Future Directions in Communications Research

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Future Directions in Communications Research

• Andrea Goldsmith
• Stanford University

IEEE Communication Theory Workshop Maui, HI May
14, 2012
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Future Wireless Networks
Ubiquitous Communication Among People and Devices
Next-generation Cellular Wireless Internet
Access Wireless Multimedia Sensor Networks Smart
Homes/Spaces Automated Highways Smart
Grid Body-Area Networks All this and more
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Future Cell Phones
Everything wireless in one device
Burden for this performance is on the backbone
network
Much better performance and reliability than today
- Gbps rates, low latency, 99 coverage indoors
and out
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Careful what you wish for
Exponential Mobile Data Growth
Leading to massive spectrum deficit
Source FCC
Source Unstrung Pyramid Research 2010
Growth in mobile data, massive spectrum deficit
and stagnant revenues require technical and
political breakthroughs for ongoing success of
cellular
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• Can we increase cellular system capacity to
  compensate for a 300MHz spectrum deficit?

Without increasing cost?
or power consumption?
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Are we at the Shannon limit of the Physical
Layer?
We dont know the Shannon capacity of most
wireless channels

• Time-varying channels with memory/feedback.

• Channels with interference or relays.

• Uplink and downlink channels with frequency
  reuse, i.e. cellular systems.

• Channels with delay/energy/ constraints.

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Rethinking Cells in Cellular
How should cellular systems be designed?
Will gains in practice be big or incremental
in capacity or coverage?

• Traditional cellular design interference-limited
  
• MIMO/multiuser detection can remove interference
• Cooperating BSs form a MIMO array what is a
  cell?
• Relays change cell shape and boundaries
• Distributed antennas move BS towards cell
  boundary
• Small cells create a cell within a cell
• Mobile cooperation via relaying, virtual MIMO,
  analog network coding.

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Are small cells the solution to increase cellular
system capacity?

• Yes, with reuse one and adaptive techniques
  (Alouini/Goldsmith 1999)

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The Future Cellular Network Hierarchical
Architecture

• Todays architecture
• 3M Macrocells serving 5 billion users
• Anticipated

MACRO solving initial coverage issue, existing
network
PICO solving street, enterprise home
coverage/capacity issue
FEMTO solving enterprise home
coverage/capacity issue
Macrocell
Picocell
Femtocell
Future systems require Self-Organization (SON)
(and WiFi Offload)
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SON Premise and Architecture
Mobile Gateway Or Cloud
SoNServer
IP Network
X2
X2
X2
X2
Small cell BS
Macrocell BS
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Algorithmic Challenge Complexity

• Optimal channel allocation was NP hard in
  2nd-generation (voice) IS-54 systems
• Now we have MIMO, multiple frequency bands,
  hierarchical networks,
• But convex optimization has advanced a lot in the
  last 20 years

Innovation needed to tame the complexity
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Green Cellular Networks
How should cellular systems be redesigned for
minimum energy?
Research indicates that significant savings is
possible

• Minimize energy at both the mobile and base
  station via
• New Infrastuctures cell size, BS placement, DAS,
  Picos, relays
• New Protocols Cell Zooming, Coop MIMO, RRM,
  Scheduling, Sleeping, Relaying
• Low-Power (Green) Radios Radio Architectures,
  Modulation, coding, MIMO

Gerhard Fettweis talk
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Antenna Placement in DAS

• Optimize distributed BS antenna location
• Primal/dual optimization framework
• Convex standard solutions apply
• For 4 ports, one moves to the center
• Up to 23 dB power gain in downlink
• Gain higher when CSIT not available

6 Ports
3 Ports
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Coding for minimum total power
Is Shannon-capacity still a good metric for
system design?
Computational Nodes
On-chip interconnects
Extends early work of El Gamal et. al.84 and
Thompson80
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Fundamental area-time-performance tradeoffs

• For encoding/decoding good codes,
• Stay away from capacity!
• Close to capacity
• Large chip-area
• More time
• More power

Encoding/decoding clock cycles
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Reduced-Dimension Communication System Design

• Compressed sensing ideas have found widespread
  application in signal processing and other areas.
• Basic premise of CS exploit sparsity to
  approximate a high-dimensional system/signal in a
  few dimensions.
• We ask how can sparsity be exploited to reduce
  the complexity of communication system design

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Sparsity where art thou?
To exploit sparsity, we need to find
communication systems where it exists

• Sparse signals e.g. white-space detection

• Sparse samples e.g. sub-Nyquist sampling

• Sparse users e.g. reduced-dimension multiuser
  detection

• Sparse state space e.g reduced-dimension network
  control

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Compressed Sensing

• Basic premise is that signals with some sparse
  structure can be sampled below their Nyquist rate
• Signal can be perfectly reconstructed from these
  samples by exploiting signal sparsity
• This significantly reduces the burden on the
  front-end A/D converter, as well as the DSP and
  storage
• Enabler for white space, SD and low-energy
  radios?
• Only for incoming signals sparse in time,
  freq., space, etc.

Rob Calderbanks Talk
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Software-Defined (SD) Radio
A/D
DSP

• Wideband antennas and A/Ds span BW of desired
  signals
• DSP programmed to process desired signal no
  specialized HW

Today, this is not cost, size, or power efficient
Compressed sensing reduces A/D and DSP burden
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Sparse Samples
Sampling Mechanism (rate fs)

• For a given sampling mechanism (i.e. a new
  channel)
• What is the optimal input signal?
• What is the tradeoff between capacity and
  sampling rate?
• What known sampling methods lead to highest
  capacity?
• What is the optimal sampling mechanism?
• Among all possible (known and unknown) sampling
  schemes

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Capacity under Sub-Nyquist Sampling

• Theorem 1
• Theorem 2
• Bank of ModulatorFilter?Single Branch ? Filter
  Bank

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Joint Optimization of Input and Filter Bank

• Selects the m branches with m highest SNR
• Example (Bank of 2 branches)

low SNR
Capacity monotonic in fs
highest SNR
2nd highest SNR
low SNR
How does this translate to practical modulation
and coding schemes
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Ideal Multiuser Detection
-

Signal 1
Signal 1 Demod
Iterative Multiuser Detection
Signal 2
Signal 2 Demod
MUD proposed for LTE (closes link at -7dB SNR)
-

Why Not Ubiquitous Today?
Power and A/D Precision
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Reduced-Dimension MUD

• Exploits that number of active users G is random
  and much smaller than total users (ala compressed
  sensing)
• Using compressed sensing ideas, can correlate
  with Mlog(G) waveforms
• Reduced complexity, size, and power consumption

10 Performance Degradation
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Reduced-Dimension Network Design
Random Network State
Reduced-Dimension State-Space Representation
Projection
Sparse Sampling
Approximate Stochastic Control and Optimization
Sampling and Learning
Utility estimation
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Feedback in Communications

• Memoryless point-to-point channels
• Capacity unchanged with perfect feedback
• Feedback drastically increases error exponent
  (L-fold exponential)
• Feedback reduces energy consumption

• Capacity of channels with feedback largely
  unknown
• For channels with memory and perfect feedback
• Under finite rate and/or noisy feedback
• For multiuser channels
• For multihop networks

• ARQ is ubiquitious in practice

Why?
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Cognitive Radios
PRx
PTx
MIMO Cognitive Underlay
Cognitive Overlay

• Cognitive radios support new wireless users in
  existing crowded spectrum without degrading
  licensed users
• Utilize advanced communication and DSP techniques
• Coupled with novel spectrum allocation policies
• Technology could
• Revolutionize the way spectrum is allocated
  worldwide
• Provide more bandwidth for new applications/servic
  es
• Multiple paradigms
• Underlay (exploiting unused spatial dimensions)
  and Overlay (exploiting relaying and interference
  cancellation) promising

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The Smart GridFusion of Sensing, Control,
Communications
carbonmetrics.eu
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Wireless and Health, Biomedicine and Neuroscience
Body-Area Networks
Todd Colemans Talk

• Doctor-on-a-chip
• Cell phone info repository
• Monitoring, remote
• intervention and services

Cloud
Ubli Mitras talk
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Summary

• Communications research alive and well
• Communications technology will enable new
  applications that will change peoples lives
  worldwide
• Design innovation will be needed to meet the
  requirements of next-generation wireless networks
• A systems view and interdisciplinary design
  approach holds the key to these innovations