To solve the policy optimizing problem in many scenarios of smart wireless network
management using a single universal algorithm, this letter proposes a universal learning …

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 …

There is a growing interest in applying deep reinforcement learning (DRL) methods to
optimizing the operation of wireless networks. In this paper, we compare three state of the art …

With the widespread deployment of wireless technologies and IoT, 5G wireless networks will
support various communication connectivity and services for the huge number of wireless …

Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …

The recent development of reinforcement learning (RL) has boosted the adoption of online
RL for wireless radio resource management (RRM). However, online RL algorithms require …

Driven by the soaring traffic demand and the growing diversity of mobile services, wireless
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …

Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent
years for future wireless networks. They can overcome the ever-increasing challenges of …

Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in
both industry and academia. A common solution is to replace partial or even all modules in …

The concept of using big data (BD) for wireless communication network optimization is no
longer new. However, previous work has primarily focused on long-term policies in the …