Study of feasible cell-free massive MIMO systems in realistic indoor scenarios




  Danaisy Prado Álvarez


  Danaisy Prado Alvarez
  José Francisco Monserrat del Río
  David Martín-Sacristán Gandía


The massive use of telecommunications demands higher capacity networks.
This capacity can be increased by increasing the number of antennas, bandwidth, spectral efficiency, or a combination of these. In response to this, cellfree massive Multiple-Input Multiple-Output (MIMO) systems have emerged.
These systems aim to offer a ubiquitous and reliable service, relying on a massive number of antennas and adapting the network to users’ needs. Cell-free massive MIMO systems have been studied both for frequencies below 6 GHz and in the millimeter Wave (mmW) band, proving to be a good alternative to small cells. However, many issues still require further study. This Thesis addresses the issues concerning cell-free massive MIMO deployments in terms of scalability, power consumption, realistic modeling of deployment scenarios,
and design of precoders for such scenarios in the mmW band.
Cell-free massive systems in their canonical form consider that all the Access Points (APs) are connected to a single Central Processing Unit (CPU) and serve all User Equipments (UEs) simultaneously. However, in practice, such a system is not feasible, due to scalability reasons. Therefore, in this Thesis, different clustering solutions that alleviate the load of both each individual AP and the CPUs, as the total processing load is divided among them, are studied and proposed. The proposed solutions show a better performance than the stateof-the-art solution studied for all cluster sizes considered and independently of the number of UEs in the scenario.
After the logical network topology considerations, the impact on the network performance of different physical topologies configurations is analyzed.
Specifically, the power consumption modeling considering fully dedicated, hybrid, and fully serial front-haul is studied. In this sense, some modifications are suggested for the traditional power consumption model in order to get more accurate results when serial environments are analyzed. The obtained results highlight the importance of applying the proposed modifications that consider the power savings due to the serial connections in a cell-free massive MIMO deployment where each AP transmits the same information (except by the precoding coefficients).
On the other hand, although wider bandwidths are available in the millimeter band, the use of these frequencies brings certain challenges. One of these challenges is modeling the radio channel since when working with wavelengths in the order of tens of millimeters, any object or roughness of the same order can affect the propagation of the wave. Another challenge is to consider the electromagnetic impact of the human body at mmW frequencies. In this sense, this Thesis, firstly, proposes some adaptations to the Third Generation Partnership Project (3GPP) body blockage model. The results obtained after the modifications are closer to real measurement values, what makes the adapted model more accurate for the consideration of body blockage at mmW. Secondly, this Thesis presents a radio channel simulation tool based on ray tracing. With
this tool, pathloss results have been obtained for an indoor scenario that are remarkably close to the actual measurements. Also, the results show that when the electromagnetic characteristics of the materials are not modeled correctly or the furniture is not taken into account in an indoor scenario, the adjustment of the simulation results can differ considerably from the real measurements.
Finally, the design of precoders in cell-free massive MIMO systems in a realistic scenario is addressed. For this purpose, an industrial scenario with specific power requirements is considered. In particular, an optimization problem with different per-antenna power constraints is solved. In this case, the scenario and the radio channel are modeled using the above mentioned tool.
This fact makes it possible to find with high precision the power coefficients to be used by each transmitting antenna to transmit to each user so that the achieved data rate is maximized.