Selective Fair Scheduling over Fading Channels

Imposing fairness in resource allocation incurs a loss of system throughput, known as the Price of Fairness (PoF ). In wireless scheduling, PoF 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 PoF by momentarily blocking the rest. We study the associated admission control problem of finding the user selection that minimizes PoF 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 PoF 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 PoF . Simulations show that our intelligent blocking outperforms by 40% in throughput previous approaches which satisfy the SLA by blocking low-SNR users.