In this paper, a deep learning approach is used to conduct an in‐depth study and analysis of
intelligent resource allocation in wireless communication networks. Firstly, the concepts …

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 …

The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge
demand of users for wireless data traffic. But the mainstream optimization algorithms have …

Deep learning (DL) based resource allocation (RA) has recently gained a lot of attention due
to its performance efficiency. However, most of the related studies assume an ideal case …

The growing number of complex and heterogeneous nodes and base station applications
has required a high computational complexity to handle wireless resources. To tackle this …

In this article, a joint task, spectrum, and transmit power allocation problem is investigated for
a wireless network in which the base stations (BSs) are equipped with mobile-edge …

In this paper, we introduce an efficient user-association and bandwidth-allocation scheme
based on an actor-critic deep learning framework for downlink data transmission in dense …

Multi-Agent Deep Reinforcement Learning (MADRL) has been widely utilized in numerous
next-generation wireless network functions, such as resource allocation in both centralized …

In the past few years, satellite communications have greatly affected our daily lives, and the
integrated terrestrial-satellite network can combine the advantages of satellite and base …

The rich mobile data and edge computing enabled wireless networks motivate to deploy
artificial intelligence (AI) at network edge, known as edge AI, which integrates wireless …