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

With the superior capability of discovering intricate structure of large data sets, deep learning
has been widely applied in various areas including wireless networking. While existing deep …

Data-driven machine learning approaches have been proposed to facilitate wireless
network optimization by learning latent knowledge from historical optimization instances …

With the growth of mobile technology in the last decade, wireless networks have become an
integral part of our everyday lives. To meet the increasingly stringent application …

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 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 …

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 the context of heterogeneous networks, users with multi-mode terminals can connect to
different radio access technologies such as 802.16, 802.11, HSPA and LTE at the same …

A large amount of research focuses on experimentally optimizing the performance of
wireless solutions. Finding the optimal performance settings typically requires investigating …

The last decade has seen significant advances in optimization-based resource allocation
and control approaches for wireless networks. However, the existing work suffer from poor …