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One of the fundamental problems for hybrid beamformer design in MIMO systems is that, in Singular-Value-Decomposition (SVD) based beamforming, the analog beamformer is limited to constant modulus. In order to solve this issue, an algorithm is proposed in this paper. Specifically, the parallel data streams are expressed mathematically, and then the math expression of the analog beamformer is achieved through maximizing the power of desired data streams while minimizing the power of interference.

We consider the problem of nonnegative tensor completion. Our aim is to derive an efficient algorithm that is also suitable for parallel implementation. We adopt the alternating optimization framework and solve each nonnegative matrix completion problem via a Nesterov-type algorithm for smooth convex problems. We describe a parallel implementation of the algorithm and measure the attained speedup in a multi-core computing environment. It turns out that the derived algorithm is an efficient candidate for the solution of very large-scale sparse nonnegative tensor completion problems.

This work proposes a real-time movement control algorithm for massive unmanned aerial vehicles (UAVs) that provide emergency cellular connections in an urban disaster site. While avoiding the inter-UAV collision under temporal wind dynamics, the proposed algorithm minimizes each UAV’s energy consumption per unit downlink rate. By means of a mean-field game theoretic flocking approach, the velocity control of each UAV only requires its own location and channel states.

Optimal resource allocation in interference networks requires the solution of non-convex optimization problems. Except from treating interference as noise (IAN) one usually has to optimize jointly over the achievable rates and transmit powers. This non-convexity is normally only due to the transmit powers while the rates are linear. Conventional approaches like the Polyblock Algorithm treat all variables equally and, thus, require a two layer solver to exploit the linearity in the rates and keep the computational complexity at a reasonable level.

In this talk we discuss some recent limit laws for empirical optimal transport distances from a simulation perspective. On discrete spaces, this requires to solve another optimal transport problem in each simulation step, which reveals simulations of such limit laws computational demanding. We discuss an approximation strategy to overcome this burden. In particular, we examine empirically an upper bound for such limiting distributions on discrete spaces based on a spanning tree approximation which can be computed explicitly.