Selective fair scheduling over fading channels

Imposing fairness in resource allocation incurs a loss of system throughput, known as the Price of Fairness (P oF). In wireless scheduling, P oF increases when serving users with very poor channel quality because the scheduler wastes resources trying to be fair. This paper proposes a novel resource allocation framework to rigorously address this issue. We introduce selective fairness: being fair only to selected users, and improving P oF by momentarily blocking the rest. We study the associated admission control problem of finding the user selection that minimizes P oF subject to selective fairness, and show that this combinatorial problem can be solved efficiently if the feasibility set satisfies a condition; in our model it suffices that the wireless channels are stochastically dominated. Exploiting selective fairness, we design a stochastic framework where we minimize P oF subject to an SLA, which ensures that an ergodic subscriber is served frequently enough. In this context, we propose an online policy that combines the drift-plus-penalty technique with GradientBased Scheduling experts, and we prove it achieves the optimal P oF. Simulations show that our intelligent blocking outperforms by 40% in throughput previous approaches which satisfy the SLA by blocking low-SNR users.