Browsing by Subject "Reinforcement learning"
Now showing items 1-20 of 32
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A cooperative Q-learning approach for distributed resource allocation in multi-user femtocell networks
( Institute of Electrical and Electronics Engineers Inc. , 2016 , Conference Paper)This paper studies distributed interference management for femtocells that share the same frequency band with macrocells. We propose a multi-agent learning technique based on distributed Q-learning, called subcarrier-based ... -
A Deep Reinforcement Learning Framework for Data Compression in Uplink NOMA-SWIPT Systems
( Institute of Electrical and Electronics Engineers Inc. , 2021 , Article)<comment< Non-orthogonal multiple access (NOMA) shall play an important role in the current and foreseeable design of 5G and beyond networks. NOMA allows multiple users to share the same time-frequency ... -
A Unified Framework for Differentiated Services in Intelligent Healthcare Systems
( IEEE , 2022 , Article)The Coronavirus disease 2019 (COVID-19) outbreak continues to significantly expose the vulnerabilities of healthcare systems around the world. These unprecedented circumstances create an opportunity for improving healthcare ... -
Can automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks
( Elsevier , 2022 , Article)Pedestrian distraction may provoke severe difficulties in automated vehicle (AV) control, which may significantly affect the safety performance of AVs, especially at unsignalized mid-block crosswalks (UMCs). However, there ... -
Closed-loop control of anesthesia and mean arterial pressure using reinforcement learning
( IEEE , 2014 , Conference Paper)General anesthesia is required for patients undergoing surgery as well as for some patients in the intensive care units with acute respiratory distress syndrome. How-ever, most anesthetics affect cardiac and respiratory ... -
Data-Driven Load Frequency Control Based on Multi-Agent Reinforcement Learning With Attention Mechanism
( Institute of Electrical and Electronics Engineers Inc. , 2022 , Article)With the massive penetration of renewable energy, traditional reinforcement learning algorithms suffer from slow convergence and area control error (ACE) in interconnected power systems. This paper proposes data-driven ... -
Deep Reinforcement Learning Algorithm for Smart Data Compression under NOMA-Uplink Protocol
( Institute of Electrical and Electronics Engineers Inc. , 2020 , Conference Paper)One of the highly promising radio access strategies for enhancing performance in the next generation cellular communications is non-orthogonal multiple access (NOMA). NOMA offers a number of advantages including better ... -
Deep Reinforcement Learning for Network Selection over Heterogeneous Health Systems
( IEEE Computer Society , 2022 , Article)Smart health systems improve our quality oflife by integrating diverse information and technologies into health and medical practices. Such technologies can significantly improve the existing health services. However, ... -
Distributed interference management using Q-Learning in cognitive femtocell networks: New USRP-based implementation
( Institute of Electrical and Electronics Engineers Inc. , 2015 , Conference Paper)Femtocell networks have become a promising solution in supporting high data rates for 5G systems, where cell densification is performed using the small femtocells. However, femtocell networks have many challenges. One of ... -
DRL-HEMS: Deep Reinforcement Learning Agent for Demand Response in Home Energy Management Systems Considering Customers and Operators Perspectives
( Institute of Electrical and Electronics Engineers Inc. , 2022 , Article)With the smart grid and smart homes development, different data are made available, providing a source for training algorithms, such as deep reinforcement learning (DRL), in smart grid applications. These algorithms allowed ... -
Energy-efficient networks selection based deep reinforcement learning for heterogeneous health systems
( Institute of Electrical and Electronics Engineers Inc. , 2021 , Conference Paper)Smart health systems improve the existing health services by integrating information and technology into health and medical practices. However, smart healthcare systems are facing major challenges including limited network ... -
I-SEE: Intelligent, Secure, and Energy-Efficient Techniques for Medical Data Transmission Using Deep Reinforcement Learning
( Institute of Electrical and Electronics Engineers Inc. , 2021 , Article)The rapid evolution of remote health monitoring applications is foreseen to be a crucial solution for facing an unpredictable health crisis and improving the quality of life. However, such applications come with many ... -
Intrusion response systems for cyber-physical systems: A comprehensive survey
( Elsevier , 2023 , Article Review)Cyberattacks on Cyber-Physical Systems (CPS) are on the rise due to CPS increased networked connectivity and may cause costly environmental hazards as well as human and financial loss. This necessitates designing and ... -
LEARNING-BASED CONTROL OF CANCER CHEMOTHERAPY TREATMENT
( Elsevier B.V. , 2017 , Article)The increasing threat of cancer to human life and the improvement in survival rate of this disease due to effective treatment has promoted research in various related fields. This research has shaped clinical trials and ... -
MMRL: A Multi-Modal Reinforcement Learning Technique for Energy-efficient Medical IoT Systems
( IEEE , 2021 , Conference Paper)The Internet of Medical Things (IoMT) couples the rapid growth of Internet of things (IoT) technologies with smart health systems, leveraging wireless battery-operated devices for remote health monitoring. Since 2019, a ... -
On Designing Smart Agents for Service Provisioning in Blockchain-powered Systems
( IEEE Computer Society , 2021 , Article)Service provisioning systems assign users to service providers according to allocation criteria that strike an optimal trade-off between users Quality of Experience (QoE) and the operation cost endured by providers. These ... -
Optimal adaptive control of drug dosing using integral reinforcement learning
( Elsevier Inc. , 2019 , Article)In this paper, a reinforcement learning (RL)-based optimal adaptive control approach is proposed for the continuous infusion of a sedative drug to maintain a required level of sedation. To illustrate the proposed method, ... -
Optimal cooperative cognitive relaying and spectrum access for an energy harvesting cognitive radio: Reinforcement learning approach
( Institute of Electrical and Electronics Engineers Inc. , 2015 , Conference Paper)In this paper, we consider a cognitive setting under the context of cooperative communications, where the cognitive radio (CR) user is assumed to be a self-organized relay for the network. The CR user and the primary user ... -
Rational Contracts: Data-driven Service Provisioning in Blockchain-powered Systems
( Institute of Electrical and Electronics Engineers Inc. , 2021 , Conference Paper)Smart Contracts (SCs), which are software programs that run on blockchain platforms, provide appealing security guarantees characterized by decentralized, autonomous, and verifiable execution. On the other hand, Service ... -
Real-Time Scheduling for Electric Vehicles Charging/Discharging Using Reinforcement Learning
( Institute of Electrical and Electronics Engineers Inc. , 2020 , Conference Paper)With the increase in Electric Vehicles (EVs) penetration, their charging needs form an additional burden on the grid. Thus, charging coordination is necessary for safe and efficient EV use. The scheduling of EVs is especially ...