  Coverage and capacity are the important metrics for performance evaluation in
wireless networks, while the coverage and capacity have several conflicting
relationships, e.g. high transmit power contributes to large coverage but high
inter-cell interference reduces the capacity performance. Therefore, in order
to strike a balance between the coverage and capacity, a novel model is
proposed for the coverage and capacity optimization of simultaneously
transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs)
assisted networks. To solve the coverage and capacity optimization (CCO)
problem, a machine learning-based multi-objective optimization algorithm, i.e.,
the multi-objective proximal policy optimization (MO-PPO) algorithm, is
proposed. In this algorithm, a loss function-based update strategy is the core
point, which is able to calculate weights for both loss functions of coverage
and capacity by a min-norm solver at each update. The numerical results
demonstrate that the investigated update strategy outperforms the fixed
weight-based MO algorithms.
