[HTML][HTML] From 5G to 6G—challenges, technologies, and applications
AI Salameh, M El Tarhuni - Future Internet, 2022 - mdpi.com
As the deployment of 5G mobile radio networks gains momentum across the globe, the
wireless research community is already planning the successor of 5G. In this paper, we …
wireless research community is already planning the successor of 5G. In this paper, we …
A survey on how network simulators serve reinforcement learning in wireless networks
S Ergun, I Sammour, G Chalhoub - Computer Networks, 2023 - Elsevier
Rapid adoption of mobile devices, coupled with the increase in prominence of mobile
applications and services, resulted in unprecedented infrastructure requirements for mobile …
applications and services, resulted in unprecedented infrastructure requirements for mobile …
Target localization using multi-agent deep reinforcement learning with proximal policy optimization
Target localization refers to identifying a target location based on sensory data readings
gathered by sensing agents (robots, UAVs), surveying a certain area of interest. Existing …
gathered by sensing agents (robots, UAVs), surveying a certain area of interest. Existing …
[HTML][HTML] Cache in fog computing design, concepts, contributions, and security issues in machine learning prospective
The massive growth of diversified smart devices and continuous data generation poses a
challenge to communication architectures. To deal with this problem, communication …
challenge to communication architectures. To deal with this problem, communication …
Predicting trends and research patterns of smart cities: A semi-automatic review using latent dirichlet allocation (LDA)
Smart cities are a current worldwide topic requiring much scientific investigation. This
research instigates the necessity of an organized review to a heedful insight of the research …
research instigates the necessity of an organized review to a heedful insight of the research …
[HTML][HTML] Task offloading based on LSTM prediction and deep reinforcement learning for efficient edge computing in IoT
In IoT (Internet of Things) edge computing, task offloading can lead to additional
transmission delays and transmission energy consumption. To reduce the cost of resources …
transmission delays and transmission energy consumption. To reduce the cost of resources …
Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems
The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G)
networks due to dense connectivity, ultrareliability, low latency, and high throughput …
networks due to dense connectivity, ultrareliability, low latency, and high throughput …
[HTML][HTML] Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview
Recent advances in sensor technology are expected to lead to a greater use of wireless
sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances …
sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances …
Review on Machine Learning for Intelligent Routing, Key Requirement and Challenges Towards 6G
B Langpoklakpam, LK Murry - Computer Networks and …, 2023 - ojs.wiserpub.com
The constant desire for faster data rates, lower latency, improved reliability, global device
integration, and pervasiveness are some of the factors driving the development of next …
integration, and pervasiveness are some of the factors driving the development of next …
Data-Driven Techniques and Security Issues in Wireless Networks
The data-Driven Technique is a method that employs real-world data to acquire insight into
system behavior. It allows for the examination of small, simple systems as well as large …
system behavior. It allows for the examination of small, simple systems as well as large …