Aim : Develop a flexible new scheduling methodology which improves fairness by adding knowledge of users

1
Wireless Schedulers with Future Sight
viaReal-Time 3D Environment MappingMatthew
Webb, Congzheng Han, Angela Doufexi and Mark Beach
Aim Develop a flexible new scheduling
methodology which improves fairness by adding
knowledge of users future data rates into the
proportional fair scheduling metric.

• Introduction
• New applications, such as Layar and ViewNet
  allow augmented reality models to represent the
  physical environment in real-time.
• ViewNet can produce and store an occupancy grid
  associating position to rate, channel state, etc.
  and a low-resolution 3D map to permit, e.g.,
  coarse RSSI prediction by identifying walls,
  doors and windows.
• Future data-rates can be estimated by
  extrapolating a users recent motion track and
  relying on previously stored values of data-rate
  at those co-ordinates, or low-resolution
  ray-tracing of stored physical structure.

Window marker
Door marker
Occupancy grid
Wall

• Future-Based Scheduling
• In a K-user system, extend user ks proportional
  fair (PF) metric to include measures of their
  future data-rates

• Scalars ?, ?, ?, ? allow choice of balance
  between past, present and future.
• Can choose how to define Fk(t) and use in
  numerator and/or denominator
• Exponentially-weighted decay over N time-slots
  into future, similarly to Tk(t) into past.

In numerator denote as 1N
In denominator denote as 1D

1. Compute Tk(t) over both past and future windows,
   as if user always transmits, for N time-slots.

1. Fully compute scheduling at N future times, and
   use resulting Tk(t) in PF metric. Effectively, ?
   ? 0.

Performance

• Future schedulers based on 1N give fairness
  improvement over PF for small capacity loss.
• Future knowledge in numerator (1N) acts to
  smooth out short dips in rate by compensating in
  the metric with near-term increases in rate.
• Best configuration has future information
  weighted less than past (?, ? lt ?, ? ), but does
  include both.
• Full re-scheduling (3) gives longer-term
  average for Tk(t), but statistics of BRAN channel
  are stationary. More useful if path-loss is
  changing.
• 1N 3 makes decisions on the 1N metric, but
  long-term average rate is on PF basis, so can
  assume wrong users, and capacity falls slightly.

tc tf N 300, 6 users
Simulation parameters 4x4 MIMO-OFDMA with 6 or 10 users, 1024 subcarriers, 768 data subcarriers, guard interval of 176. Transmit power 17 dBm for each user.
Simulation parameters 3000 BRAN C fading realizations with 802.11n path-loss in a 100m-radius cell.
Simulation parameters 48 physical resource blocks (PRBs) of 16 subcarriers are each scheduled separately. Rk(t) is the users mean capacity across the PRB.

• With various system-level parameters, fairness
  enhancement for 1N and 1N2 is retained.
• General behaviour is familiar from classical PF
  scheduler
• More users reduces fairness but future-based
  schedulers do much better than greedy.
• Longer tc and tf trade fairness for capacity.
  But 1N 3 loses on both since decisions it
  makes are based on more wrong information.
• Increasing future horizon, N, also improves
  fairness as scheduling metric can take more
  future information into account if there is a
  near-term dip in rate for a particular user.

? ? 5 ? ? 1

• Conclusions and Future Work
• Future-based schedulers can achieve better
  fairness and nearly the same capacity as
  classical PF scheduler.
• The new scheduling metric including future
  knowledge allows a flexible capacity-fairness
  tradeoff to be made.
• Future-based schedulers with a significant
  weighting to the past (?, ?) are the most
  successful in this channel model.
• Future work Analyse effects of (i) imperfect
  future data-rates (ii) motion, i.e. changing
  path-loss in channel models.

This work was co-funded by the UK Technology
Strategy Board. We thank all the partners to the
ViewNet project for their help and discussions.