Wireless Link Adaptation for Deadline Constrained Traffic with Imperfect Channel Estimates

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Wireless Link Adaptation for Deadline Constrained
Traffic with Imperfect Channel Estimates
Tim Holliday Depts of MSE and E.E. Stanford
University thollida_at_stanford.edu
Andrea Goldsmith Dept of E.E. Stanford
University andrea_at_ee.stanford.edu
Peter Glynn Dept of MSE Stanford
University glynn_at_stanford.edu
IEEE ICC 2002 New York, NY
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The Problem

• Streaming audio/video and other deadline
  constrained traffic require tight QoS constraints
• Current Fixed-BER control policies consume far
  more power than necessary and reduce system
  capacity
• Delayed and erroneous channel estimates can wreak
  havoc on the performance of Fixed-BER controls
• High speed mobiles and urban propagation
  environments are a particularly bad combination

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The Main Issues

• How do you solve for optimal link adaptation
  policies for deadline constrained traffic?
• Minimum power consumption subject to constraints
  on deadline expiration
• How do channel characteristics and poor channel
  estimates affect the optimal control?
• What types of QoS constraints accurately
  represent performance?
• Is a simple constraint on probability of data
  loss sufficient? (Answer It depends on the
  channel)

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A Solution

• We propose a dynamic programming solution for
  optimal power and coding with deadline
  constrained traffic
  
• Key advantages
• Permits a great deal of flexibility and detail in
  our wireless channel models
• Allows for a wide variety of performance
  constraints
• Allows us to adapt to delayed/inaccurate channel
  estimates

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System Model

• Standard TDMA cellular structure (no intra-cell
  interference)
• Single wireless mobile transmitting deadline
  sensitive data to a base station
• All data generated at the mobile has a deadline
  by which is must be transmitted
• Mobile can adapt transmission power, modulation,
  and convolutional code parameters to meet
  deadline requirements

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Dynamic Programming Formulation

• Construct a Markov chain model for a mobile
• See the paper for details (Markov modulated
  traffic sources, traffic with multiple deadlines,
  etc.)
• Each unique combination of power, modulation, and
  coding determines a different transition matrix
• Objective of the DP is to choose the optimal
  transition matrix for each state of the mobile

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Value Function and Transition Dynamics

• Define a control policy g as a function that maps
  states into actions (i.e. a transition matrix)
• Then define a value function and transition
  matrix (V(g) and P(g)) such that

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Optimization and Constraints

• We can optimize the value function through a
  simple linear program.
• This structure also allows us to easily constrain
  various metrics like probability of deadline
  expiration

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Imperfect Channel Estimates

• Consider a simple two-state Markov chain channel
  model with transition matrix Q.
• Suppose the estimates are uncertain and delayed
  by 5 timeslots. The best guess of the channel
  state is NOT the old estimate!

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Numerical Example Setup

• System is modeled after EDGE, see the paper for
  details on time slot structure, BLER plots, etc.
• Mobile adaptation options
• Transmission power range 20mW to 800mW
• Choose between MCS-6 and MCS-9 (rate ½ and full
  rate 8PSK)
• Traffic deadlines are 60ms or 3 time slots
• Deadline constraint is 1

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Channel Models

• Consider two mobile speeds 3 km/h and 50 km/h
• Two shadowing environments with mean path loss of
  120dB
• Macrocell
• Std. Dev. 7dB
• Correlation.82 at 100 meters
• Microcell
• Std. Dev. 4.3dB
• Correlation.3 at 20 meters
• So we should expect estimate delay to have bigger
  impact in a 50 km/h microcell than a 3 km/h
  macrocell

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Power Cost For A Fixed-BER Control
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Power Cost For Dynamic Programming Solution
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Example Control Policy
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3 km/h
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3 km/h
50 km/h
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Power Cost With Consecutive Loss Constraint
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Conclusion

• We have presented a framework for finding optimal
  link adaptation policies for deadline constrained
  traffic
• We also proposed a means to adapt to channel
  estimation errors and delays
• Both the optimal control and the validity of the
  QoS constraints can be greatly affected by the
  channel model