A new graph perspective on max-min fairness in Gaussian parallel channels

In this work we utilize a novel view of graph theory to characterize the notion of max-min fairness in Gaussian parallel channels. We show that the max-min fair performance behaves as a version of the Lovasz function in terms of a graph induced by the channel sharing. This constructive result leads also to the specification of some easily computable fair policies.