  Antenna selection is capable of reducing the hardware complexity of massive
multiple-input multiple-output (MIMO) networks at the cost of certain
performance degradation. Reconfigurable intelligent surface (RIS) has emerged
as a cost-effective technique that can enhance the spectrum-efficiency of
wireless networks by reconfiguring the propagation environment. By employing
RIS to compensate the performance loss due to antenna selection, in this paper
we propose a new network architecture, i.e., RIS-assisted massive MIMO system
with antenna selection, to enhance the system performance while enjoying a low
hardware cost. This is achieved by maximizing the channel capacity via joint
antenna selection and passive beamforming while taking into account the
cardinality constraint of active antennas and the unit-modulus constraints of
all RIS elements. However, the formulated problem turns out to be highly
intractable due to the non-convex constraints and coupled optimization
variables, for which an alternating optimization framework is provided,
yielding antenna selection and passive beamforming subproblems. The
computationally efficient submodular optimization algorithms are developed to
solve the antenna selection subproblem under different channel state
information assumptions. The iterative algorithms based on block coordinate
descent are further proposed for the passive beamforming design by exploiting
the unique problem structures. Experimental results will demonstrate the
algorithmic advantages and desirable performance of the proposed algorithms for
RIS- assisted massive MIMO systems with antenna selection.
