  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.
