Nov 11, 2022 · In particular, we minimize the number of fiber-connected nodes in terms of hop-constraint, considering blockage and channel conditions in the ...

Ultra-Dense 5G Network Deployment Strategy: A. Reinforcement Learning Approach. Biswa P. S. Sahoo∗, Prerit Jain∗, Dheeraj Kumar∗, Satya Kumar Vankayala ...

In this paper, we focus on Wireless Honeypots (WHs) in ultra-dense networks. In particular, we introduce a strategic honeypot deployment method.

Biswa P. S. Sahoo, Prerit Jain, Dheeraj Kumar, Satya Kumar Vankayala: Ultra-Dense 5G Network Deployment Strategy: A Reinforcement Learning Approach.

Oct 22, 2024 · In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a) e−Greedy and (b) Q− ...

This paper proposed an optimal deployment and allocation strategy for deploying edge servers(ESs) in UDN to minimize the cost of service providers.

Oct 29, 2024 · This study employs the Multi-Agent Reinforcement Learning (MARL) strategy, a cutting-edge machine learning approach that allows multi-agent to ...

Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach.

This paper introduces a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques, to identify the optimal number of honeypots ...

Optimizing Honeypot Deployment in Ultra-Dense Beyond 5G Networks Using Deep Q-Networks: A Novel Reinforcement Learning Strategy. Authors. Vijaya S Rao ...