Power Constraint

Power constraint optimization in wireless communication networks focuses on efficiently allocating transmit power to maximize network utility or minimize error, while adhering to individual device limitations. Current research emphasizes learning-based approaches, employing actor-critic algorithms, alternating optimization, and deep unfolding of classical methods like successive concave approximation, often incorporating graph convolutional networks to leverage network topology information. These advancements aim to improve the performance of various applications, including over-the-air computation and federated learning, by enabling more efficient and robust communication under power limitations.