Aim : Develop a flexible new scheduling methodology which improves fairness by adding knowledge of users’ future data rates into the proportional fair scheduling metric.

Wireless Schedulers with Future Sight viaReal-Time 3D Environment Mapping

Matthew Webb, Congzheng Han, Angela Doufexi and Mark Beach

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

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 user’s 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.

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• Can choose how to define Fk(t) and use in numerator and/or denominator:

1. Exponentially-weighted decay over N time-slots into future, similarly to Tk(t) into past.

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

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

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.

3000 BRAN ‘C’ fading realizations with 802.11n path-loss in a 100m-radius cell.

48 physical resource blocks (PRBs) of 16 subcarriers are each scheduled separately.Rk(t) is the user’s mean capacity across the PRB.

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.

In numerator: denote as ‘1N’

In denominator: denote as ‘1D’

Greedy PF 1N 1D 2 1N + 2 3 1N + 313

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Scheduler

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= = = = 1 = = 1; = = 0.5 = = 0.5; = = 1 = = 5; = = 1 = = 1; = = 5

Greedy PF 1N 1D 2 1N + 2 3 1N + 30.5

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= = = = 1 = = 1; = = 0.5 = = 0.5; = = 1 = = 5; = = 1 = = 1; = = 5

Greedy PF 1N 1D 2 1N + 2 3 1N + 30.3

0.35

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6 users, tc = t

f = N = 300

6 users, tc = t

f = N = 3000

10 users, tc = t

f = N = 300

10 users, tc = t

f = N = 3000

6 users, N = 3000, tc = t

f = 300

Greedy PF 1N 1D 2 1N + 2 3 1N + 313

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Scheduler

Mea

n c

apa

city

, bps

/Hz

6 users, t

c = t

f = N = 300

6 users, tc = t

f = N = 3000

10 users, tc = t

f = N = 300

10 users, tc = t

f = N = 3000

6 users, N = 3000, tc = t

f = 300

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 (, < , ), 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.

• With various system-level parameters, fairness enhancement for ‘1N’ and ‘1N+2’ 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.

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.

Occupancy grid

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Window marker Door marker

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