  Future wireless communications are largely inclined to deploy massive numbers
of antennas at the base stations (BSs) by leveraging cost- and energy-efficient
as well as environmentally friendly antenna arrays. The emerging technology of
dynamic metasurface antennas (DMAs) is promising to realize such massive
antenna arrays with reduced physical size, hardware cost, and power
consumption. The goal of this paper is the optimization of the energy
efficiency (EE) performance of DMA-assisted massive multiple-input
multiple-output (MIMO) wireless communications. Focusing on the uplink, we
propose an algorithmic framework for designing the transmit precoding of each
multi-antenna user and the DMA tuning strategy at the BS to maximize the EE
performance, considering the availability of either instantaneous or
statistical channel state information (CSI). Specifically, the proposed
framework is shaped around Dinkelbach's transform, alternating optimization,
and deterministic equivalent methods. In addition, we obtain a closed-form
solution to the optimal transmit signal directions for the statistical CSI
case, which simplifies the corresponding transmission design for the
multiple-antenna case. Our numerical results verify the good convergence
behavior of the proposed algorithms, and showcase the considerable EE
performance gains of the DMA-assisted massive MIMO transmissions over the
baseline schemes.
