Transcript Multi-user scheduling in HSDPA systems

TKK HELSINKI UNIVERSITY OF TECHNOLOGY
Euro-NF JRA 2.3 meeting, 2.10.2008
Multi-user scheduling in
HSDPA systems
Samuli Aalto and Pasi Lassila
Department of Communications and Networking
TKK Helsinki University of Technology
Email: {Samuli.Aalto, Pasi.Lassila}@tkk.fi
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TKK HELSINKI UNIVERSITY OF TECHNOLOGY
Euro-NF JRA 2.3 meeting, 2.10.2008
HSDPA systems
– Downlink scheduling
• BS decides allocation
of radio resources for
different users’ traffic
– Radio resource management
• In HSDPA, resources = orthogonal codes
• Each user terminal has a ”category”
• Category defines the processing power limitation of the terminal
Scheduling:
– Based on user’s channel quality and terminal category a coding
scheme and number of codes is used which determines the ”bit rate”
– Scheduler should use all resources (i.e., schedule multiple users)
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TKK HELSINKI UNIVERSITY OF TECHNOLOGY
Euro-NF JRA 2.3 meeting, 2.10.2008
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TKK HELSINKI UNIVERSITY OF TECHNOLOGY
Euro-NF JRA 2.3 meeting, 2.10.2008
Flow-level model (1)
– Elastic flows with random sizes with a general distribution, Poisson
arrivals with rate l
• At time t there are N(t) flows, each flow is indexed by n
– We do not consider fast fading
• Flows only see average channel behavior
• Flows have different channel’s due to, e.g., distance to the base station
– Codes correspond to servers
• Number of servers denoted by K and servers indexed by k
• The service rate of each server k is user dependent, denoted by rnk
– e.g., rate attenuates with distance dn, rnk ~ Min{1,(d0/dn)a}
• Aggregate rate is linear in number of codes (orthogonality)
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TKK HELSINKI UNIVERSITY OF TECHNOLOGY
Euro-NF JRA 2.3 meeting, 2.10.2008
Flow-level model (2)
– Terminal category
• Associated with each flow n is terminal category cn telling the number of
codes
– Due to terminal category constraints multiple flows need to be
scheduled simultaneously (HSDPA systems)
• Earlier we assumed all codes are given to exactly one user (old CDMA
1xEV-DO systems)
• Multiple servers can serve one flow
– classical multiserver problem assumes one server per queue (flow)
• Servers are heterogeneous (service rate depends on flow)
• Again, size-based information is used to select flows intelligently
– Same problem formulation applies to OFDMA systems
• Carriers correspond to codes
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TKK HELSINKI UNIVERSITY OF TECHNOLOGY
Euro-NF JRA 2.3 meeting, 2.10.2008
Problem reduction
– Idea: First try to simplify the problem to the simplest possible system
amenable to analysis
• Gives insight for analyzing more complex scenarios
– Assumptions
• All flows have identical channels (symmetric situation)
• All flows have the same terminal category so that K/2 codes can be
allocated per user
– Corresponds to an M/G/2 system (with homogenous servers)
• Even for this system the optimal scheduling rule is not known (for
minimizing mean flow delay)
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TKK HELSINKI UNIVERSITY OF TECHNOLOGY
Euro-NF JRA 2.3 meeting, 2.10.2008
Collection of useful results (found so far…)
– In a static setting with a fixed number of flows SRPT is optimal1
• Applies even with heterogeneous servers
• Assumes one server / flow
– In the dynamic setting
• “long jobs are stuck at the end of the busy period”2
– Gain from (size-based) scheduling
• Impact greatest for M/G/1 queue
• For M/G/n, as n increases, scheduling has less and less impact
• In an M/G/∞ queue scheduling does not affect performance
1
2
Pinedo (1995)
Wierman (2007)
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Euro-NF JRA 2.3 meeting, 2.10.2008
TKK HELSINKI UNIVERSITY OF TECHNOLOGY
Some tests for M/G/2 (relative to PS)
Erlang flow sizes
Pareto flow sizes
1.2
SRPT/LRPT
Mean delay relative to PS
Mean delay relative to PS
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1.0
0.8
FCFS
SRPT
SRPT*
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1.0
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SRPT
SRPT*
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0.4
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0.5
SRPT/LRPT
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0.5
0.9
0.6
Exponential flow sizes
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1.2
FCFS
1.0
Mean delay relative to PS
Mean delay relative to PS
0.8
SRPT
1.2
SRPT/LRPT
0.8
SRPT
SRPT*
0.6
0.4
0.5
0.7
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0.7
0.8
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1.0
0.8
Det
0.6
Erl
0.4
Pareto
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0.8
Exp
0.9
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TKK HELSINKI UNIVERSITY OF TECHNOLOGY
Euro-NF JRA 2.3 meeting, 2.10.2008
Two forthcoming ACM MSWiM 2008 papers
– Pasi Lassila and Samuli Aalto
”Combining opportunistic and size-based scheduling in wireless
systems”
• studying how to optimally combine channel-aware and size-based
scheduling of elastic flows in HSPDA/HDR type systems
• channel-awareness exploits variations in the quality of the radio channel
• size-based schduling gets rid of flows as soon as possible
– Jarno Nousiainen, Jorma Virtamo and Pasi Lassila
”Forwarding capacity of an infinite wireless network”
• studying the maximal forwarding capacity of a massively dense wireless
multihop network
• separation of micro (single hop) and macroscopic (end-to-end path) levels
• formulation of the forwarding problem and development of simulation
algorithms for obtaing upper bounds
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