## 1 Introduction  
Nowadays, one of the most active research areas in the wireless communication field is the design of communication techniques for fifth generation (5G) cellular systems. Although the final technical components of the 5G system are still under discussion, important progress has already been made towards the definition of the set of scenarios and requirements to be addressed by these systems. In particular, the European project Mobile Enablers for the Twenty-Twenty Information Society (METIS) played a key role in this direction by stating that, among other goals, the 5G system shall boost the user data rate of 4G cellular systems by a factor ranging from 10 to 100 [1]. A promising technology to meet this specific 5G requirement is the use of advanced multiple-input multiple-output (MIMO) techniques with large antenna arrays, where the number of antenna elements is much higher than the number of users to be served, also known as massive MIMO [2]. Massive MIMO techniques imply the use of, at least, an order of magnitude more antenna elements than in current systems [3], and their application can be twofold: either it can be used to compensate the propagation losses of backhaul and access links through beamforming, or it can be used to boost capacity with multi-stream transmission. In fact, although it has been historically associated with beamforming, current trends point towards the application of MIMO spatial multiplexing techniques in conjunction with beamforming to best exploit the richness of the channel [4]. Unfortunately, one of the showstoppers for the successful incorporation of massive MIMO into cellular networks is the large physical size of the antenna arrays at currently used cellular frequencies. For this reason, massive MIMO is being considered in conjunction with millimeter-Wave (mmW) frequencies [3; 5], where antenna arrays of reasonable physical sizes are feasible [6]. From a signal processing perspective, transmission with massive arrays increases the processing complexity derived from the computation of the required precoders when digital schemes are applied to hundreds of antennas. Multi-antenna digital precoding is carried out at baseband and, thus, it requires an architecture with as many radio-frequency (RF) chains as antenna ports to do the digital to analog data conversion and subsequent upmixing to RF. However, the use of one RF chain per antenna is not only a very costly option for massive MIMO systems, but it also leads to a extremely high power consumption [3; 7]. Motivated by this, there is now a growing interest in alternative transmission schemes based on analog beamforming (ABF) and hybrid beamforming (HBF) architectures, where all or part of the processing is based on radio-frequency beamforming. Many ABF and HBF approaches have been proposed in the last years, both for single-user (SU) [8; 9] and multi-user (MU) transmissions [10; 11], all of them using a substantially reduced number of RF chains with respect to the number of antennas. The potential of these schemes is well-known; however, to the best of the authors' knowledge, there is not yet a clear view on the performance of the different beamforming alternatives in a complete system with multiple transmission and reception points and a realistic channel model. In this direction, some preliminary tests were carried out for a single-link case in [12] and for a multi-link case in [13]. System level simulations usually rely on a set of ideal assumptions that may have a non negligible impact on the accuracy of the assessments based on these simulations [13]. One such assumption is the perfect knowledge of the channel state at the transmitter, the receiver or both entities. In practice, channel measurements present errors and, additionally, channel state changes within the time elapsed from the channel measurement until the transmission/reception phase. Therefore, perfect channel knowledge is not realistic. Another common assumption is the use of ideal hardware components in all the devices involved in the communication.