In practical content delivery, when the time-frequency resources are limited, it is a
challenging task to satisfy terminals' data demand in a heavy-traffic and mutual-interfered …

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 …

Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) are two
emerging paradigms that enable the feasible and scalable deployment of Virtual Network …

For decades, optimization has played a central role in addressing wireless resource
management problems such as power control and beamformer design. However, these …

Sixth-generation (6G) communication networks will fulfill users' requests for high data
speeds and low latency without causing network outages throughout the world. However …

In this paper, we address the problem of resource allocation (RA) in wireless communication
networks, where each user has a dynamic data rate constraint. The objective of RA is to …

Resource allocation is of great importance in the next generation wireless communication
systems, especially for cognitive radio networks (CRNs). Many resource allocation strategies …

This paper investigates the satellite communication network (SCN) to optimize the channel
allocation of the fixed ground base station and the transmission power allocation of the Low …

Edge content caching has emerged as a promising solution against network latency by pre-
caching popular contents at the edge of networks. However, the edge content caching and …

With the great success of deep learning (DL) in image classification, speech recognition,
and other fields, more and more studies have applied various neural networks (NNs) to …