Generalized proportional fair scheduling in third generation wireless data networks

In 3G data networks, network operators would like to balance system throughput while serving users in a fair manner. This is achieved using the notion of proportional fairness. However, so far, proportional fairness has been applied at each base station independently. Such an approach can result in non-Pareto optimal bandwidth allocation when considering the network as a whole. Therefore, it is important to consider proportional fairness in a network-wide context with user associations to base stations governed by optimizing a generalized proportional fairness objective. In this paper, we take the first step in formulating and studying this problem rigorously. We show that the general problem is NP-hard and it is also hard to obtain a close-to-optimal solution. We then consider a special case where multi-user diversity only depends on the number of users scheduled together. We propose efficient offline optimal algorithms and heuristic-based greedy online algorithms to solve this problem. Using detailed simulations based on the base station layout of a large service provider in the U.S., we show that our simple online algorithm, which assigns a newly arrived user to a base station that improves the generalized proportional fairness objective the most without changing existing users' association, is very close to the offline optimal solution. The greedy algorithm can achieve significantly better throughput and fairness in heterogeneous user distributions, when compared to the approach that assigns a user to the base station with the best signal strength.