Resource allocation and transceivers in wireless networks are usually designed by solving
optimization problems subject to specific constraints, which can be formulated as variable or …

In this paper, we investigate a deep learning (DL) approach to solve a generic constrained
optimization problem in wireless networks, where the objective and constraint functions can …

It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …

Deep neural networks (DNNs) have been introduced for designing wireless policies by
approximating the mappings from environmental parameters to solutions of optimization …

Deep learning based on artificial neural networks (ANNs) is a powerful machine-learning
method that, in recent years, has been successfully used to realize tasks such as image …

In the conventional approaches using reinforcement learning (RL) for resource allocation in
wireless networks, the structure of the policy depends on network circumstances such as the …

With the development of wireless networks, the scale of network optimization problems is
growing correspondingly. While algorithms have been designed to reduce complexity in …

In this paper, we study radio and energy resource management in renewable energy-
powered wireless networks, where base stations (BSs) are powered by both on-grid and …

Resource allocation in wireless networks, such as device-to-device (D2D) communications,
is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are …

Edge artificial intelligence will empower the ever simple industrial wireless networks (IWNs)
supporting complex and dynamic tasks by collaboratively exploiting the computation and …