Related papers: Dueling DDQN-Based Adaptive Multi-Objective Handov…
Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue policy learning---termed NDQN, and apply it to an…
Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein,…
Low earth orbit (LEO) satellite network can complement terrestrial networks for achieving global wireless coverage and improving delay-sensitive Internet services. This paper proposes an integrated satellite-terrestrial network (ISTN)…
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we consider the scenario with discrete-continuous hybrid action…
With the rapid development of sixth-generation (6G) communication technology, global communication networks are moving towards the goal of comprehensive and seamless coverage. In particular, low earth orbit (LEO) satellites have become a…
Unmanned aerial vehicles (UAVs) are increasingly deployed to provide wireless connectivity to static and mobile ground users in situations of increased network demand or points of failure in existing terrestrial cellular infrastructure.…
In satellite constellation-based communication systems, continuous user coverage requires frequent handoffs due to the dynamic topology induced by the Low Earth Orbit (LEO) satellites. Each handoff between a satellite and ground users…
Unmanned aerial vehicle (UAV)-assisted communications is a promising solution to improve the performance of future wireless networks, where UAVs are deployed as base stations for enhancing the quality of service (QoS) provided to ground…
This paper studies the potential performance improvement that can be achieved by enabling multi-operator wireless connectivity for cloud/fog computing-connected vehicular systems. Mobile network operator (MNO) selection and switching…
The model-driven power allocation (PA) algorithms in the wireless cellular networks with interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays, the data-driven model-free machine learning-based approaches…
Transmission switching is a well-established approach primarily applied to minimize operational costs through strategic network reconfiguration. However, exclusive focus on cost reduction can compromise system reliability. While…
End-to-end routing in Low Earth Orbit (LEO) satellite constellations (LSatCs) is a complex and dynamic problem. The topology, of finite size, is dynamic and predictable, the traffic from/to Earth and transiting the space segment is highly…
Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep reinforcement learning for routing in AANETs aiming at minimizing the end-to-end (E2E) delay.…
This letter presents a deep reinforcement learning (DRL) approach for transmission design to optimize the energy efficiency in vehicle-to-vehicle (V2V) communication links. Considering the dynamic environment of vehicular communications,…
In recent years, with the large-scale deployment of space spacecraft entities and the increase of satellite onboard capabilities, delay/disruption tolerant network (DTN) emerged as a more robust communication protocol than TCP/IP in the…
In reinforcement learning, it is often difficult to automate high-dimensional, rapid decision-making in dynamic environments, especially when domains require real-time online interaction and adaptive strategies such as web-based games. This…
In this work we present a method for using Deep Q-Networks (DQNs) in multi-objective environments. Deep Q-Networks provide remarkable performance in single objective problems learning from high-level visual state representations. However,…
The use of higher frequencies in mobile communication systems leads to smaller cell sizes, resulting in the deployment of more base stations and an increase in handovers to support user mobility. This can lead to frequent radio link…
Internet of things, supported by machine-to-machine (M2M) communications, is one of the most important applications for future 6th generation (6G) systems. A major challenge facing by 6G is enabling a massive number of M2M devices to access…
Double Q-learning is a classical control algorithm that mitigates the maximization bias of Q-learning. To do so, it explicitly trains two independent action-value functions and uses them to decouple action-selection and action-evaluation…