Transfer learning for autonomous cell activation based on relational reinforcement learning with adaptive reward
G Sun, D Ayepah-Mensah, R Xu… - IEEE Systems …, 2021 - ieeexplore.ieee.org
With the increasing threat of global warming due to high energy consumption of wireless
network infrastructure, cell activation complements the capabilities of next-generation …
network infrastructure, cell activation complements the capabilities of next-generation …
Deep reinforcement learning for communication flow control in wireless mesh networks
Wireless mesh network (WMN) is one of the most promising technologies for Internet of
Things (IoT) applications because of its self-adaptive and self-organization nature. To meet …
Things (IoT) applications because of its self-adaptive and self-organization nature. To meet …
RayNet: A simulation platform for developing reinforcement learning-driven network protocols
L Giacomoni, B Benny, G Parisis - arXiv preprint arXiv:2302.04519, 2023 - arxiv.org
Reinforcement Learning (RL) has gained significant momentum in the development of
network protocols. However, RL-based protocols are still in their infancy, and substantial …
network protocols. However, RL-based protocols are still in their infancy, and substantial …
Relational deep reinforcement learning for routing in wireless networks
While routing in wireless networks has been studied extensively, existing protocols are
typically designed for a specific set of network conditions and so do not easily accommodate …
typically designed for a specific set of network conditions and so do not easily accommodate …
Rlops: Development life-cycle of reinforcement learning aided open ran
Radio access network (RAN) technologies continue to evolve, with Open RAN gaining the
most recent momentum. In the O-RAN specifications, the RAN intelligent controllers (RICs) …
most recent momentum. In the O-RAN specifications, the RAN intelligent controllers (RICs) …
Applications of Deep Learning and Deep Reinforcement Learning in 6G Networks
TH Nguyen, H Park, K Seol, S So… - … on Ubiquitous and …, 2023 - ieeexplore.ieee.org
As the demand for data-driven applications and emerging technologies such as extended
reality, autonomous vehicles, and the Internet of Things (IoT) continues to grow, the …
reality, autonomous vehicles, and the Internet of Things (IoT) continues to grow, the …
Towards multi-agent reinforcement learning for wireless network protocol synthesis
This paper proposes a multi-agent reinforcement learning based medium access framework
for wireless networks. The access problem is formulated as a Markov Decision Process …
for wireless networks. The access problem is formulated as a Markov Decision Process …
Challenges of real-world reinforcement learning
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …
beginning to show some successes in real-world scenarios. However, much of the research …
Adaptive wireless network management with multi-agent reinforcement learning
Wireless networks are trending towards large scale systems, containing thousands of nodes,
with multiple co-existing applications. Congestion is an inevitable consequence of this scale …
with multiple co-existing applications. Congestion is an inevitable consequence of this scale …
AIF: An artificial intelligence framework for smart wireless network management
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 …
management using a single universal algorithm, this letter proposes a universal learning …