  Reconfigurable intelligent surfaces (RIS)-assisted massive multiple-input
multiple-output (mMIMO) is a promising technology for applications in
next-generation networks. However, reflecting-only RIS provides limited
coverage compared to a simultaneously transmitting and reflecting RIS
(STAR-RIS). Hence, in this paper, we focus on the downlink achievable rate and
its optimization of a STAR-RIS-assisted mMIMO system. Contrary to previous
works on STAR-RIS, we consider mMIMO, correlated fading, and multiple user
equipments (UEs) at both sides of the RIS. In particular, we introduce an
estimation approach of the aggregated channel with the main benefit of reduced
overhead links instead of estimating the individual channels. {Next, leveraging
channel hardening in mMIMO and the use-and-forget bounding technique, we obtain
an achievable rate in closed-form that only depends on statistical channel
state information (CSI). To optimize the amplitudes and phase shifts of the
STAR-RIS, we employ a projected gradient ascent method (PGAM) that
simultaneously adjusts the amplitudes and phase shifts for both energy
splitting (ES) and mode switching (MS) STAR-RIS operation protocols.} By
considering large-scale fading, the proposed optimization can be performed
every several coherence intervals, which can significantly reduce overhead.
Considering that STAR-RIS has twice the number of controllable parameters
  compared to conventional reflecting-only RIS, this accomplishment offers
substantial practical benefits. Simulations are carried out to verify the
analytical results, reveal the interplay of the achievable rate with
fundamental parameters, and show the superiority of STAR-RIS regarding its
achievable rate compared to its reflecting-only counterpart.
