• 한국전자통신학회논문지
• /
• 제13권6호
• /
• pp.1223-1228
• /
• 2018

고속철도 시스템에서 열차의 안전 운행을 지원하기 위해서 사용되는 이동통신기술로서 LTE-R(: Long Term Evolution-Railway)은 열차의 고속이동환경에 적합한 핸드오버기술을 갖추어야 한다. 본 논문에서는 철도통신시스템에서 사용되는 LTE-R 네트워크의 핸드오버 성능을 분석하고 이를 토대로 핸드오버 수행 조건을 제시한다. 성능 분석은 NS(: Network Simulator)-3를 기반으로 하는 컴퓨터 시뮬레이션을 사용하며 성능 파라미터로는 SINR(: Signal to Interference and Noise Ratio), RSRP(: Reference Signal Received Power) 및 데이터율을 사용한다.

• 한국전자통신학회논문지
• /
• 제16권6호
• /
• pp.1059-1068
• /
• 2021

도심지역에서 GPS(Global Positioning System)/GNSS(Global Navigation Satellite System) 신호는 건물과 같은 구조물에 의해 차단되거나 왜곡되어 위치추정에 한계가 존재한다. 이 문제를 보완하기 위해 본 논문에서는 LTE 신호의 RSRP(Reference Signal Received Power) 정보를 사용한 Fingerprinting 기법으로 측위를 수행하고자 한다. Fingerprinting의 측위 단계에서 많이 사용되는 W-KNN(Weighted - K Nearest Neighbors) 기법은 Correlation 시 사용되는 유사도 거리 계산 방법과 가중치 적용 방법 등에 따라 다른 측위 성능의 결과를 생성한다. 본 논문에서는 Correlation 시 사용되는 기법들에 따른 Fingerprinting 측위 성능을 실 데이터 기반으로 비교 분석하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제5권9호
• /
• pp.1596-1612
• /
• 2011

Coordinated Multi-Point (CoMP) transmission / reception is being studied in Long Term Evolution-Advanced (LTE-A) for future evolution of the $3^{rd}$ Generation Partnership Project (3GPP) LTE. Support of soft handover is essential for improving the performance of cell edge users. CoMP provides a natural framework for enabling soft handover in the LTE system. This paper evaluates the soft handover gain in LTE-A downlink. Mathematical analysis of signal to interference plus noise ratio (SINR) gain and the handover margins for soft handover and hard handover are derived. CoMP system model is developed and an inter-cell and intra-cell interference model is derived, taking into account the pathloss, shadowing, cell loading, and traffic activity. Reference signal received power (RSRP) is used to define the triggers and the measurements for soft handover. Our results indicate that parameter choices such as handover margin and the CoMP set size impact CoMP performance gain.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2021년도 춘계학술대회
• /
• pp.182-183
• /
• 2021

UE(User Equipment)의 이동성을 보장해주기 위해 전파 신호 레벨 및 품질에 따라 handover 현상이 발생한다. 이때 설정해 놓은 handover event 에 따라 handover 의 threshold 가 바뀌게 된다. 본 연구에서는 아일랜드에서 측정한 전파 데이터의 LTE KPI(Key Performance Indicator)를 사용하여 handover 성능을 분석한다.

• 한국항해항만학회지
• /
• 제45권5호
• /
• pp.231-237
• /
• 2021

Enhancing the performance of maritime wireless communication has been highlighted by the issue of cell planning in the sea area because of lack of an appropriate Propagation Loss Model (PLM). To resolve the cell planning issue in vast sea areas, it was essential to develop the (PLM) matching the intended sea area. However, there were considerable gaps between the prediction of legacy PLMs and field measurement in propagation loss and there was a need to develop the adjusted PLM (A-PLM). Therefore, cell planning was performed on this adjusted model, including modification of the base station's location, altitude, and antenna azimuth to meet the quality objectives. Furthermore, in order to verify the availability of the cell planning, Communication Service Quality Monitoring System (CS-QMS) was developed in the LTE-Maritime project to collect LTE signal quality information from the onboard equipment at regular intervals and to ensure that the service quality was high enough to satisfy the goals in each designated grid. As a result of verification, the success rate of RSRP was 95.7% for the intensive management zone (IMZ) and 96.4% for the interested zone (IZ), respectively.

• 전자공학회논문지
• /
• 제53권5호
• /
• pp.19-24
• /
• 2016

LTE-A(Long Term Evolution-Advanced)에서는 비용 효율적 방법으로 급증하는 무선 데이터 서비스를 대처하고 사용자의 QoS(Quality of Service)를 만족시키기 위해 소형셀 향상(SCE:Small Cell Enhancement)에 대한 연구가 활발히 진행되고 있다. 하지만 수많은 소형셀이 밀집하여 불규칙하게 배치되기 때문에 오프로딩 기법이 적용되어야 한다. 본 논문에서는 LTE-Advanced 시스템에서 SCE 위한 새로운 사용자 오프로딩 기법을 제안한다. 제안 기법은 UE(User Equipment)로부터 받은 RSRP(Reference Signal Received Power)를 비교하여 소형셀의 클러스터를 구성한다. 클러스터 내에서 셀의 사용자 수와 간섭 상황을 고려하여 사용자 오프로딩을 적용한다. 모의실험 결과, 제안한 기법에서 소형셀 사용자의 전송률 및 스펙트럼 효율이 향상되어 전체적인 셀 성능이 향상 되는 것을 볼 수 있다.

• Journal of Positioning, Navigation, and Timing
• /
• 제10권3호
• /
• pp.207-214
• /
• 2021

A new positioning technique for positioning of LTE base stations is proposed. The positioning information of the base station is absolutely necessary for model-based wireless positioning, and is required in some of the various merhodologies for estimating signals in an uncorrected area when construnting a database for fingerprinting-based positioning. Using the acquired location-based Reference Signal Received Power (RSRP) information to estimate the location of the base station, it is impossible with the existing trilateration methods. Therefore, in this paper, a method using reference particles is proposed. Particles are randomly generated in the application area, and signal propagation modeling is performed assuming that a base station is located in each particle. Based on this, the errors of measurements are calculated. The particle group with the minimum measurement errors is selected, the position of the base station is estimated through weighted summation, and the signal propagation model of the corresponding base station is built at the same time. The performance of the proposed technology is verified using data acquired in Seocho-dong, Seoul.

• Journal of Positioning, Navigation, and Timing
• /
• 제10권1호
• /
• pp.55-66
• /
• 2021

In this paper, we propose a signal propagation modeling technique for generating a positioning fingerprint DB based on Long Term Evolution (LTE) signals. When a DB is created based on the location-based signal information collected in an urban area, gaps in the DB due to uncollected areas occur. The spatial interpolation method for filling the gaps has limitations. In addition, the existing gap filling technique through signal propagation modeling does not reflect the signal attenuation characteristics according to directions occurring in urban areas by considering only the signal attenuation characteristics according to distance. To solve this problem, this paper proposes a Deep Neural Network (DNN)-based signal propagation functionalization technique that considers distance and direction together. To verify the performance of this technique, an experiment was conducted in Seocho-gu, Seoul. Based on the acquired signals, signal propagation characteristics were modeled for each method, and Root Mean Squared Errors (RMSE) was calculated using the verification data to perform comparative analysis. As a result, it was shown that the proposed technique is improved by about 4.284 dBm compared to the existing signal propagation model. Through this, it can be confirmed that the DNN-based signal propagation model proposed in this paper is excellent in performance, and it is expected that the positioning performance will be improved based on the fingerprint DB generated through it.

• Journal of Positioning, Navigation, and Timing
• /
• 제11권3호
• /
• pp.217-227
• /
• 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.

• 전자공학회논문지
• /
• 제53권5호
• /
• pp.13-18
• /
• 2016

LTE-A(Long Term Evolution-Advanced)에서는 비용 효율적 방법으로 급증하는 무선 데이터 서비스를 대처하고 사용자의 QoS(Quality of Service)를 만족시키기 위해 SCE(Small Cell Enhancement)에 대한 연구가 활발히 진행되고 있다. 하지만 수많은 소형셀이 밀집하여 불규칙하게 배치되기 때문에 전송품질 저하 등 많은 문제가 발생하게 된다. 본 논문에서는 밀집한 소형셀 네트워크에서 클러스터링 기반 간섭 관리 기법을 제안한다. 제안 기법은 UE(User Equipment)로부터 받은 RSRP(Reference Signal Received Power)를 비교하여 소형셀의 클러스터를 구성한다. 클러스터 내에서 ABS를 적용하여 소형셀 간 간섭을 완화시킨다. 또한, 전력제어를 적용하여 클러스터 간 간섭을 줄인다. 모의실험 결과, 제안한 기법에서 소형셀 사용자의 SINR(Signal to Interference plus Noise Ratio), 전송률 및 스펙트럼 효율이 향상되어 전체적인 셀 성능이 향상 되는 것을 볼 수 있다.