oncavity can be exploited. As an example, consider the interfer- ence function (7.13) Vk is a vector of interference coupling coefficients, chosen from a set VECRA. For each p, the vector Vk is adjusted SO that interference is minimized. This is a typical task of a receiver; therefore, we refer to Vk as the receive strategy. Note that the function in Equation (7.13) fulfills A1-A3, SO it is an interference function. It is also concave because the point-wise minimum over linear functions is concave. Boche and Schubert [40] showed that the power minimization problem (7.10) can be solved with superlinear convergence, whereas the fixed-point iteration (7.12) has only linear convergence [40,46]. This improved convergence speed is facilitated by exploiting the concavity of the interference function (7.13). Boche and Schubert [41] also showed that every concave interference function can be expressed in the form of Equation (7.13); thus, the power minimization problem (7.10) can be solved with superlinear convergence. Similar results can be shown for convex interference functions. Superlinear convergence can be achieved for any convex or concave interference function. Another interesting operating point is obtained by the principle of proportional fairness. The motivation for proportional fair resource allocation [47] is the observed utility inefficiency of min-max fairness. A min-max fair scheduler allocates the resources in such a way that all communication links achieve the same QoS. This strategy does not perform well in the presence of bottleneck links. If one link is very weak, then it may require all of the available resources in order to achieve an acceptable QoS. Min-max fairness is therefore known for its bad overall system efficiency. Proportional fairness avoids this effect by putting more emphasis on the links with good channel conditions. This was first studied in the context of wireline networks [47]. Later, it was also successfully applied in the wireless context (see, for example, Viswanth et al. [54]). 