Blar i Faculty of Engineering and Science på forfatter "Jaiswal, Rahul Kumar"
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Data-driven Transfer Learning Methods for Wireless Networks
Jaiswal, Rahul Kumar (Doctoral dissertations at University of Agder; no. 475, Doctoral thesis, 2024)Radio maps provide information about spatial signal strength and network coverage in a designated geographical area. The estimation of accurate radio maps is necessary to improve the performance of many applications of ... -
Deep Transfer Learning Based Radio Map Estimation for Indoor Wireless Communications
Jaiswal, Rahul Kumar; Elnourani, Mohamed; Deshmukh, Siddharth; Beferull-Lozano, Baltasar (Chapter; Peer reviewed, 2022)This paper investigates the problem of transfer learning in radio map estimation for indoor wireless communications, which can be exploited for different applications, such as channel modelling, resource allocation, network ... -
Implicit Wiener Filtering for Speech Enhancement In Non-Stationary Noise
Jaiswal, Rahul Kumar; Romero, Daniel (Chapter, 2021) -
Location-free Indoor Radio Map Estimation using Transfer learning
Jaiswal, Rahul Kumar; Elnourani, Mohamed; Deshmukh, Siddharth; Beferull-Lozano, Baltasar (Chapter; Peer reviewed, 2023)Accurate estimation of radio maps is important for various applications of wireless communications, such as network planning, and resource allocation. To learn accurate radio map models, one needs to have accurate knowledge ... -
Single-channel speech enhancement using implicit Wiener filter for high-quality speech communication
Jaiswal, Rahul Kumar; Yeduri, Sreenivasa Reddy; Cenkeramaddi, Linga Reddy (Peer reviewed; Journal article, 2022)Speech enables easy human-to-human communication as well as human-to-machine interaction. However, the quality of speech degrades due to background noise in the environment, such as drone noise embedded in speech during ... -
Transfer Learning Based Joint Resource Allocation for Underlay D2D Communications
Jaiswal, Rahul Kumar; Deshmukh, Siddharth; Elnourani, Mohamed; Beferull-Lozano, Baltasar (Chapter; Peer reviewed, 2022)In this paper, we investigate the application of transfer learning to train a Deep Neural Network (DNN) model for joint channel and power allocation in underlay device-todevice (D2D) communication. Based on the traditional ...