Related papers: A Q-learning-based QoS-aware multipath routing pro…
To satisfy the increasing demand for multimedia services in broadband Internet networks, the WiMAX (Worldwide Interoperability for Microwave Acces) technology has emerged as an alternative to the wired broadband access solutions. It…
Routing in multi-hop wireless networks is a complex problem, especially in heterogeneous networks where multiple wireless communication technologies coexist. Reinforcement learning (RL) methods, such as Q-learning, have been introduced for…
In this work, a new region-based, multipath-enabled packet routing is presented and called SmartPacket Routing. The proposed approach provides several opportunities to re-distribute the smartness and decision making among various elements…
Quantum machine learning (QML) networks promise to have some computational (or quantum) advantage for classifying supervised datasets (e.g., satellite images) over some conventional deep learning (DL) techniques due to their expressive…
The Mixture-of-Experts (MoE) architecture has emerged as a powerful paradigm for scaling deep learning models, yet it is fundamentally limited by challenges such as expert imbalance and the computational complexity of classical routing…
Our objective is to provide guaranteed packet delivery service in time constrained sensor networks. The wireless network is a highly variable environment, where available link bandwidth may vary with network load. Since multimedia…
Bluetooth Low Energy (BLE) Mesh is widely recognized as a driver technology for IoT applications. However, the lack of quality of service (QoS) in BLE Mesh, represented by packet prioritization, significantly limits its potential. This work…
A mobile ad hoc network (MANET) is a wireless network that uses multi-hop peer-to- peer routing instead of static network infrastructure to provide network connectivity. MANETs have applications in rapidly deployed and dynamic military and…
Quality of Service (QoS) techniques are applied in IP networks to utilize available network resources in the most efficient manner to minimize delays and delay variations (jitters) in network traffic having multiple type of services.…
Quantum Machine Learning (QML) is considered one of the most promising applications of Quantum Computing in the Noisy Intermediate Scale Quantum (NISQ) era for the impact it is thought to have in the near future. Although promising…
In this study, we develop a resource management framework for a quantum virtual private network (qVPN), which involves the sharing of an underlying public quantum network by multiple organizations for quantum entanglement distribution. Our…
The innovation of Wi-Fi 6, IEEE 802.11ax, was be approved as the next sixth-generation (6G) technology of wireless local area networks (WLANs) by improving the fundamental performance of latency, throughput, and so on. The main technical…
Efficient energy management is essential in Wireless Sensor Networks (WSNs) to extend network lifetime and ensure reliable data transmission. This paper presents a novel method using reinforcement learning-based cluster-head selection and a…
Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update,…
Efficient routing algorithms based on vehicular ad hoc networks (VANETs) play an important role in emerging intelligent transportation systems. This highly dynamic topology faces a number of wireless communication service challenges. In…
As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make…
The rapidly growing traffic demands in fiber-optical networks require flexibility and accuracy in configuring lightpaths, for which fast and accurate quality of transmission (QoT) estimation is of pivotal importance. This paper introduces a…
Quantum Machine Learning (QML) is an accelerating field of study that leverages the principles of quantum computing to enhance and innovate within machine learning methodologies. However, Noisy Intermediate-Scale Quantum (NISQ) computers…
We introduce Qlustering, a quantum-inspired algorithm for unsupervised learning that leverages network-based quantum transport to perform data clustering. In contrast to traditional distance-based methods, Qlustering treats the steady-state…
In cognitive radio (CR) networks, the perceived reduction of application layer quality of service (QoS), such as multimedia distortion, by secondary users may impede the success of CR technologies. Most previous work in CR networks ignores…