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This paper presents a scheduling framework for wireless networks focusing on maximizing users' satisfaction and revenue management. It covers models for users' satisfaction, scheduling algorithms, a case study on UMTS HSDPA, and numerical results. The study addresses the allocation problem of Radio Resource Management, considering efficient resource usage and constraints related to resource availability. It introduces the concept of utility and explores the trade-offs between welfare maximization, revenue, and pricing policies. The proposed scheduling algorithm starts with a trial solution, followed by a Local Search to enhance revenue. The approach aims to improve revenue up to 20-30% for 200 users. Future work includes parameter optimization, diverse traffic mixtures, and more advanced pricing strategies tailored to utility functions.
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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
Outline • Users’ Satisfaction Model for the RRM • Scheduling Algorithms Framework • Case Study: UMTS HSDPA • Numerical Results • Conclusions
Allocation problem of the RRM • General problem: assignment of a scarce resource. Radio Resource Management. Efficient resource usage and multiple access for different users.
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)
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.
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.
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):
Price effect introduction • This model allows a direct revenue evaluation as: Two different optimisation goals can be identified:
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.
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.
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
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
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.
