SNR thresholds 6.5 10 14 18 22.5 26 30
  dB
• (why not 1-8?)
• 4 priority levels (2 bits)
• 0-3
• Random SNR for each user and bin
• 100 schedule simulations per criteria setup

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Simulation 1
12 users, 4 priorities 3 users of each
priority Same SNR distribution for all
N(10,10) Maximum modulation 7 (128QAM)
Throughput per user
Criteriammppm
Criteriammmpp
Criteriamppmm
Criteriappmmm
Criteriammpmp
Criteriampmpm
Criteriapmpmm
Criteriampmmp
Criteriapmmpm
Criteriapmmmp
(A)
(B)
Total throughput
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Simulation 2
12 users, 4 priorities 3 users of each
priority 4 different SNR distributions
N(15,12,9,6,5) Highest priority for worst SNR
Throughput per user
Criteriammppm
Criteriammmpp
Criteriamppmm
Criteriappmmm
Criteriammpmp
Criteriampmpm
Criteriapmpmm
Criteriampmmp
Criteriapmmpm
Criteriapmmmp
(A)
(B)
Total throughput
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Conclusion

• For practical scheduler abandon complex search
  algorithms
• Too many uncertainties (channel prediction,
  buffer usage)
• Scheduling can handle also distant users with
  worse conditions than near users
• Work with priorities
• Upgrade the importance of priority
• Probably, average throughput target will also
  help distant users
• Over-provisioned near users will give resources
  to under-provisioned distant users

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Simulation 3
12 users, 4 priorities 3 users of each
priority 3 different SNR distributions
N(5,10,15,5) Maximum modulation 7 (128QAM)
Throughput per user
Criteriammppm
Criteriammmpp
Criteriamppmm
Criteriappmmm
Criteriammpmp
Criteriampmpm
Criteriapmpmm
Criteriampmmp
Criteriapmmpm
Criteriapmmmp
(A)
(B)
Total throughput