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 via Real-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 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. Scalars,,, allow choice of balance between past, present and future. Can choose how to define F k (t) and use in numerator and/or denominator: 1.Exponentially-weighted decay over N time-slots into future, similarly to T k (t) into past. 2.Compute T k (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 T k (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 BRAN C fading realizations with n path-loss in a 100m-radius cell. 48 physical resource blocks (PRBs) of 16 subcarriers are each scheduled separately. R k (t) is the users 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 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 T k (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 t c and t f 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 Wall Window markerDoor marker t c = t f = N = 300, 6 users = = 5; = = 1