• Journal of Information Processing Systems
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• v.5 no.4
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• pp.207-220
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• 2009

A large number of phone calls and data services will take place in indoor environments. In Long Term Evolution (LTE), femtocell, as a home base station for indoor coverage extension and wideband data service, has recently gained significant interests from operators and consumers. Since femtocell is frequently turned on and off by a personal owner, not by a network operator, one of the key issues is that femtocell should be identified autonomously without system information to support handover from macrocell to femtocell. In this paper, we propose a dynamic reservation scheme of Physical Cell Identities (PCI) for 3GPP LTE femtocell systems. There are several reserving types, and each type reserves a different number of PCIs for femtocell. The transition among the types depends on the deployed number of femtocells, or the number of PCI confusion events. Accordingly, flexible use of PCIs can decrease PCI confusion. This reduces searching time for femtocell, and it is helpful for the quick handover from macrocell to femtocell. Simulation results show that our proposed scheme reduces average delay for identifying detected cells, and increases network capacity within equal delay constraints.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.229-232
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• 2008

급격한 이동통신 기술의 발전에 힘입어 음영지역 해소 및 고속 데이터 처리를 위해 댁내 기지국에 대한 개발 및 연구가 진행 중이다. 댁내 기지국은 크게 음영지역 해소를 위한 Open 방식의 기지국과 고속 데이터 처리를 위해 특정 가입자만 사용할 수 있는 Close 방식이 있다. 상기 방식들은 망의 특성에 맞게 이를 제공하는 망 사업자에 의해 선택 된다. 댁내 기지국을 관리하기 위해서는 많은 시간과 인력자원이 소요되므로 자동으로 설정 및 최적화시키는 기능이 요구 시 되고 있으며, 이를 3GPP에서 SON (Self Organizing Optimizing Networks) 이라 일컫고 연구진행 중이다. 본 논문은 댁내 기지국 관리를 위해 셀의 기본 인자인 PCI(Physical Layer Identity) 할당 방안과 댁내 기지국간 간섭을 최소화 시키기 위한 Adaptive Coverage 방안을 제시한다. 또한 계층적 셀 구성(Hierarchical Cell Structure)에 따른 이동성 제공 방안을 제시한다.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.217-227
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• 2022

In order to provide a location-based services regardless of indoor or outdoor space, it is important to provide position information of the terminal regardless of location. Among the wireless/mobile communication resources used for this purpose, Long Term Evolution (LTE) signal is a representative infrastructure that can overcome spatial limitations, but the positioning method based on the location of the base station has a disadvantage in that the accuracy is low. Therefore, a fingerprinting technique, which is a pattern recognition technology, has been widely used. The simplest yet widely applied algorithm among Fingerprint positioning technologies is k-Nearest Neighbors (kNN). However, in the kNN algorithm, it is difficult to find the optimal K value with the lowest positioning error for each location to be estimated, so it is generally fixed to an appropriate K value and used. Since the optimal K value cannot be applied to each estimated location, therefore, there is a problem in that the accuracy of the overall estimated location information is lowered. Considering this problem, this paper proposes a technique for adaptively varying the K value by using a Convolutional Neural Network (CNN) model among Artificial Neural Network (ANN) techniques. First, by using the signal information of the measured values obtained in the service area, an image is created according to the Physical Cell Identity (PCI) and Band combination, and an answer label for supervised learning is created. Then, the structure of the CNN is modeled to classify K values through the image information of the measurements. The performance of the proposed technique is verified based on actual data measured in the testbed. As a result, it can be seen that the proposed technique improves the positioning performance compared to using a fixed K value.