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Towards software-defined tactical networks: Experiments and challenges for control overhead
Software-defined Tactical Networks (SDTNs) have the potential to host intelligent
mechanisms to manage ever-changing communication scenarios in networks with …
mechanisms to manage ever-changing communication scenarios in networks with …
Adversarial attacks against reinforcement learning based tactical networks: A case study
Dynamic changes caused by conditions such as challenging terrain or hostile encounters
force tactical networks to be highly adaptable. To tackle this problem, new proposals …
force tactical networks to be highly adaptable. To tackle this problem, new proposals …
Mobility prediction at the tactical edge: A handover for centralized/decentralized networks
This paper presents a mobility prediction model as part of a handover mechanism
combining centralized and decentralized network control to improve performance and …
combining centralized and decentralized network control to improve performance and …
Cooperative agent system for quantifying link robustness in tactical networks
This paper presents a cooperative agent system employing Reinforcement Learning (RL) to
quantify path robustness in tactical networks. We implement an environment builder agent …
quantify path robustness in tactical networks. We implement an environment builder agent …
Coordinating the sdn and tdma planes in software-defined tactical manets with adaptive coding and modulation
In this paper, we investigate a tactical mobile ad hoc network (MANET) enhanced with
software defined networking (SDN) functionality. Radio transmissions of network links are …
software defined networking (SDN) functionality. Radio transmissions of network links are …
Controller Placement and TDMA Link Scheduling in Software Defined Wireless Multihop Networks
In this paper, we iterate on Software Defined wireless Multihop Networks (SDWMNs) and
TDMA-scheduled links, where data and SDN control traffic compete for the same resources …
TDMA-scheduled links, where data and SDN control traffic compete for the same resources …
DRL meets GNN to improve QoS in Tactical MANETs
This paper proposes a hybrid AI model combining Graph Neural Network (GNN) and Deep
Reinforcement Learning (DRL) to improve QoS in modern communication systems deployed …
Reinforcement Learning (DRL) to improve QoS in modern communication systems deployed …
GNN-based Deep Reinforcement Learning with Adversarial Training for Robust Optimization of Modern Tactical Communication Systems
This paper investigates the feasibility of a Graph Neural Network (GNN)-based Deep
Reinforcement Learning (DRL) for tackling complex optimization problems in modern …
Reinforcement Learning (DRL) for tackling complex optimization problems in modern …
[PDF][PDF] Bachelor's Thesis
J Bode - 2023 - rettore.com.br
Reinforcement Learning (RL) is a promising approach for routing in highly dynamic
environments such as Tactical Networks (TNs). However, the use of RL in TNs exposes a …
environments such as Tactical Networks (TNs). However, the use of RL in TNs exposes a …
[PDF][PDF] SOFTANET: Slices, QoS and Resilience in Federated Mission Networks with SDN
M de Graaf, PHL Rettore, D Belabed, A van der Linden… - sto.nato.int
The EDA project SOFTANET has investigated how to apply Software-defined Networking
(SDN) and Network Function Virtualisation (NFV) to achieve flexible and robust military …
(SDN) and Network Function Virtualisation (NFV) to achieve flexible and robust military …