Near Optimum Power Control and Precoding under Fairness Constraints in Network MIMO Systems

We consider the problem of setting the uplink signal-to-noise-and-interference (SINR) target and allocating transmit powers for mobile stations in multicell spatial multiplexing wireless systems. Our aim is twofold: to evaluate the potentialof such mechanisms in network multiple input multiple output (MIMO) systems, and to develop scalable numerical schemes thatallow real-time near-optimal resource allocation across multiple sites. We formulate two versions of the SINR target and powerallocation problem: one for maximizing the sum rate subject to power constraints, and one for minimizing the total powerneeded to meet a sum-rate target. To evaluate the potential of our approach, we perform a semianalytical study in Mathematica using the augmented Lagrangian penalty function method. We find that the gain of the joint optimum SINR setting and power allocation may be significant depending on the degree of fairness that we impose. We develop a numerical technique, based on successive convexification, for real-time optimization of SINR targets and transmit powers. We benchmark our procedure against the globally optimal solution and demonstrate consistently strong performance in realistic network MIMO scenarios. Finally, we study the impact of near optimal precoding in a multicell MIMO environment and find that precoding helps to reduce the sum transmit power while meeting a capacity target