Energy-Efficient Power Control in Cell-Free and User-Centric Massive MIMO at Millimeter Wave

In a cell-free massive MIMO architecture a very large number of distributed access points simultaneously and jointly serves a much smaller number of mobile stations; a variant of the cell-free technique is the user-centric approach, wherein each access point just serves a reduced set of mobile stations. This paper introduces and analyzes the cell-free and user-centric architectures at millimeter wave frequencies, considering a training-based channel estimation phase, and the downlink and uplink data transmission phases. First of all, a multiuser clustered millimeter wave channel model is introduced in order to account for the correlation among the channels of nearby users; second, an uplink multiuser channel estimation scheme is described along with low-complexity hybrid analog/digital beamforming architectures. Third, the non-convex problem of power allocation for downlink global energy efficiency maximization is addressed. Interestingly, in the proposed schemes no channel estimation is needed at the mobile stations, and the beamforming schemes used at the mobile stations are channel-independent and have a very simple structure. Numerical results show the benefits granted by the power control procedure, that the considered architectures are effective, and permit assessing the loss incurred by the use of the hybrid beamformers and by the channel estimation errors.Comment: To appear on the IEEE Transactions on Green Communications and Networking; originally submitted on April 24, 2018 and finally accepted for publication on March 24, 201

Joint cell selection and radio resource allocation in MIMO small cell networks via successive convex approximation

It is widely recognized that one of the factors that are going to yield the most significant capacity increase in wireless networks is spatial reuse of radio resources through dense deployment of radio access points. This leads to the development of small cell networks where different size cells, e.g. macro cells, picocells, femtocells, relays, coexist under the same standard. Of course, dense deployment is able to unravel its potential benefits only provided that interference is properly managed. In this paper, we propose an algorithm able to perform cell association and radio resource allocation jointly, in order to maximize the sum rate in a MIMO (interference) network. Cell selection is inherently a combinatorial problem. To deal with the nonconvexity, we introduce a suitably chosen convex relaxation of the objective function and develop a fast algorithm converging to a locally optimal solution of the nonconvex problem. © 2014 IEEE