Efficient resource prediction framework for software-defined heterogeneous radio environmental infrastructures |
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| Affiliation: | 1. Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain;2. Dpto. de Ingeniería Mecánica y Construcción, Universitat Jaume I, Castellón, Spain;3. School of Engineering Technology, Purdue University, West Lafayette, IN, USA;1. Maritime Intelligent Transportation Research Group, Navigation College, Dalian Maritime University, Dalian 116026, China;2. College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China;3. Liverpool Logistics, Offshore and Marine Research Institute, Liverpool John Moores University, Liverpool L3 3AF, UK;1. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, United States;2. School of Industrial and Systems Engineering, Atlanta, GA 30332, United States;1. Department of Mechanical Engineering, Tianjin University, Tianjin 300350, China;2. Department of Mechanical Engineering, Tianjin Ren’ai College, Tianjin 301636, China;3. Institute of Advance Design and Manufacturing, Southwest Jiaotong University, Chengdu 610031, China;1. Department of Electrical and Computer Engineering, University of Houston, 4226 Martin Luther King Blvd., Houston, TX 77204, USA;2. Department of Civil and Environmental Engineering, University of Houston, 4226 Martin Luther King Blvd., Houston, TX 77204, USA;1. Sakarya University Institute of Natural Sciences Industrial Engineering Department, Sakarya, Türkiye;2. Sakarya University Industrial Engineering Department, Sakarya, Türkiye;3. Amity University/ Department of Mechanical Engineering, Noida, India;4. Sakarya University of Applied Sciences, Faculty of Applied Sciences, International Trade and Finance Department, Sakarya, Türkiye;5. Sakarya University of Applied Sciences, AI Research and Application Center, Sakarya, Türkiye |
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| Abstract: | Artificial Intelligence (AI) is defining the future of next-generation infrastructures as proactive and data-driven systems. AI-empowered radio systems are replacing the existing command and control radio networks due to their intelligence and capabilities to adapt to the radio environmental infrastructures that include intelligent networks, smart cities and AV/VR enabled factory premises or localities. An efficient resource prediction framework (ERPF) is proposed to provide proactive knowledge about the availability of radio resources in such software-defined heterogeneous radio environmental infrastructures (SD-HREIs). That prior information enables the coexistence of radio users in SD-HREIs. In a proposed framework, the radio activity is measured in both the unlicensed bands that include 2.4 and 5 GHz, respectively. The clustering algorithms k- means and DBSCAN are implemented to segregate the already measured radioactivity as signal (radio occupancy) and noise (radio opportunity). Machine learning techniques CNN and LRN are then trained and tested using the segregated data to predict the radio occupancy and radio opportunity in SD-HREIs. Finally, the performance of CNN and LRN is validated using the cross-validation metrics. |
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| Keywords: | SDH-REIs DBSCAN K-means Convolutional neural network Layer recurrent network Cross validation |
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