  Simultaneously transmitting and reflecting reconfigurable intelligent
surfaces (STAR-RISs) is a promising passive device that contributes to a
full-space coverage via transmitting and reflecting the incident signal
simultaneously. As a new paradigm in wireless communications, how to analyze
the coverage and capacity performance of STAR-RISs becomes essential but
challenging. To solve the coverage and capacity optimization (CCO) problem in
STAR-RIS assisted networks, a multi-objective proximal policy optimization
(MO-PPO) algorithm is proposed to handle long-term benefits than conventional
optimization algorithms. To strike a balance between each objective, the MO-PPO
algorithm provides a set of optimal solutions to form a Pareto front (PF),
where any solution on the PF is regarded as an optimal result. Moreover, in
order to improve the performance of the MO-PPO algorithm, two update
strategies, i.e., action-value-based update strategy (AVUS) and loss
function-based update strategy (LFUS), are investigated. For the AVUS, the
improved point is to integrate the action values of both coverage and capacity
and then update the loss function. For the LFUS, the improved point is only to
assign dynamic weights for both loss functions of coverage and capacity, while
the weights are calculated by a min-norm solver at every update. The numerical
results demonstrated that the investigated update strategies outperform the
fixed weights MO optimization algorithms in different cases, which includes a
different number of sample grids, the number of STAR-RISs, the number of
elements in the STAR-RISs, and the size of STAR-RISs. Additionally, the
STAR-RIS assisted networks achieve better performance than conventional
wireless networks without STAR-RISs. Moreover, with the same bandwidth,
millimeter wave is able to provide higher capacity than sub-6 GHz, but at a
cost of smaller coverage.
