NSF Workshop on Bridging Gap Between Wireless Networking Technologies and Advances in Physical Layer

1
NSF Workshop on Bridging Gap Between Wireless
Networking Technologies and Advances in Physical
Layer

• Breakout Session 3

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Participants

• Mario Gerla, Michael Honig, Tom Hou, Vijay Kumar,
  Tom Luo, Narayan Mandayam, Madhav Marathe, Anna
  Scaglione, Kang Shin, R Srikant, Aravind
  Srinivasan, Tan Wong, Lizhong Zheng

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Summary

• A number of advances are being made at creating
  new and more realistic models at PHY layer
• E.g. DMT models that quantify reliability rate
  trade-off
• New technologies in form of Cognitive Radios,
  MIMO radios and Cooperative communication will
  likely result in new PHY layer abstractions
• Current algorithmic/optimization methods often do
  not use the new models
• Tradeoff between realistic models and tractable
  models
• New techniques in algorithm theory and
  combinatorial optimization need to be developed
• Higher layers can benefit from an understanding
  of the PHY layer

4
PHY layer models 1

• Holistic view of control and data needs to be
  taken
• New and more realistic models at PHY layer are
  now available
• E.g. what is the networking viewpoint on the
  Diversity-Multiplexing Gain Tradeoff, for
  wireless multihop networks ?
• Layered approach should be maintained to the
  extent possible interfaces need to be created to
  expose information between layers for cross layer
  optimization
• Distinguish between models versus metrics

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PHY Layer Models 2

• New technologies such as cognitive radios are
  currently being developed. PHY level abstractions
  for these new technologies would provide the
  first step to more realistic protocol design and
  algorithmic analysis

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Issues in Optimization and Algorithms 1

• Many problems are cast as mixed (continuous as
  well as discrete variables) non-convex programs
• Very little work has been done in distributed
  optimization
• Good working definitions of distributed
  algorithms
• Robust Optimization algorithms should work when
  instances vary slightly, proof techniques should
  work when models are varied slightly
• Fault Tolerant algorithms

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Issues in Algorithms and Optimization 2

• Algorithms and analysis based on simple
  interference models such as disk graphs should
  not be discarded off hand
• Even though theorems might not extend, the proof
  techniques might still be extensible
• Simple models are amenable to analysis and one
  needs to quantify the additional gains that can
  be made by using more complicated models
• Generic algorithms algorithms work for all
  successively complicated models, analysis depends
  on the model at hand

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Issues in Networking

• Better systems and high level protocols can be
  built by understanding the PHY level abstractions
• E.g. Cognitive radio helps reduce the
  interference and enrich topology options (such as
  TCP fairness)
• New application areas such as Vehicular networks,
  Underwater networks, etc. require modifying PHY
  models
• Spatial and temporal variation in usage of
  spectrum can be used for better spectrum
  utilization

9
Summary

• A number of advances are being made at creating
  new and more realistic models at PHY layer
• E.g. DMT models that quantify reliability rate
  trade-off
• New technologies in form of Cognitive Radios,
  MIMO radios and Cooperative communication will
  likely result in new PHY layer abstractions
• Current algorithmic/optimization methods often do
  not use the new models
• Tradeoff between realistic models and tractable
  models
• New techniques in algorithm theory and
  combinatorial optimization need to be developed
• Identifying classes of integer programs and
  non-convex programs that can be solved
  efficiently exploiting the structure of the
  underlying problems

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What are we asked to Cover 1

• Comments on talks so far
• Specific Comments on Talks
• Important issues that are overlooked
• For physical layer folks
• what are other recent advances at physical layer,
  what are their impact on wireless networking,
  future
• expected advances/breakthroughs at physical layer
  and how they will impact wireless networking
  research, where are the research gaps

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What are we asked to Cover 2

• Wireless networking folks
• Current status (where we are now), future
  expectations on wireless networking and research
• Challenges, desired technology advances/
  breakthrough from the physical layer, new
  advances needed from theoretical perspective
• Algorithm design and optimization
• Status and Open Problems
• Advances/breakthrough at the physical layer, new
  challenging problems arising from future wireless
  networking, identify research gaps

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Models

• Realistic yet computationally tractable
• Get provable bounds using realistic models
• Recent techniques in optimization have not been
  exploited
• Over head of managing additional parameters