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Hunger marketing and Blockchain Technology: Applications in Wireless Spectrum Management
Njoku Judith Nkechinyere(운저구 주디스 인게친녜레),Manuel Eugenio Morocho-Cayamce(마누엘 에우제니오 모로초 카얌셀라),Wansu Lim(임완수) 한국통신학회 2019 한국통신학회 학술대회논문집 Vol.2019 No.11
Due to the ever-increasing demand by bandwidth-hungry mobile applications and the prevalent growth in wireless communication, effective spectrum management continues to constitute an important issue. So many spectrum management techniques have been employed in different areas including broadband satellite systems, cognitive acoustic networks, railway cognitive radio networks, and smart grid network environments. Spectrum management mechanisms have evolved to meet the different requirements of increasing spectrum use efficiency. In this paper, we discuss two state of the art approaches for spectrum management: Hunger marketing and Blockchain technology. We summarize the pros and cons of these technologies and their application in spectrum management.
Predicting target data rates for dynamic spectrum allocation using Gaussian process regression
Judith Nkechinyere Njoku,유제니오,Angela Caliwag,Pei Xiao,Wansu Lim 한국통신학회 2022 ICT Express Vol.8 No.2
Issues in spectrum allocation between wireless network users have arisen due to the fast increase in the number of broadband services. Such issues include the failure to maximize the performance of all users by considering only a particular category of users. Specifically, a previously adopted selfish algorithm for spectrum allocation considers only the performance of the weakest user. To resolve this issue, we propose a new target data rate setting algorithm for dynamic spectrum allocation. In this algorithm, a Gaussian process regression model is trained to predict the target data rate. All users that perform below the defined target rate will have their frequency band allocations changed to one that guarantees a better performance. Through simulations, we show that the maximum data rate achieved by the weakest user in our algorithm is 121.7% higher than the selfish algorithm.
Efficient Deep Learning Model for Data-Limited Modulation Recognition
Judith Nkechinyere Njoku(주디스),Angela Caliwag(안젤라),Wansu Lim(임완수) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
Deep learning (DL) has been successfully applied for modulation recognition tasks. Most of the existing applications pay little attention to the volume of data used. In reality, real historic data for modulation recognition could be limited. Thus it is better to train recognition models on small amounts of data. This is however a huge challenge, since the performance of DL models depend on sufficient data. In this paper, we introduce an efficient system model based on CNN and different data augmentation methods, for the purpose of modulation recognition. Our system employs random rotation, flip, zoom, random shift and resize methods for data augmentation. The CNN model employs small filter sizes, pooling layers, and dropout layers to improve the network. We apply a small dataset consisting of 40 constellation images per modulation type, to the system. We further analyze the performance based on three data augmentation intervals. From our experiments, the model achieved an accuracy of 32% without data augmentation and 76.07%, 35.71% and 96.42% on the three data-augmentation intervals.
Real-time Deep Learning-based Scene Recognition Model For Metaverse Applications
Judith Nkechinyere Njoku,Amaizu Gabriel Chukwunonso,Jae-Min Lee,Dong-Seong Kim 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
Scene recognition is a type of image recognition task essential to the success of Metaverse applications. Previous works have focused on Scene recognition tasks for real-world environments. However, it is crucial to establish scene recognition models that can be applied in Metaverse applications. This paper applied two Convolutional Neural Networks (CNN) models: SimpleNet and AlexNet, to automatically recognize Virtual scenes. The models are trained on the Scene15 dataset of real-world scenes and tested on virtual-world scenes. The models achieved a test recognition accuracy of 50.96% and 78.08%, and a test time of 50.00ms and 10.00ms respectively on 7 different categories of virtual scenes.
Predicting target data rates for dynamic spectrum allocation using Gaussian process regression
Judith Nkechinyere Njoku,Manuel Eugenio Morocho-Cayamce,Angela Caliwag,Pei Xiao,Wansu Lim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
Issues in spectrum allocation between wireless network users have arisen due to the fast increase in the number of broadband services. Such issues include the failure to maximize the performance of all users, by considering only a particular category of users. Specifically, a previously adopted selfish algo-rithm for spectrum allocation considers only the performance of the weakest user. To resolve this issue, we propose a new target data rate setting algorithm for dynamic spectrum allocation. In this algorithm, a Gaussian process regression model is trained to predict the target data rate. All users that perform below the defined target rate, will have their frequency band allocations changed to one that guarantees a better performance. Through simulations, we show that the maximum data rate achieved by the weakest user in our algorithm is 1217% higher than the selfish algorithm.