Results 31 to 40 of about 87,779 (141)
Shared Access Terrestrial-Satellite Backhaul Network enabled by Smart Antennas: SANSA [PDF]
This extended abstract gives an overview of the research activities that will be carried out in the SANSA project, funded by the EU under the H2020 Programme.
Pérez-Neira, Ana Isabel +2 more
core +1 more source
CA‐PPO: Collision‐Aware Reinforcement Learning for Grant‐Free NOMA in Wireless Federated Learning
This paper proposes a collision‐aware proximal policy optimisation (CA‐PPO) framework for joint clustering and power allocation in grant‐free NOMA–enabled wireless federated learning systems. By explicitly accounting for random access collisions, SIC decoding failures and energy–latency constraints, the proposed approach stabilises learning under ...
Emmanuel Atebawone +5 more
wiley +1 more source
Mobile Microgrids: Concepts, Key Technologies, Business Models, Applications and Prospects
Our study explores MMG concepts, key technologies, business models and applications, illustrating their impact on improving the flexible ADN operation. This study emphasises not only the practical importance of MMGs but also their pivotal role in reshaping the future landscape of energy delivery, offering valuable insights for researchers, policymakers
Tao Ding +5 more
wiley +1 more source
Poisoning‐Aware Data Cleansing and Restoration for Secure IoT‐Enabled Wireless Sensor Systems
This article presents a poisoning‐aware data cleansing and restoration framework for secure IoT‐enabled wireless sensor systems, where suspicious sensor readings are treated as potential adversarial data poisoning attempts rather than only benign faults.
Quazi Mamun
wiley +1 more source
Reliable Deep Learning–Assisted Spectrum Sensing for Cognitive Radio and 6G Wireless Networks
Reliable spectrum sensing is a key requirement for cognitive radio (CR) networks; however, its performance is significantly degraded by fading, noise uncertainty, and channel estimation errors. This paper proposes a hybrid gated recurrent unit–long short‐term memory–convolutional neural network (GRU–LSTM–CNN) framework for cooperative spectrum sensing (
Arun Kumar +5 more
wiley +1 more source
This paper proposes a new framework combining distributed collective intelligence and deep neural networks (DNNs) for multiobjective optimization in wireless systems of future generation. This paper mitigates some of those disadvantages by proposing a distributed consensus algorithm that incorporates predictive energy allocation and power‐based ...
Mohammed Aboud Kadhim +3 more
wiley +1 more source
This paper proposes an AI‐based light fidelity (Li‐Fi) and millimeter‐wave (mmWave) architecture to hastily mitigate most of the challenges in vehicle‐to‐vehicle (V2V) communication systems. Through traffic prediction based on machine learning technologies, the system enables proactive resource allocation, as well as intelligent frequency management in
Mohammed Aboud Kadhim +3 more
wiley +1 more source
A Review of the Internet of Things (IoT) Landscape: Technologies, Applications, and Open Challenges
The Internet of Things (IoT) has emerged as a large‐scale ecosystem that integrates sensing, communication, and computation across diverse application domains. Its deployment spans smart homes, wearable devices, industrial automation, and smart cities, where heterogeneous devices are required to operate under strict performance, reliability, and ...
Sidna Jeddou +5 more
wiley +1 more source
This study presents a simulation‐driven digital twin (DT) prototype framework, designed around a wireless sensor network (WSN) style pipeline, to study congestion and vehicular emissions at an urban signalized junction. The architecture follows a sensing‐to‐edge workflow: detector‐like traffic measurements (counts and speeds) are published through an ...
Timothy Dkhar +5 more
wiley +1 more source
We introduce a fully distributed framework that leverages belief‐desire‐intention extended (BDIx) agents on user devices to perform dynamic clustering and access‐point selection in a cell‐free 6G network, using both classical and deep‐learning methods for deterministic, scalable AP grouping.
Iakovos Ioannou +5 more
wiley +1 more source

