Achieving High Data Rates
in a Distributed MIMO
System
Horia Vlad Balan
Ryan Rogalin
Antonios Michaloliakos Konstantinos Psounis
Giuseppe Caire
USC
Structure of this talk
•Motivation
•Multiuser MIMO and precoding
schemes
•Distributed MIMO and
synchronization
•Experimental results
Motivation
• Cellular companies
spend billions for more
bandwidth
• Spectrum reuse is the
most promising way to
increase wireless transfer
rates and distributed
MIMO is its ideal
implementation
• In WiFi networks, with a
high number of users,
spectrum reuse becomes
equally important
[Webb - The Future of Wireless Communication]
Enterprise WiFi
Multiuser MIMO
Shannon’s Theory
Increasing the Rate
Prelog Factor
Increase your
bandwidth!
Inlog Factor
Increase your
power
exponentially!!!
MIMO
Communication
Separate the
Channels
limited interference
Dirty Paper Coding
provides the achievable
rate region
Zero-Forcing
-1
Tomlinson-Harashima
Precoding
-1
L
L
LU
U
U
-1
Tomlinson-Harashima
3
3
-1
2
2
1
+2
-2
1
1
+2
+3
+4
14 (mod 5) =
4
-9 (mod 5) =
Modulo4
1
Compensation
-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9
Tomlinson-Harashima
Precoding
-1
-1
mod
(
mod
(
mod
(
L
L
U
U
U
-1
U
) mod
) mod
) mod
-1
Blind Interference
Alignment
+
+
3 slots, 4 symbols => 4/3
DoFs
+
+
+
+
+
+
+
+
+
+
+
+
Distributed MIMO
Challenges
• Maintaining phase synchronization between
the different APs
• Gathering channel state information and
transmitting before the channel coherence
time ends
OFDM Modulation
Subcarrier
s
Cyclic
Prefix
OFDM
Symbol
Carrier
OFDM Demodulation
I
F
F
T
FFT
Distributed OFDM
Symbol
Alignmen
t
TX 1
Phase
Alignmen
t
TX 2
RX
FFT
Distributed OFDM
TX 1
TX 2
Random
Phase
Timing
Offset
Carrier
Frequency
Offset
Phase Alignment
Phase Alignment
option 1
option 2: coherence time depends on the
electronics
What should be the effective channel
matrix?
Phase Alignment
option 1
What should be the effective channel
matrix?
Achieving Phase
Synchronization
Pilot
Signal
Maste
r
Secondari
es
Data
User
Distributed MIMO Testbed
Pilot
Signal
Master
Data
Secondaries
TDMA point-to-point
Clients
(4x4 MIMO)
Results
Phase
Accuracy
ZFB
F
(2x2 MIMO)
Channel
Orthogonalization
Results
Tomlinson
Harashima
85% rate
increase
(85% of
the theoretical
gain)
(2x2 MIMO)
Results
Tomlinson
Harashima
165% rate
increase
(55% of
the theoretical
gain)
(4x4 MIMO)
Results
Blind Interference
Alignment
22% rate
increase
(66% of the theoretical
gain)
MAC Layer Results
• Comparing scheduling strategies through
simulation in a 4 AP, 8 users scenario
• Greedy Zero-Forcing, Tomlinson-Harashima
precoding, Blind Interference Alignment
• Using TDMA as a reference point
Results
4x4 achievable rates
(simulation)
Future Work
• improving the accuracy of our estimators
• combining distributed MIMO with incremental
redundancy schemes
• characterize the channel quality variations of
BIA in large deployments
Questions
?
Thank you!