The exponential increase of wireless user equipments (UEs) and network services associated with current fifth-generation (5G) deployments poses several unprecedented design challenges that need to be addressed with the advent of future beyond-5G networks. Specifically, the growing demand for high data rates along with the need to serve a large number of heterogeneous devices, ranging from classical mobile phones, to connected objects forming the Internet-of-Things (IoT), motivates the study of novel signal processing and transmission schemes. In this regard, massive multiple-input multiple-output (MIMO) is a well-established access technology, which allows to serve many tens of UEs using the same time-frequency resources by means of highly directional beamforming techniques. However, massive MIMO exhibits scalability issues in massive access scenarios where the UE population is composed of a large number of heterogeneous devices.
Indeed, while the availability of a large number of antennas in massive MIMO transceivers brings substantial performance gains, it also greatly increases the system overhead and complexity. Specifically, the high dimensionality of the channels requires the allocation of considerable time-frequency resources to acquire the channel state information (CSI) and results in largematrix operations to construct precoders/decoders. Moreover, in the context of multicast communications as, e.g., wireless edge caching or broadcasting of mission-critical messages, conventional multi-antenna techniques exhibit vanishing rates as the number of UEs increases even in the massive antenna regime. Lastly, the large number of radio frequency (RF) chains associated with massive MIMO transceivers, which are used to counteract propagation losses in harsh environments such as, e.g., at millimiter-wave (mmWave) frequencies, clashes with the limited power budget of IoT devices.
In this thesis, we propose novel scalable multi-antenna methods for performance enhancement in the aforementioned scenarios of interest. Specifically, we describe the fundamental role played by statistical CSI that can be leveraged for reduction of both complexity and overhead for CSI acquisition, and for multi-UE interference suppression. Indeed, when the UEs are equipped with at least two antennas, their spatial selectivity properties can be leveraged to enforce statistical orthogonality among interfering transmissions. Moreover, we exploit device-to-device (D2D) communications to overcome the fundamental bottleneck of conventional multicasting. In particular, we exploit the precoding capabilities of a multi-antenna transmitter to carefully select UEs in favorable channel conditions, which in turn act as opportunistic relays and retransmit the message via the D2D links. Lastly, in the context ofmmWave communications, we explore the benefits of the recently proposed reconfigurable intelligent surfaces (RISs), which are a key innovation enabler thanks to their inherently passive structure that allows to control the propagation environment and effectively counteract propagation losses. In particular, we employ passive beamforming at the RIS, i.e., without any significant power expenditure, together with conventional active beamforming at the transmitter to substantially increase the network throughput performance.