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MIMO-OFDM에서 PAPR 저감 및 사전 왜곡기에 의한 HPA의 전력 효율 개선
NGO THI THU TRANG,한태영,김남 한국콘텐츠학회 2005 한국콘텐츠학회논문지 Vol.5 No.1
In this paper, we evaluate the peak-to-average power ratio (PAPR) performance in a space-time block code (STBC) multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system using selected mapping (SLM) and partial transmit sequences (PTS) approaches. SLM and PTS methods are used to decrease the nonlinear distortion and to improve the power efficiency of the nonlinear high power amplifier(HPA) in the MIMO-OFDM system. In simulation result, when compared with the existing MIMO-OFDM system using QPSK, the PTS method reduces the PAPR about 5 dB while the SLM method can reduce about 3.5 dB. Also, we find the BER performance of the MIMO-OFDM system with and without the predistorter in front of the HPA. When the predistorter is used, the input back-off (IBO) of 4 dB is required in the PTS method, and IBO of 6 dB in the SLM method to closely conform to the linear amplifier. If the method of improving the PAPR is not used, the value of IBO of 8 dB is required. 본 논문에서는 SLM(selective mapping)과 PTS(partial transmit sequence) 방식을 이용한 STBC MIMO-OFDM(space-time block code multi-input multi-output orthorgonal frequency division multiplexing) 시스템의 PAPR(peak-to-average power ratio) 성능을 분석한다. MIMO-OFDM 시스템에서 SLM과 PTS 방식은 비선형왜곡을 줄이고 비선형 HPA(high power amplifier)의 전력효율을 개선하기위해 사용된다. 시뮬레이션 결과에서는 QPSK를 사용한 기존의 MIMO-OFDM 시스템과 비교하였을때, SLM의 PAPR이 3.5dB 정도 줄어드는 동안 PTS는 5dB 정도 감소되는 결과를 볼 수 있다. 또한, HPA의 앞단에 사전왜곡기의 유무에 따른 MIMO-OFDM 시스템의 BER 성능을 분석해 본 결과 사전 왜곡기를 사용하였을 때 선형 증폭기에 근접하기 위해서 SLM 방식에서 6dB IBO(input backoff)가 요구되고 PTS 방식에서는 4dB가 요구됨을 확인하였다. PAPR을 개선하는 방식을 사용하지 않으면 8dB의 IBO가 필요하다.
A Multiple Variable Regression-based Approaches to Long-term Electricity Demand Forecasting
Lan Dong Thi Ngoc,Khai Phan Van,Ngo-Thi-Thu-Trang,Gyoo Seok Choi,Ha-Nam Nguyen 한국인터넷방송통신학회 2021 Journal of Advanced Smart Convergence Vol.10 No.4
Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.
A Multiple Variable Regression-based Approaches to Long-term Electricity Demand Forecasting
Ngoc, Lan Dong Thi,Van, Khai Phan,Trang, Ngo-Thi-Thu,Choi, Gyoo Seok,Nguyen, Ha-Nam The Institute of Internet 2021 International journal of advanced smart convergenc Vol.10 No.4
Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.
Cao, Ngoc-Anh,Phan, Thanh-Hang,Chinh, Nguyen Thi,Tran, Duc-Quynh,Nguyen, Ha-Nam,Trang, Ngo-Thi-Thu,Choi, Gyoo-Seok The Institute of Internet 2022 International Journal of Internet, Broadcasting an Vol.14 No.4
Currently, issues related to freight at Vietnamese logistics companies are becoming more and more urgent because of typical problems in Vietnam such as traffic, infrastructure, and application of information technology. This problem has been studied by applying many different approaches such as Integer Programming (LP), Mixed Integer Programming (MIP), hybrid, meta search, … In this paper, we applied the ILP model in order to deal with the VRP problem in a small size logistics company which is very popular in Vietnam. The experiments showed promising results with some optimal solutions with some small extra costs.