Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8849941
Weiqi Hua, Minglei You, Hongjian Sun
Energy hub scheduling plays a vital role in optimally integrating multiple energy vectors, e.g., electricity and gas, to meet both heat and electricity demand. A scalable scheduling model is needed to adapt to various energy sources and operating conditions. This paper proposes a conditional random field (CRF) method to analyse the intrinsic characteristics of energy hub scheduling problems. Building on these characteristics, a reinforcement learning (RL) model is designed to strategically schedule power and natural gas exchanges as well as the energy dispatch of energy hub. Case studies are performed by using real-time digital simulator that enables dynamic interactions between scheduling decisions and operating conditions. Simulation results show that the CRF-based RL method can approach the theoretical optimal scheduling solution after 50 days training. Scheduling decisions are particularly more dependent on received price information during peak-demand period. The proposed method can reduce 9.76% of operating cost and 1.388 ton of carbon emissions per day, respectively.
{"title":"Real-Time Price Elasticity Reinforcement Learning for Low Carbon Energy Hub Scheduling Based on Conditional Random Field","authors":"Weiqi Hua, Minglei You, Hongjian Sun","doi":"10.1109/ICCChinaW.2019.8849941","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8849941","url":null,"abstract":"Energy hub scheduling plays a vital role in optimally integrating multiple energy vectors, e.g., electricity and gas, to meet both heat and electricity demand. A scalable scheduling model is needed to adapt to various energy sources and operating conditions. This paper proposes a conditional random field (CRF) method to analyse the intrinsic characteristics of energy hub scheduling problems. Building on these characteristics, a reinforcement learning (RL) model is designed to strategically schedule power and natural gas exchanges as well as the energy dispatch of energy hub. Case studies are performed by using real-time digital simulator that enables dynamic interactions between scheduling decisions and operating conditions. Simulation results show that the CRF-based RL method can approach the theoretical optimal scheduling solution after 50 days training. Scheduling decisions are particularly more dependent on received price information during peak-demand period. The proposed method can reduce 9.76% of operating cost and 1.388 ton of carbon emissions per day, respectively.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124045489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8849965
Ling Li, Sida Dai, Zhi-Wei Cao
The visualization of urban thermal analysis is a prevalent topic in the establishment of smart cities. With the popularity of mobile devices, large volumes of data about people and their locations are gradually accumulating at mobile base stations. In this study, we used such call details records (CDR) and long short-term memory (LSTM) networks—a kind of recurrent neural network (RNN) —to predict the future traffic of a base station. By implementing gate mechanism, the LSTM can solve the problem of exploding and vanishing gradients of ordinary RNNs. We use a sliding-window approach to transform the problem of time series forecasting into a supervised learning problem. Then, we use the proposed deep LSTM network to model the traffic of base stations, which enables the prediction of future traffic and the generation of the heat map of a city. The method we presented can decrease the root mean square error (RMSE) of the predicted access time down to 23.34 minutes per hour per base station.
{"title":"Deep Long Short-term Memory (LSTM) Network with Sliding-window Approach in Urban Thermal Analysis","authors":"Ling Li, Sida Dai, Zhi-Wei Cao","doi":"10.1109/ICCChinaW.2019.8849965","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8849965","url":null,"abstract":"The visualization of urban thermal analysis is a prevalent topic in the establishment of smart cities. With the popularity of mobile devices, large volumes of data about people and their locations are gradually accumulating at mobile base stations. In this study, we used such call details records (CDR) and long short-term memory (LSTM) networks—a kind of recurrent neural network (RNN) —to predict the future traffic of a base station. By implementing gate mechanism, the LSTM can solve the problem of exploding and vanishing gradients of ordinary RNNs. We use a sliding-window approach to transform the problem of time series forecasting into a supervised learning problem. Then, we use the proposed deep LSTM network to model the traffic of base stations, which enables the prediction of future traffic and the generation of the heat map of a city. The method we presented can decrease the root mean square error (RMSE) of the predicted access time down to 23.34 minutes per hour per base station.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124053572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8849972
Yihao Li, C. Zhong
This paper introduces a special method to construct channel coefficient for describing the general channel correlation scenario in the multiple-input multiple-output (MIMO) radio frequency identification (RFID) system, where two types of channel correlation are involved. Also, we derive an approximate expression for the capacity and discuss the impact of channel correlation on the capacity. In particular, the performance of the capacity in low SNR regime has been investigated to gain more insight. Monte-Carlo simulations are performed to validate our analytical results.
{"title":"Capacity for Correlated MIMO Backscatter Systems","authors":"Yihao Li, C. Zhong","doi":"10.1109/ICCChinaW.2019.8849972","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8849972","url":null,"abstract":"This paper introduces a special method to construct channel coefficient for describing the general channel correlation scenario in the multiple-input multiple-output (MIMO) radio frequency identification (RFID) system, where two types of channel correlation are involved. Also, we derive an approximate expression for the capacity and discuss the impact of channel correlation on the capacity. In particular, the performance of the capacity in low SNR regime has been investigated to gain more insight. Monte-Carlo simulations are performed to validate our analytical results.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116290463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8850168
Yang Li, Chunjing Hu, Jun Wang, Mingfeng Xu
In 5G mobile networks, multiple scenarios have emerged to meet different services requirement. The limited spectrum resource becoming more and more crowed to meet different requirements. To improve the limited transmission resource (spectrum, time, power etc.) utilization while meet the different needs of users, we introduce the reward function as a measure of different allocate policies. Then we calculate the reward that different allocation policies might gain. The arrived state is a Markov Process which means the next coming state is only determined by the current state. To solve the optimization problem, we introduce the Q-Iearning algorithm. Due to the state space is enormous, this paper strives to illustrate a DQN (Deep Q-Network) based resource allocation algorithm. Numerical experiments provided in this paper show the performance of the proposed algorithms by comparing with two baselines.
{"title":"Optimization of URLLC and eMBB Multiplexing via Deep Reinforcement Learning","authors":"Yang Li, Chunjing Hu, Jun Wang, Mingfeng Xu","doi":"10.1109/ICCChinaW.2019.8850168","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8850168","url":null,"abstract":"In 5G mobile networks, multiple scenarios have emerged to meet different services requirement. The limited spectrum resource becoming more and more crowed to meet different requirements. To improve the limited transmission resource (spectrum, time, power etc.) utilization while meet the different needs of users, we introduce the reward function as a measure of different allocate policies. Then we calculate the reward that different allocation policies might gain. The arrived state is a Markov Process which means the next coming state is only determined by the current state. To solve the optimization problem, we introduce the Q-Iearning algorithm. Due to the state space is enormous, this paper strives to illustrate a DQN (Deep Q-Network) based resource allocation algorithm. Numerical experiments provided in this paper show the performance of the proposed algorithms by comparing with two baselines.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123552532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8849966
Yingting Liu, Zhengwei Pan, Jianmei Shen, Hongwu Yang, Chun-man Yan
In a decode-and-forward (DF) wireless energy harvesting (EH) relaying network, where a hybrid power-time splitting (HPTS) scheme is considered. We study the outage performance of one-way DF relaying network over log-normal fading channels, which are applicable to the indoor environment. We analyse the performance of the proposed scheme under the conditions considering the statistical or instantaneous channel state information (CSI). Analytical expressions of outage probability and achievable throughput are derived based on the statistical CSI, furthermore, we can get the optimal time switching factor and power splitting factor. For the instantaneous CSI, the power splitting factor is optimized to minimize the outage probability. In order to find the optimal time switching factor, a bisection iteration method is adopted. The simulation results show that the outage probability and throughput of the hybrid protocol outperform the existing power splitting-based relaying (PSR) scheme.
{"title":"Outage Performance Analysis for a DF Based Hybrid Scheme over Log-normal Fading Channels","authors":"Yingting Liu, Zhengwei Pan, Jianmei Shen, Hongwu Yang, Chun-man Yan","doi":"10.1109/ICCChinaW.2019.8849966","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8849966","url":null,"abstract":"In a decode-and-forward (DF) wireless energy harvesting (EH) relaying network, where a hybrid power-time splitting (HPTS) scheme is considered. We study the outage performance of one-way DF relaying network over log-normal fading channels, which are applicable to the indoor environment. We analyse the performance of the proposed scheme under the conditions considering the statistical or instantaneous channel state information (CSI). Analytical expressions of outage probability and achievable throughput are derived based on the statistical CSI, furthermore, we can get the optimal time switching factor and power splitting factor. For the instantaneous CSI, the power splitting factor is optimized to minimize the outage probability. In order to find the optimal time switching factor, a bisection iteration method is adopted. The simulation results show that the outage probability and throughput of the hybrid protocol outperform the existing power splitting-based relaying (PSR) scheme.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134038836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8849939
K. Umebayashi
This paper focuses on a spectrum usage model in the time domain in the context of dynamic spectrum access (DSA). To achieve a sophisticated dynamic spectrum access, understanding the spectrum usage is an important task. We focus on duty cycle (DC) as a feature quantity of spectrum usage in the time domain and observed DC (O-DC) obtained from long-term spectrum measurement results is used for the modeling. In fact, O-DC has stochastic and deterministic behaviors and we have been investigated modeling for both behaviors. O-DC is stochastic behavior and a mixture distribution based modeling has been considered for the model of stochastic behavior. We employ nonparametric Bayesian model (NPBM) in which the number of distributions is also an adjustable parameter. Statistics of O-DC, such as mean of O-DC, has a deterministic behavior in time domain. Specifically, the deterministic behavior is determined by the common daily habits, such as mean of O-DC during is night is low, but it is high during daytime. We show the validity of the stochastic and deterministic model for O-DC based on long-term spectrum measurement results.
{"title":"Spectrum usage model for smart spectrum","authors":"K. Umebayashi","doi":"10.1109/ICCChinaW.2019.8849939","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8849939","url":null,"abstract":"This paper focuses on a spectrum usage model in the time domain in the context of dynamic spectrum access (DSA). To achieve a sophisticated dynamic spectrum access, understanding the spectrum usage is an important task. We focus on duty cycle (DC) as a feature quantity of spectrum usage in the time domain and observed DC (O-DC) obtained from long-term spectrum measurement results is used for the modeling. In fact, O-DC has stochastic and deterministic behaviors and we have been investigated modeling for both behaviors. O-DC is stochastic behavior and a mixture distribution based modeling has been considered for the model of stochastic behavior. We employ nonparametric Bayesian model (NPBM) in which the number of distributions is also an adjustable parameter. Statistics of O-DC, such as mean of O-DC, has a deterministic behavior in time domain. Specifically, the deterministic behavior is determined by the common daily habits, such as mean of O-DC during is night is low, but it is high during daytime. We show the validity of the stochastic and deterministic model for O-DC based on long-term spectrum measurement results.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114668054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8849955
Kitty Stacpoole, Hongjian Sun, Jing Jiang
Demand side response (DSR) and the interconnectivity of smart technologies will be essential to transform and revolutionize the way consumers engage with the energy industry. The carbon intensity of electricity varies throughout the day as a result of emissions released during generation. These fluctuations in carbon intensity are predicted to increase due to increased penetration of variable generation sources. This paper proposes a novel insight into how reductions in domestic emissions can be achieved, through the scheduling of certain wet appliances to optimally manage low carbon electricity. An appliance detecting and scheduling algorithm is presented and results are generated using real demand data, electricity generation and carbon intensity values. Reductions were achieved from the variations in grid carbon intensity and the availability of solar generation from a household photovoltaic (PV) supply.
{"title":"Smart Scheduling of Household Appliances to Decarbonise Domestic Energy Consumption","authors":"Kitty Stacpoole, Hongjian Sun, Jing Jiang","doi":"10.1109/ICCChinaW.2019.8849955","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8849955","url":null,"abstract":"Demand side response (DSR) and the interconnectivity of smart technologies will be essential to transform and revolutionize the way consumers engage with the energy industry. The carbon intensity of electricity varies throughout the day as a result of emissions released during generation. These fluctuations in carbon intensity are predicted to increase due to increased penetration of variable generation sources. This paper proposes a novel insight into how reductions in domestic emissions can be achieved, through the scheduling of certain wet appliances to optimally manage low carbon electricity. An appliance detecting and scheduling algorithm is presented and results are generated using real demand data, electricity generation and carbon intensity values. Reductions were achieved from the variations in grid carbon intensity and the availability of solar generation from a household photovoltaic (PV) supply.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131960120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8849936
Zhaojie Wu, Wangfei Quan, Tingting Zhang
High accuracy and seamless positioning of vehicles formulate the basis of autonomous driving, as well as the modern intelligent transportation systems. In this paper, aiming at the vehicles in the “blind” spots, where only limited global navigation satellite system (GNSS) signals are provided, the unmanned aerial vehicles (UAVs) are introduced as alternating solutions. Furthermore, the energy efficient resource allocation frameworks are thus provided, based on the Fisher information inequality. All proposed methods can be solved through standard semidefinite programming (SDP) problems. Numerical results are provided. The joint power and bandwidth allocation (JPBA) outperforms both the pure power optimization, and the simple uniform resource allocation methods. Meanwhile, energy consumption tradeoffs between the UAVs and vehicles are also discussed.
{"title":"Resource Allocation in UAV-aided Vehicle Localization Frameworks","authors":"Zhaojie Wu, Wangfei Quan, Tingting Zhang","doi":"10.1109/ICCChinaW.2019.8849936","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8849936","url":null,"abstract":"High accuracy and seamless positioning of vehicles formulate the basis of autonomous driving, as well as the modern intelligent transportation systems. In this paper, aiming at the vehicles in the “blind” spots, where only limited global navigation satellite system (GNSS) signals are provided, the unmanned aerial vehicles (UAVs) are introduced as alternating solutions. Furthermore, the energy efficient resource allocation frameworks are thus provided, based on the Fisher information inequality. All proposed methods can be solved through standard semidefinite programming (SDP) problems. Numerical results are provided. The joint power and bandwidth allocation (JPBA) outperforms both the pure power optimization, and the simple uniform resource allocation methods. Meanwhile, energy consumption tradeoffs between the UAVs and vehicles are also discussed.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128272212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8849948
Jie Wu, Kai Feng, Nan Jin, Zhenjun Wu, Shuaibiao He, Xin Liu, D. Ma
In wireless power transfer (WPT) system, the primary and secondary sides employ full bridges to transfer power. The direction of power transmission is controlled by shifting the phase angle. In this paper, a method of bidirectional power trabsfer and information reverse transmission is proposed for the bidirectional WPT system with dual active bridges through a shared inductive channel. In order to realize the information reverse transmission during power transfer, a new magnetic coil is added to the main circuit. The analysis of transfer function validates the shared channel carrying two frequencies is able to transfer power and information simultaneously. The simulation verifies the proposed method of information reverse transmission.
{"title":"Information Reverse Transmission Method for Bidirectional WPT with Dual Active Bridges","authors":"Jie Wu, Kai Feng, Nan Jin, Zhenjun Wu, Shuaibiao He, Xin Liu, D. Ma","doi":"10.1109/ICCChinaW.2019.8849948","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8849948","url":null,"abstract":"In wireless power transfer (WPT) system, the primary and secondary sides employ full bridges to transfer power. The direction of power transmission is controlled by shifting the phase angle. In this paper, a method of bidirectional power trabsfer and information reverse transmission is proposed for the bidirectional WPT system with dual active bridges through a shared inductive channel. In order to realize the information reverse transmission during power transfer, a new magnetic coil is added to the main circuit. The analysis of transfer function validates the shared channel carrying two frequencies is able to transfer power and information simultaneously. The simulation verifies the proposed method of information reverse transmission.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132784955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/ICCChinaW.2019.8849957
Zihao Xiang, Shiying Han, Bo Liu, Huyang Peng, Zixiong Wang, Guiling Sun
A PMOS RF-DC conversion circuit design for ambient energy harvesting (AEH) at UHF is presented in this paper. The output voltage and power conversion efficient (PCE) of the circuit are theoretically derived, which provides guideline for choosing the parameters of PMOS. We simulate the circuit with Multisim by varying the input RF power level from −40 dBm (0.1 µW) to −3 dBm (0.5 mW) at 2.45 GHz. The theoretical analysis is verified by the simulation results, and we can observe a 82.85% PCE, 0.11% ripple factor and −15 dBm (31.62 µW) sensitivity with 1.62 V output voltage on a 100 kΩ load resistance, which outperforms the Schottky diode based conversion circuit.
{"title":"Design and Analysis of a PMOS RF-DC Conversion Circuit at UHF for Ambient Energy Harvesting","authors":"Zihao Xiang, Shiying Han, Bo Liu, Huyang Peng, Zixiong Wang, Guiling Sun","doi":"10.1109/ICCChinaW.2019.8849957","DOIUrl":"https://doi.org/10.1109/ICCChinaW.2019.8849957","url":null,"abstract":"A PMOS RF-DC conversion circuit design for ambient energy harvesting (AEH) at UHF is presented in this paper. The output voltage and power conversion efficient (PCE) of the circuit are theoretically derived, which provides guideline for choosing the parameters of PMOS. We simulate the circuit with Multisim by varying the input RF power level from −40 dBm (0.1 µW) to −3 dBm (0.5 mW) at 2.45 GHz. The theoretical analysis is verified by the simulation results, and we can observe a 82.85% PCE, 0.11% ripple factor and −15 dBm (31.62 µW) sensitivity with 1.62 V output voltage on a 100 kΩ load resistance, which outperforms the Schottky diode based conversion circuit.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127669294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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