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Manuel Eugenio Morocho Cayamce(마누엘 에우제니오 모로초 카얌셀라),Wansu Lim(임완수) 한국통신학회 2019 한국통신학회 학술대회논문집 Vol.2019 No.6
Vehicle-to-vehicle (V2V) technology generally adopts Dedicated Short-Range Communications (DSRC) to transmit based safety messages (BSMs) e.g., geographical location, braking information, speed, the status of the turn signal, and direction of travel. Specific propagation and wireless communications channel models have been proposed from industry and academic researchers. However, the range of DSRC is limited to a few hundred meters, and it is necessary to employ a multi-hop communication to extend the range of communication, reaching many target vehicles as possible. In this article, we explore the problem of multi-hop connectivity in V2V networks and propose a methodology that consists of two different deep learning (DL) routines. First, two convolutional neural networks (CNN) are created and tuned to segment terrestrial imagery into different environments. The multi-environments are anticipated to have different propagation models. The second part uses a reinforcement learning (RL) algorithm to find the optimal multi-hop path with the lowest propagation loss, based on the results of the environment segmentation. The optimal multi-hop link is simulated and compared with current single propagation models, showing that our proposal can extend the coverage of multi-hop wireless links by transmitting the link via the optimum path.
Automatic Radar Waveform Recognition using the Wigner-Ville distribution and AlexNet-SVM
Njoku Judith Nkechinyere(주디스),Manuel Eugenio Morocho-Cayamce(유제니오),Wansu Lim(임완수) 한국통신학회 2020 한국통신학회 학술대회논문집 Vol.2020 No.8
In this paper, we propose a radar signal modulation algorithm to recognize three different radar signals amidst other wireless communication waveforms, including Barker, linear frequency modulation, and rectangular codes. First we extract the features of the original signal by computing its smoothed pseudo Wigner-Ville distribution. Second, we construct a transfer learning-based convolutional neural network over AlexNet to further extract features from the time-frequency images. Finally, a support vector machine classifier is applied for the signal classification. We also perform a similar analysis with models which use AlexNet for both feature extraction, and classification tasks. Results show that the proposed model which incorporates the linear classifier, achieves the highest recognition accuracy of 97.8%.
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.
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.