1. Scheduling Considerations for Multi-User MIMO
Sae-Young Chung
Wireless Communications Lab
KAIST
05/19/2005
05/19/2005
Wireless Communications Lab, KAIST

2. Wireless Communications Lab, KAIST
Overview
Introduction
Multi-user MIMO
Dirty paper coding
Optimal schedulers
Summary
05/19/2005
Wireless Communications Lab, KAIST

3. Wireless Communications Lab, KAIST
Small-Scale Fading
05/19/2005
Wireless Communications Lab, KAIST

4. Wireless Communications Lab, KAIST
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
05/19/2005
Wireless Communications Lab, KAIST

5. Wireless Communications Lab, KAIST
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
05/19/2005
Wireless Communications Lab, KAIST

6. Wireless Communications Lab, KAIST
Multi-User MIMO
Broadcast
Multiple access
05/19/2005
Wireless Communications Lab, KAIST

7. Dirty Paper Coding The quick brown fox jumps over the lazy dog
키스의 고유 조건은 입술끼리 만나야 되고 특별한 요령은 필요치 않다
The
quick
brown
jumps
over
lazy
the
dog
fox
키스의 고유 조건은 입술끼리 만나야 되고 특별한 요령은 필요치 않다
The
quick
brown
jumps
over
lazy
the
dog
fox
키스의 고유 조건은 입술끼리 만나야 되고 특별한 요령은 필요치 않다
05/19/2005
Wireless Communications Lab, KAIST

8. Wireless Communications Lab, KAIST
Dirty Paper Coding
DPC: M. Costa ’83
DPC achieves capacity of Gaussian MIMO broadcast channel
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
05/19/2005
Wireless Communications Lab, KAIST

9. Wireless Communications Lab, KAIST
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
05/19/2005
Wireless Communications Lab, KAIST

10. Wireless Communications Lab, KAIST
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
05/19/2005
Wireless Communications Lab, KAIST

11. Opportunistic Scheduling
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Wireless Communications Lab, KAIST

12. Wireless Communications Lab, KAIST
Optimal Scheduler
Scheduler maximizes the following for each channel state
It maximizes
Therefore the following is on the boundary of the capacity region
05/19/2005
Wireless Communications Lab, KAIST

13. Wireless Communications Lab, KAIST
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
05/19/2005
Wireless Communications Lab, KAIST

14. Wireless Communications Lab, KAIST
Other Schedulers
Maximize sum-rate:
Fair (equal throughput):
Same as max min
Max harmonic mean throughput
Circuit capacity
PF scheduler Sum-rate Fair scheduler
05/19/2005
Wireless Communications Lab, KAIST

15. Calculation of DPC Capacity
Convert to a convex optimization by using duality between BC and MAC
S. Viswanath, N. Jindal, A. Goldsmith ’02
05/19/2005
Wireless Communications Lab, KAIST

16. Wireless Communications Lab, KAIST
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
05/19/2005
Wireless Communications Lab, KAIST

17. Wireless Communications Lab, KAIST
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
05/19/2005
Wireless Communications Lab, KAIST

18. 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
05/19/2005
Wireless Communications Lab, KAIST

19. Wireless Communications Lab, KAIST
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
05/19/2005
Wireless Communications Lab, KAIST

20. Wireless Communications Lab, KAIST
Thank You
05/19/2005
Wireless Communications Lab, KAIST