Breaking the Interference Barrier

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Breaking the Interference Barrier

• David Tse
• Wireless Foundations
• University of California at Berkeley
• Mobicom/Mobihoc Plenary Talk
• September 13, 2007

TexPoint fonts used in EMF AAAA
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The Interference Barrier

• Lots of recent advances in physical layer
  wireless communication (multiple antennas MIMO,
  space-time codes, opportunistic scheduling,
  turbo codes, hybrid ARQ.)
• From theory to practice in a decade.
• Gains pertain mainly to point-to-point or
  multiple access performance.
• But performance of many wireless systems
  ultimately limited by interference.
• Breaking this interference barrier will be the
  next step.

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Examples of Interference Barrier

• Cellular networks inter-cell interference
• Ad hoc networks interference from simultaneous
  transmissions
• Wireless LANS interference between adjacent
  networks
• Cognitive networks interference between primary
  and secondary users and between multiple
  secondary systems

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Talk Outline

• We discuss several speculative approaches to
  break the interference barrier
• cooperative distributed MIMO
• exploiting mobility to localize interference
• interference alignment
• Key message
• Solving the interference problem requires a
  combination of physical layer and architectural
  ideas.

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Traditional Interference Management in Cellular
Systems

• Narrowband (eg. GSM)
• Inter-cell interference made negligible at the
  price of poor frequency reuse
• Wideband (eg. CDMA, OFDM)
• Universal frequency reuse but system is
  interference-limited.

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Example WiMax is Interference-Limited
SIR 2 dB
SNR 20 dB
Universal Reuse 6 dominant interferers
SIR to SNR gap 18dB
Source Intel WiMax simulations
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Fractional Reuse A Partial Solution

• Universal reuse for cell-interior users.
• Orthogonal bands for cell-edge users.
• But cell-edge users are still the bottleneck.

f2
f3
f0
f0
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Tale of Two Cell-Edge Users

• keep users on orthogonal bands lose half the
  effective bandwidth but avoid interference
• Best of both worlds?
• Yes, base-stations can cooperate to form a
  distributed MIMO array.

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MIMO in One Slide
Signal space at Rx array (M2)
direction of signal from Tx antenna 1
M by M MIMO system with a sufficiently random
channel supports M simultaneous data streams.
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Infrastructure Cooperation

• Base stations cooperate to form a macro-array to
  jointly decode in the uplink and transmit in the
  downlink.
• Turns harmful inter-cell interference into useful
  signals
• High-speed connectivity to a central processing
  unit.

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Simulation in a Hexagonal Cellular System
cooperation
single-cell processing
(Alessandro et al 06)
Rise-over-thermal 6dB 2 Rx antennas per BS
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Cooperation in Ad Hoc Networks

• Capacity of ad hoc networks limited by mutual
  interference between simultaneous transmissions.
• How can cooperation between mobiles improve
  capacity?
• Unlike infrastructure-based cellular systems,
  such cooperation comes at an over-the-air
  transmission cost.
• Will the overhead swamp the cooperation gain?

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Scaling Law Formulation

• (Gupta-Kumar 00)
• n nodes randomly located in a fixed area.
• n randomly assigned source-destination pairs.
• Each S-D pair demands the same data rate.
• How does the total throughput T(n) of the network
  scale with n?

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How much can Cooperation Help?
Courtesy David Reed
?
Can we get linear scaling with more sophisticated
cooperation?
Arbitrarily closely. (Ozgur,Leveque,T. 06)
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Gupta-Kumar Capacity is Interference-Limited

• Long-range transmission causes too much
  interference.
• Multi-hop means each packet is transmitted many
  times.
• To get linear scaling, must be able to do many
  simultaneous long-range transmissions.
• How to deal with interference?
• A natural idea distributed MIMO!
• But cooperation overhead is bottleneck.
• What kind of cooperation architecture minimizes
  overhead?

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A 3-Phase Scheme

• Divide the network into clusters of size M nodes.
• Focus first on a specific S-D pair.
• source s wants to send M bits to destination d.

Phase 1 Setting up Tx cooperation 1 bit to
each node in Tx cluster
Phase 2 Long-range MIMO between s and d
clusters.
Phase 3 Each node in Rx cluster quantizes signal
into k bits and sends to destination d.
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Parallelization across S-D Pairs
Phase 1 Clusters work in parallel. Sources in
each cluster take turn distributing their
bits. Total time M2
Phase 2 1 MIMO trans. at a time. Total time n
Phase 3 Clusters work in parallel. Destinations
in each cluster take turn collecting their
bits. Total time kM2
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Back-of-the-Envelope Throughput Calculation

• total number of bits transferred nM
• total time in all three phases M2 n kM2
• Throughput
  bits/second
• Optimal cluster size
• Best throughput
  ?

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Further Parallelization

• In phase 1 and 3, M2 bits have to be exchanged
  within each cluster, 1 bit per node pair.
• Previous scheme exchanges these bits one at a
  time (TDMA), takes time M2.
• Can we increase the spatial reuse ?
• Can break the problem into M sessions, each
  session involving M S-D pairs communicating 1 bit
  with each other
• cooperation communication
• Any better scheme for the small network can build
  a better scheme for the original network.

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Recursion

• Lemma A scheme with thruput Mb for the smaller
  network yields for the original network a
  thruput

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MIMO Hierarchical Cooperation-gt Linear Scaling
Long-range MIMO
Setting up Tx cooperation
Cooperate to decode

• .

By having many levels of hierarchy, we can get as
close to linear scaling as we wish.
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Linear Scaling with Less Work?

• Linear scaling means that the capacity of the
  network is not significantly limited by
  interference.
• But the hierarchical scheme requires tracking of
  channel information as well as significant
  cooperation between nodes.
• Can one get linear scaling with less work?
• Yes, if nodes are mobile.

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Mobility Can Help!

• (Grossglauser and T. 01)
• Suppose nodes move randomly and independently.
• A linear throughput can be achieved
• if one is willing to wait.
• Throughput is averaged over the time-scale of
  mobility.

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Direct Communication Does Not Work

• The source and destination are nearest neighbors
  only O(1/n) of the time.

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Detour Multiuser Diversity in Cellular Systems
By opportunistically scheduling transmissions to
users with instantaneously strong channels,
multiuser diversity gain is achieved.
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Multiuser Diversity via Relaying

• Multiuser diversity created artificially using
  all other nodes as relays.

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Phase I Source to Relays

• At each time slot, source relays a packet to
  nearest neighbor.
• Different packets are distributed to different
  relay nodes.

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Phase 2 Relays to Destination

• Steady state all nodes have packets destined for
  D.
• Each relay node forwards packets to D only when
  it gets close.

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Phase I and II Staggered

• O(1) throughput from S to D
• Communication is confined to nearest neighbors,
  but each packet goes through at most two hops
• Load is distributed evenly between all relay
  nodes, enabling every S-D pair to follow the same
  strategy.

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Linear Scaling without Cooperation?

• The two approaches rely on some sort of
  cooperation to mitigate interference.
• Is cooperation really necessary?

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Spectrum Sharing Revisited

• Working assumption
• only one transmission on each
• time-frequency-space resource.
• Implicit assumption
• spectrum is a common ether shared by all.
• But is this metaphor correct?

Rx 1
Tx 1
Channel11
Channel21
Tx 2
Rx 2


Channel2n
Tx n
Rx n
Channelnn
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Interference Alignment Example

• (Cadambe-Jafar 07)
• All direct channels delay transmission by 1
  symbol time.
• All cross channels delay by 2 symbol times.
• Each user can transmit every other symbol time,
  yet no interference!
• What matters is what happens at the receiver, and
  each receiver sees a different picture.
• So all the interference can be aligned onto one
  symbol time and yet the signal is orthogonal to
  the interference.

Rx 1
Tx 1
Channel11
Channel12
Tx 2
Rx 2


Channel2n
Tx n
Rx n
Channelnn
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Interference Alignment Geometry
Tx 1
Rx 1
H11
Rx 2
Tx 2
Rx 3
Tx 3
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Recurring Theme

• Channel diversity is a key resource for breaking
  the interference barrier.
• The three approaches can be viewed as ways to
  exploit this diversity
• Hierarchical cooperation to exploit MIMO gain.
• Mobility and relaying to exploit multiuser
  diversity gain.
• Interference alignment to exploit diversity
  between direct and cross channels.

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Conclusions

• Breaking the interference barrier is the next
  step in the evolution of wireless systems.
• We focus on speculative ideas in this talk.
• Hopefully they provide some food for thought for
  system builders.