Massive access for Internet-of-Things (IoT) in beyond 5G networks represents a daunting challenge for conventional bandwidth-limited technologies. Millimeter-wave technologies (mmWave)-which provide large chunks of bandwidth at the cost of more complex wireless processors in harsher radio environments-is a promising alternative to accommodate massive IoT but its cost and power requirements are an obstacle for wide adoption in practice. In this context, meta-materials arise as a key innovation enabler to address this challenge by Re-configurable Intelligent Surfaces (RISs).In this paper we take on the challenge and study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE). This novel approach reveals convenient properties used to design two algorithms, RISMA and Lo-RISMA, which are able to either find simple and efficient solutions to our problem (the former) or accommodate practical constraints with low-resolution RISs (the latter). Numerical results show that our algorithms outperform conventional benchmarks that do not employ RIS (even with low-resolution meta-surfaces) with gains that span from 20% to 120% in sum rate performance.

The low-rank behavior of massive multiple-input multiple-output (MIMO) channel covariance matrices and its exploitation for pilot decontamination and statistical beamforming are well documented. Existing algorithms, however, rely on signal subspace separation among user equipments (UEs) and, as such, they tend to fail when the distance between UEs becomes small. This paper proposes a solution to this problem via covariance shaping at the UE-side in the case where the UEs are equipped with (a small number of) multiple antennas. The key resides in: i) exploiting general non-Kronecker MIMO channel structures that allow the transmitter to suitably alter the channel statistics perceived by the base station, and ii) sacrificing some spatial degrees of freedom at each UE so as to improve the statistical orthogonality between closely spaced UEs. Numerical results illustrate the sum-rate performance gains of the proposed covariance shaping method with respect to existing ones.

Next generation mobile networks need to expand towards uncharted territories in order to enable the digital transformation of society. In this context, aerial devices such as unmanned aerial vehicles (UAVs) are expected to address this gap in hard-toreach locations. However, limited battery-life is an obstacle for the successful spread of such solutions. Reconfigurable intelligent surfaces (RISs) represent a promising solution addressing this challenge since on-board passive and lightweight controllable devices can efficiently reflect the signal propagation from the ground BSs towards specific target areas. In this paper, we focus on airto-ground networks where UAVs equipped with RIS can fly over selected areas to provide connectivity. In particular, we study how to optimally compensate flight effects and propose RiFe as well as its practical implementation Fair-RiFe that automatically configure RIS parameters accounting for undesired UAV oscillations due to adverse atmospheric conditions. Our results show that both algorithms provide robustness and reliability while outperforming state-of-the-art solutions in the multiple conditions studied.

Multicast services, whereby a common valuable message needs to reach a whole population of user equipments (UEs), are gaining attention on account of new applications such as vehicular networks. As it proves challenging to guarantee decodability by every UE in a large population, service reliability is indeed the Achilles' heel of multicast transmissions. To circumvent this problem, a two-phase protocol capitalizing on device-to-device (D2D) links between UEs has been proposed, which overcomes the vanishing behavior of the multicast rate. In this paper, we revisit such a D2D-aided protocol in the new light of precoding capabilities at the base station (BS). We obtain an enhanced scheme that aims at selecting a subset of UEs who cooperate to spread the common message across the rest of the network via D2D retransmissions. With the objective of maximizing the multicast rate under some outage constraint, we propose an algorithm with provable convergence that jointly identifies the most pertinent relaying UEs and optimizes the precoding strategy at the BS.

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In this paper, we present our experience designing, prototyping, and empirically characterizing RF Switch-based Reconfigurable Intelligent Surfaces (RIS). Our RIS design comprises arrays of patch antennas, delay lines and programmable radio-frequency (RF) switches that enable almost-passive 3D beamforming, i.e., without active RF components. We implement this design using PCB technology and low-cost electronic components, and thoroughly validate our prototype in a controlled environment with high spatial resolution codebooks. Finally, we make available a large dataset with a complete characterization of our RIS and present the costs associated with reproducing our design.

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The low-rank behavior of channel covariance matrices in massive multiple-input multiple-output (MIMO) settings can be exploited for pilot decontamination and statistical precoding. In this regard, many well-known algorithms are designed building on the assumption of signal subspace separation among user equipments (UEs), which is hardly satisfied in practice. To cope with this issue, our prior work introduced the concept of covariance shaping to enforce statistical orthogonality by exploiting the multiple antennas at the UE-side in the simple case of two UEs. This paper focuses on: i) extending the covariance shaping approach to a more general multiuser setting, and ii) evaluating its performance in several realistic scenarios, providing useful insights into its behavior and practical implementation.