in those highly probable cases in which the number of active in the network is greater than the number of available chains at each (i.e., $K > L$), the $m$th , with $m\in\{1,\ldots,M\}$, can only beamform to a group of $L$ out of the $K$ in the network, which are indexed by the set $\mathcal{K}_m=\left\{\kappa_{m1},\ldots,\kappa_{mL}\right\}$. As the beamforming matrices at the are a function of only the large-scale spatial channel covariance matrices and are common to both the and the , the selection of the sets of to beamform from each must also be based only on the available large-scale . Inspired by the Frobenius norm-based suboptimal user selection algorithm proposed by Shen *et al.* in [@Shen06], a selection algorithm is proposed that aims at maximizing the sum of the average energy (i.e., average Frobenius norm) of the equivalent channels (including the corresponding beamformer) between the $M$ and the $K$ with the constraints that, first, the minimum average energy of the equivalent channel between the $M$ and any of the active must be maximized and, second, that each can only beamform to $L$ . Note that this optimization problem, which tends to provide some degree of (average) max-min fairness among 