scheduling considerations for multi-user mimo
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Scheduling Considerations for Multi-User MIMO. Sae-Young Chung Wireless Communications Lab KAIST 05/19/2005. Overview. Introduction Multi-user MIMO Dirty paper coding Optimal schedulers Summary. Small-Scale Fading. Channel Knowledge. Assume perfect CSI at Tx and at Rx - PowerPoint PPT PresentationTRANSCRIPT
05/19/2005 Wireless Communications Lab, KAIST 1
Scheduling Considerations for Multi-User MIMO
Sae-Young ChungWireless Communications Lab
KAIST05/19/2005
05/19/2005 Wireless Communications Lab, KAIST 2
Overview Introduction Multi-user MIMO Dirty paper coding Optimal schedulers Summary
05/19/2005 Wireless Communications Lab, KAIST 3
Small-Scale Fading
05/19/2005 Wireless Communications Lab, KAIST 4
Channel Knowledge Assume perfect CSI at Tx and at Rx
Requires CSI feedback from Rx to Tx Is it a realistic assumption?
If packet duration << coherence time E.g., 3km/h, 2GHz: ~ 30 msec Packet duration: ~> 1 msec in 3G
If packet duration >> coherence time Channel coding provides time diversity
CSI feedback consumes resource Worse for MIMO
Penalty due to time delay Estimation errors
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Single-User MIMO
Capacity increases as at high SNR Dimension-limited regime # of spatial dimensions = w.p. 1
Capacity increases as at low SNR Uses only one spatial dimension, i.e., beamforming Only the quality of the best spatial channel matters
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Multi-User MIMOBroadcast
Multiple access
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키스의 고유 조건은 입술끼리
만나야 되고 특별한 요령은 필요치 않다
Thequick
brownjumpsover
lazythe
dog
fox
Dirty Paper Coding
The quick brown fox
jumps over the lazy dog
The quick brown fox
jumps over the lazy dog
키스의 고유 조건은 입술끼리
만나야 되고 특별한 요령은 필요치 않다
The quick brown fox
jumps over the lazy dog
키스의 고유 조건은 입술끼리
만나야 되고 특별한 요령은 필요치 않다
Thequick
brownjumpsover
lazythe
dog
fox
05/19/2005 Wireless Communications Lab, KAIST 8
Dirty Paper Coding DPC: M. Costa ’83 DPC achieves capacity of Gaussian MIMO broadcast ch
annel H. Weingarten, Y. Steinberg, S. Shamai ’04
Practical schemes Interference cancelling at the transmitter
Erez, Shamai, Zamir ’00
But, complicated to implement More practical schemes are yet to be discovered
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Single Tx Antenna Channels become degraded
BC
DPC is equivalent to SIC Sum capacity is achievable
with TDM Other boundary points are not
achievable by TDM in general E.g., rates achieved by PF
scheduler
0 1 2 3 4 5 6 70
0.5
1
1.5
2
2.5
3
3.5
R1 [b/s/Hz]
R2 [
b/s
/Hz]
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Scheduling Gain Three sources of scheduling gain in wireless
Channel variation over time Channel variation over frequency Channel variation over space
Optimal scheduling Allocates dimensions and power optimally across time, frequency and
space Peak or average power constraints
Constant power allocation or optimal power allocation
over Time, frequency, and space
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Opportunistic Scheduling
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Optimal Scheduler Scheduler maximizes the following for each channel
state
It maximizes Therefore the following is on the boundary of the capacity region
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PF Scheduler achieves PF since for all T
implies for all T Therefore, PF scheduler should maximize
for each channel state, where is the measured throughput of user k
This generalizes Qualcomm’s PF scheduler H. Viswanathan, S. Venkatesan, H. Huang ’03
Equivalent to max sum-rate scheduler if channel statistics are the same for all users
DPC and PF scheduling can be combined
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Other Schedulers Maximize sum-rate: Fair (equal throughput):
Same as max min Max harmonic mean throughput
Circuit capacity
PF scheduler Sum-rate Fair scheduler
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Calculation of DPC Capacity Convert to a convex optimization by using dualit
y between BC and MAC S. Viswanath, N. Jindal, A. Goldsmith ’02
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DPC Capacity Example 4x1 (solid) or 4x2 (dashed)
MIMO 10 users Simultaneously scheduled
users: 1, 2, 3, or 4 (from bottom to up)
Plots sum capacity, i.e., scheduler maximizes sum throughput
-20 -10 0 10 20 300
5
10
15
20
25
30
35
40
SNR [dB]
R [
b/s
/Hz]
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Asymptotic Behavior Low SNR
Power limited, dimension irrelevant Picking one user is enough (i.e., TDM) Pick the best eigen mode for the chosen user
High SNR Dimension limited, power irrelevant Number of dimensions:
Number of users: Maximum number of users scheduled simultaneously:
Picking users is enough High # of users
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Current Research Areas at WCL Practical multi-user MIMO schemes
Beamforming Combined with LDPC codes Iterative decoding techniques Limited CSI feedback
Cross layer optimization Scheduler design OFDM Network information theory
Relay channels Interference channels Ad-hoc networks
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Summary MIMO can increase capacity Multi-user MIMO can increase capacity further Good practical schemes are desirable Optimal scheduling for multi-user MIMO Many research problems
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Thank You