There has been a growing interest in developing data-driven, and in particular deep neural
network (DNN) based methods for modern communication tasks. These methods achieve …

There has been a growing interest in developing data-driven, in particular deep neural
network (DNN) based methods for modern communication tasks. For a few popular tasks …

Improving learning efficiency is paramount for learning resource allocation with deep neural
networks (DNNs) in wireless communications over highly dynamic environments …

This paper considers the design of optimal resource allocation policies in wireless
communication systems, which are generically modeled as a functional optimization …

The fifth generation (5G) of wireless communications has led to many advancements in
technologies such as large and distributed antenna arrays, ultra-dense networks, software …

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

This paper proposes a paradigm of uncertainty injection for training deep learning model to
solve robust optimization problems. The majority of existing studies on deep learning focus …

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

Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard
wireless resource allocation problems. However, in the presence of intricate constraints, eg …

Efficient radio resource allocation is a fundamental optimization problem for wireless
networks, and has been widely studied in the past. However, wireless systems are evolving …