  Reconfigurable intelligent surfaces (RISs) are widely considered a promising
technology for future wireless communication systems. As an important indicator
of RIS-assisted communication systems in green wireless communications, energy
efficiency (EE) has recently received intensive research interest as an
optimization target. However, most previous works have ignored the different
power consumption between ON and OFF states of the PIN diodes attached to each
RIS element. This oversight results in extensive unnecessary power consumption
and reduction of actual EE due to the inaccurate power model. To address this
issue, in this paper, we first utilize a practical power model for a
RIS-assisted multi-user multiple-input single-output (MU-MISO) communication
system, which takes into account the difference in power dissipation caused by
ON-OFF states of RIS's PIN diodes. Based on this model, we formulate a more
accurate EE optimization problem. However, this problem is non-convex and has
mixed-integer properties, which poses a challenge for optimization. To solve
the problem, an effective alternating optimization (AO) algorithm framework is
utilized to optimize the base station and RIS beamforming precoder separately.
To obtain the essential RIS beamforming precoder, we develop two effective
methods based on maximum gradient search and SDP relaxation respectively.
Theoretical analysis shows the exponential complexity of the original problem
has been reduced to polynomial complexity. Simulation results demonstrate that
the proposed algorithm outperforms the existing ones, leading to a significant
increase in EE across a diverse set of scenarios.
