Future-Based Scheduling

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 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. Window marker Door marker Occupancy grid Wall • Future-Based Scheduling • In a K-user system, extend user k’s 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 Ntime-slots into future, similarly to Tk(t) into past. In numerator: denote as ‘1N’ In denominator: denote as ‘1D’ • Compute Tk(t) over both past and future windows, as if user always transmits, for N time-slots. • 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 (,  < ,  ), 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 • 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.  =  = 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.