Special Issue on Massive MIMO

International audienceDemand for wireless communications is projected to grow by more than a factor of forty or more over the next five years. A potential technology for meeting this demand is Massive MIMO (also called Large-Scale Antenna Systems, Large-Scale MIMO, ARGOS, Full-Dimension MIMO, or Hyper-MIMO), a form of multi-user multipleantenna wireless which promises orders-of-magnitude improvements in spectral-efficiency over 4G technology, and accompanying improvements in radiated energy-efficiency. The distinguishing feature of Massive MIMO is that a large number of service-antennas - possibly hundreds or even thousands - work for a significantly smaller number of active autonomous terminals. Upsetting the traditional parity between service antennas and terminals in this manner is a game-changer: The simplest multiplexing pre-coding and de-coding algorithms can be nearly optimal, expensive ultra-linear forty-Watt power amplifiers are replaced by many low-power units, and the favorable action of the law of large numbers can greatly facilitate power-control and resource-allocation. Massive MIMO is still an emerging field. There are many unanswered theoretical questions and much remains to be done to obtain a reduction to practice. The six papers in this Special Issue are a sampling of the types of problems that are topics of active research. The papers logically fall into three categories: a) Acquisition of Channel State Information, b) Spatial Multiplexing Algorithms, and c) Massive Array Issues and Architectures

Before/after precoded massive MIMO in cloud radio access networks

Abstract—In this paper, we investigate two types of IQ-data transfer methods for cloud MIMO network operation. They are termed “after-precoding ” and “before-precoding. ” We formulate a cloud massive MIMO operation problem that includes the best IQ-transfer method and beamforming strategy (beamforming technique, the number of concurrently receiving users, the number of used antennas for transmission) to maximize the ergodic sum-rates under the limited capacity of the digital unit (DU)-radio unit (RU) link. Based on our proposed solution, the optimal numbers of users and antennas are simultaneously chosen. Numerical results confirm the sum-rate gain is greater when adaptive “after/before-precoding ” method is available than when only “after-precoding ” IQ-data transfer is available. Index Terms—massive MIMO, cloud base station, C-RAN, IQ-data, multi-user MIMO