• ETRI Journal
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• v.45 no.6
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• pp.939-951
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• 2023

It is critical to estimate the location using only Long-Term Evolution (LTE) and Wi-Fi information gathered by the user's smartphone and deployable for emergency rescue, regardless of whether the Global Positioning System is received. In this research, we used a vehicle to gather LTE and Wi-Fi wireless signals over a large area for an extended period of time. After that, we used the learning technique to create a positioning database that included both collection and noncollection points. We presented a two-step positioning algorithm that utilizes coarse localization to discover a rough location in a wide area rapidly and fine localization to estimate a particular location based on the coarse position. We confirmed our technology utilizing different sorts of devices in four regional types that are generally encountered: dense urban, urban, suburban, and rural. Results presented that our algorithm can satisfactorily achieve the target accuracy necessary in emergency rescue circumstances.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.151-156
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• 2020

A technology for calculating the position of a device is very important for users who receive positioning services, regardless of various indoor/outdoor or with/without any positioning infrastructure existence environments. One of the positioning resources widely used at present, LTE, is a typical infrastructure that can overcome the space limitation, however its positioning method based on the position of the LTE base station has low accuracy. A method of constructing a radio wave map of an LTE signal has been proposed as a method for overcoming the accuracy, but it takes a lot of time and cost to perform high-density collection in a wide area. In this paper, we describe a method of creating a high-density DB for the entire region by using vehicle-based partial collection data. To create a positioning database, we applied the idea of Generative Adversarial Network (GAN), which has recently been in the spotlight in the field of deep learning, and learned the collected data. Then, a virtually generated map which having the smallest error from the actual data is selected as the optimum DB. We verified the effectiveness of the positioning DB generation algorithm using the positioning data obtained from un-collected area.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.3
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• pp.81-86
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• 2014

According to the development of streaming services with P2P and mobile network technologies, researches to enhance the service quality in mobile environment have been proposed. However, streaming services considering high-speed mobile environment and characteristics of heterogenous terminals have been hindered from being provided with the required quality from user because of bandwidth congestion between selfish peers of existing P2P system. It is also prone to long delay and loss in accordance with the repeated traffic amounts because there are no optimized solution for traffic localization. The structure to enhance peer contribution for service differentiation and peer selection with clustering scheme with location information of terminal can satisfy both users and service providers with service quality and efficiency. In this paper, we propose an incentive mechanism and resource distribution scheme with user contribution and traffic cost information based on user location, which make mobile users increase the satisfaction of service quality in LTE environments.