Scheduling for Wireless Networks with Users’ Satisfaction and Revenue Management

1. Scheduling for Wireless Networks with Users’ Satisfaction and Revenue Management Leonardo Badia*, Michele Zorzi+ Speaker: Andrea Zanella+ {lbadia, zorzi}@ing.unife.it *Dept. of Engineering, University of Ferrara +Dept. of Information Eng., University of Padova

2. Outline • Users’ Satisfaction Model for the RRM • Scheduling Algorithms Framework • Case Study: UMTS HSDPA • Numerical Results • Conclusions

3. Allocation problem of the RRM • General problem: assignment of a scarce resource. Radio Resource Management. Efficient resource usage and multiple access for different users.

4. Allocation problem of the RRM • Micro-economic concept of utility (dep. on allocated resource g) Target: welfare maximisation (?) Constraints on the availability of the resource (band)

5. User’s Satisfaction Concept • High performance peaks are useless when a low assignment is satisfactory. • Target of the operator: to satisfy the customers and to have high profit. • Another trade-off: total welfare vs. total earned revenue.

6. Price effect introduction • Price piimpacts on the revenue. • In this work we focus on two different pricing policies: • pi (gi) = p, admitted user (flat price) • pi (gi) = kgi , user (linear price) The appreciation of the service depends on the paid price.

7. Price effect introduction Our proposal is to consider an Acceptance-probability Ai dependent on both price and utility. A possible choice (coherent with the properties of such a probability):

8. Price effect introduction • This model allows a direct revenue evaluation as: Two different optimisation goals can be identified:

9. Our Scheduling Algorithm • Start with a “trial” solution. • The starting solution is similar but not equal to the CS scheduler. This overcomes fairness problems. • A further Local Search is performed. • The Local Search is aimed at increasing the revenue at each iteration.

10. Wireless Networks Scheduling • The starting solution is based on the marginal utility u’(g) equal to a given threshold. • This avoids over-assignments which are instead present with the CS scheduler. • Performance metrics: revenue, admission rate.

11. High Speed Downlink Packet Access • UMTS - release 5 • New Shared Channel (High Speed – Downlink Shared CHannel – HS DSCH) • Fast scheduling (MAC – High Speed, located in the Node B) • Downlink side, asymmetric traffic

12. Simulation parameters

13. Results

14. Results

15. Conclusions • Microeconomic theory considerations (utility and pricing trade-off) • Consequences on operator’s revenue and users’ service appreciation • Good results with LC strategy (local search aimed at revenue maximisation): revenue is improved up to 20-30% for 200 users. • Possibilities of price tuning and more aware choice of the RRM parameters

16. Future work • Parameter optimisation. • Different traffic mixtures. • More complex pricing strategies, suited to the form of the utility functions. • Possibility of adopting the acceptance-probability as a sorting metric directly into the scheduler.