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Massive access for Internet-of-Things (IoT) in beyond 5G networks representsa daunting challenge for conventional bandwidth-limited technologies.Millimeter-wave technologies (mmWave)---which provide large chunks of bandwidthat the cost of more complex wireless processors in harsher radioenvironments---is a promising alternative to accommodate massive IoT but itscost and power requirements are an obstacle for wide adoption in practice. Inthis context, meta-materials arise as a key innovation enabler to address thischallenge by Re-configurable Intelligent Surfaces (RISs). In this paper we takeon the challenge and study a beyond 5G scenario consisting of a multi-antennabase 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, webuild a mathematical framework to jointly optimize the precoding strategy ofthe BS and the RIS parameters in order to minimize the system sum mean squarederror (SMSE). This novel approach reveals convenient properties used to designtwo algorithms, RISMA and Lo-RISMA, which are able to either find simple andefficient solutions to our problem (the former) or accommodate practicalconstraints with low-resolution RISs (the latter). Numerical results show thatour algorithms outperform conventional benchmarks that do not employ RIS (evenwith low-resolution meta-surfaces) with gains that span from 20% to 120% in sumrate performance.

RISMA: Reconfigurable Intelligent Surfaces Enabling Beamforming for IoT Massive Access