Pub Date : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947311
Haoyue Xiao, Yubai Li
This paper discusses the singular source’s Direction-Of-Arrival (DOA) and Direction-Of-Distance (DOD) estimation method based on a tensor decomposition algorithm in the near-field situation. With the assistance of the uniqueness of tensor decomposition, the proposed method achieves a high-accuracy performance in both DOA and DOD estimations. For Uniform Linear Arrays (ULA), the steering vector of near-field sources is determined by both angle and distance parameters. Two modified models are built for DOA and DOD estimations respectively and each of them contains only one parameter. These two models are furtherly turned to tensor models by cutting to slices. Rank-l tensor approximation Alternating Least Squares (ALS) algorithms are then used to estimate DOA and DOD for its general global convergence property. The results are used for localization and numerical simulations have verified the effectiveness of the proposed method.
{"title":"CANDECOMP&PARAFAC-based Near-Field Source Localization by Passive Sensor Arrays","authors":"Haoyue Xiao, Yubai Li","doi":"10.1109/ICCT46805.2019.8947311","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947311","url":null,"abstract":"This paper discusses the singular source’s Direction-Of-Arrival (DOA) and Direction-Of-Distance (DOD) estimation method based on a tensor decomposition algorithm in the near-field situation. With the assistance of the uniqueness of tensor decomposition, the proposed method achieves a high-accuracy performance in both DOA and DOD estimations. For Uniform Linear Arrays (ULA), the steering vector of near-field sources is determined by both angle and distance parameters. Two modified models are built for DOA and DOD estimations respectively and each of them contains only one parameter. These two models are furtherly turned to tensor models by cutting to slices. Rank-l tensor approximation Alternating Least Squares (ALS) algorithms are then used to estimate DOA and DOD for its general global convergence property. The results are used for localization and numerical simulations have verified the effectiveness of the proposed method.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134603733","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947053
Wenli Zhu, Min Zhang
An efficient neural network-based approach for broadband direction of arrival (DOA) estimation is presented in this paper. The received data of the uniform circle array (UCA) is transformed into direction image, which is used as the input of the neural network. The phase component of the spatial covariance matrix of the received signal is extracted to form the direction image. We establish a convolutional neural network (CNN) with five hidden layers to learn the inverse mapping from the space of possible antenna element excitations to the space of possible angular directions to the signal source. DOA estimation is formulated as a regression problem, where the each DOA label to the direction image is consisted of the sine and cosine values of the angle of arrival. Simulation results show that the trained CNN network can be successfully used for broadband DOA estimation. The performance of the developed CNN model is comparable to the performance of the conventional algorithms at the lower signal-to-noise ratio. Importantly, the proposed CNN estimator further reduces the computation time which makes it successful to apply to real-time applications.
{"title":"A Deep Learning Architecture for Broadband DOA Estimation","authors":"Wenli Zhu, Min Zhang","doi":"10.1109/ICCT46805.2019.8947053","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947053","url":null,"abstract":"An efficient neural network-based approach for broadband direction of arrival (DOA) estimation is presented in this paper. The received data of the uniform circle array (UCA) is transformed into direction image, which is used as the input of the neural network. The phase component of the spatial covariance matrix of the received signal is extracted to form the direction image. We establish a convolutional neural network (CNN) with five hidden layers to learn the inverse mapping from the space of possible antenna element excitations to the space of possible angular directions to the signal source. DOA estimation is formulated as a regression problem, where the each DOA label to the direction image is consisted of the sine and cosine values of the angle of arrival. Simulation results show that the trained CNN network can be successfully used for broadband DOA estimation. The performance of the developed CNN model is comparable to the performance of the conventional algorithms at the lower signal-to-noise ratio. Importantly, the proposed CNN estimator further reduces the computation time which makes it successful to apply to real-time applications.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124484179","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947070
Xiaoyan Jin, Jun Zhang, Xinghua Sun, Ping Zhang, Shu Cai
Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost.
{"title":"Computation Offloading and Resource Allocation for MEC in C-RAN: A Deep Reinforcement Learning Approach","authors":"Xiaoyan Jin, Jun Zhang, Xinghua Sun, Ping Zhang, Shu Cai","doi":"10.1109/ICCT46805.2019.8947070","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947070","url":null,"abstract":"Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115775485","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947076
Linxi Wang, Xiaoxi Hu, Xun Han, Yin Kuang, Xinquan Yang
Multi-target tracking technologies have important research value in many fields. Algorithms based on random finite set theory can achieve a better tracking effect without data association, which have attracted wide attentions. In this paper, after establishing a real multi-target motion scenario, CBMeMBer filtering algorithm is simulated and implemented on the linear Gauss condition, and is compared with PHD, CPHD and MeMBer filtering algorithm. The simulation results show that CBMeMBer filtering algorithm is correct and effective. Under the same simulation conditions, its tracking performance is obviously improved, and it has good application prospects in multi-target tracking field.
{"title":"Simulation of CBMeMber Multi-target Tracking Algorithm Based on Gauss Mixture","authors":"Linxi Wang, Xiaoxi Hu, Xun Han, Yin Kuang, Xinquan Yang","doi":"10.1109/ICCT46805.2019.8947076","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947076","url":null,"abstract":"Multi-target tracking technologies have important research value in many fields. Algorithms based on random finite set theory can achieve a better tracking effect without data association, which have attracted wide attentions. In this paper, after establishing a real multi-target motion scenario, CBMeMBer filtering algorithm is simulated and implemented on the linear Gauss condition, and is compared with PHD, CPHD and MeMBer filtering algorithm. The simulation results show that CBMeMBer filtering algorithm is correct and effective. Under the same simulation conditions, its tracking performance is obviously improved, and it has good application prospects in multi-target tracking field.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124283423","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947271
Rui Zhong, Xiaocen Dong, Rongheng Lin, Hua Zou
With the continuous development of the communication industry, more and more fraud calls appear in the user’s daily life and the crime of telecom fraud is growing rapidly, causing huge losses every year. Traditional fraud detection methods are less flexible and they all belong to passive interception and rely on intelligent terminals. At present, a more accurate and timely method is needed to deal with the evolving fraud. Therefore, this paper proposes an identification method for fraud phone calls based on Broad Learning System (BLS). We processed the text data of fraud phone calls through the first 15s of the call content identification monitoring, constructed the TF-IDF model, then converted it into a neural network based on the BLS and identified the fraud phone calls on this model. At the same time, the model can be updated quickly by corresponding incremental learning algorithm without retraining based on the BLS, which is suitable for fraud identification systems with few data features but high real-time prediction requirements. The method mentioned above is experimented and analyzed in detail. The results show that this method has higher accuracy and excellent training speed on fraud data. Compared with the original fraud identification methods, it can actively intercept and has higher accuracy. Compared with other neural network algorithms used in fraud system, the method has better training speed, can ensure the accuracy and timeliness of online fraud identification and help quickly identify fraud phone calls.
{"title":"An Incremental Identification Method for Fraud Phone Calls Based on Broad Learning System","authors":"Rui Zhong, Xiaocen Dong, Rongheng Lin, Hua Zou","doi":"10.1109/ICCT46805.2019.8947271","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947271","url":null,"abstract":"With the continuous development of the communication industry, more and more fraud calls appear in the user’s daily life and the crime of telecom fraud is growing rapidly, causing huge losses every year. Traditional fraud detection methods are less flexible and they all belong to passive interception and rely on intelligent terminals. At present, a more accurate and timely method is needed to deal with the evolving fraud. Therefore, this paper proposes an identification method for fraud phone calls based on Broad Learning System (BLS). We processed the text data of fraud phone calls through the first 15s of the call content identification monitoring, constructed the TF-IDF model, then converted it into a neural network based on the BLS and identified the fraud phone calls on this model. At the same time, the model can be updated quickly by corresponding incremental learning algorithm without retraining based on the BLS, which is suitable for fraud identification systems with few data features but high real-time prediction requirements. The method mentioned above is experimented and analyzed in detail. The results show that this method has higher accuracy and excellent training speed on fraud data. Compared with the original fraud identification methods, it can actively intercept and has higher accuracy. Compared with other neural network algorithms used in fraud system, the method has better training speed, can ensure the accuracy and timeliness of online fraud identification and help quickly identify fraud phone calls.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132994265","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947132
Juan Yang, Zhiping Shi, Dandi Yang, Chen-Xi Wang
In this paper, we propose an improved belief propagation (BP) decoding algorithm for decoding BATS codes. In the traditional BP decoding, the BP decoder decodes the input packets only when the rank equals to the degree of the efficient matrix for a batch. For improving the decoding performance, our proposed scheme exploits the fact that some packets can be decoded even when the rank is smaller than the degree. We decode these packets in the process of calculating the rank of batch, which will not induce extra computational cost. The simulation results show that the decoding performance of our proposed method is better than the traditional BATS codes.
{"title":"An Improved Belief Propagation Decoding of BATS Codes","authors":"Juan Yang, Zhiping Shi, Dandi Yang, Chen-Xi Wang","doi":"10.1109/ICCT46805.2019.8947132","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947132","url":null,"abstract":"In this paper, we propose an improved belief propagation (BP) decoding algorithm for decoding BATS codes. In the traditional BP decoding, the BP decoder decodes the input packets only when the rank equals to the degree of the efficient matrix for a batch. For improving the decoding performance, our proposed scheme exploits the fact that some packets can be decoded even when the rank is smaller than the degree. We decode these packets in the process of calculating the rank of batch, which will not induce extra computational cost. The simulation results show that the decoding performance of our proposed method is better than the traditional BATS codes.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122327753","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947227
K. Mao, Xianqiang Li, Chen Wei, Zhiyuan Ma, Xixiu Wu
When the high-power VLF (Very Low Frequency) communication system is in operation, dielectric loss and corresponding temperature rising will occur at the terminal of the antenna due to the alternating high electric field intensity environment. Based on the theoretical analysis of dielectric loss and temperature rise, the distribution of electric field intensity at the terminal of antenna and its influencing factors are numerically analyzed by establishing a three-dimensional simulation model. The results show that the electric field intensity varies linearly with the increase of the voltage of the antenna terminal; however, the working frequency has little effect on the electric field intensity. Finally, the reduction effect of a corona ring on the electric field intensity at the antenna terminal is analyzed.
{"title":"Numerical Analysis of Electric Field Distribution at the Terminal of VLF Antenna","authors":"K. Mao, Xianqiang Li, Chen Wei, Zhiyuan Ma, Xixiu Wu","doi":"10.1109/ICCT46805.2019.8947227","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947227","url":null,"abstract":"When the high-power VLF (Very Low Frequency) communication system is in operation, dielectric loss and corresponding temperature rising will occur at the terminal of the antenna due to the alternating high electric field intensity environment. Based on the theoretical analysis of dielectric loss and temperature rise, the distribution of electric field intensity at the terminal of antenna and its influencing factors are numerically analyzed by establishing a three-dimensional simulation model. The results show that the electric field intensity varies linearly with the increase of the voltage of the antenna terminal; however, the working frequency has little effect on the electric field intensity. Finally, the reduction effect of a corona ring on the electric field intensity at the antenna terminal is analyzed.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"46 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120974556","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947273
Yan Xu, Yi Zheng, Hao Cui, Yifan Hu, Xiaodong Gong, Hui Liu
Aiming at the problem of One-Side EMD on non-equal modulus composite signal processing, this paper proposes the mechanism and method for phase spectrum decomposition based on spectral component companding. This mechanism proposes the conception of “Hypothesis Equimodular Vector”, designs the phase deflection model to calculate equalmodulus resultant vector through spectral component companding method, constructs equal modulus phase spectrum and its One-Side superposition. The equal modulus phase spectrum has features forOne-Side EMD and could be decomposed. This mechanism and method extend One-Side EMD use field from equal modulus to non-equal, which could gain higher generality and wider application prospect. The simulation testing proves that it could be used in the instantaneous time-frequency analysis for any two frequency components with higher reliability and accuracy.
{"title":"The Research on Phase Spectrum Decomposition Mechanism Based on Spectral Component Companding","authors":"Yan Xu, Yi Zheng, Hao Cui, Yifan Hu, Xiaodong Gong, Hui Liu","doi":"10.1109/ICCT46805.2019.8947273","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947273","url":null,"abstract":"Aiming at the problem of One-Side EMD on non-equal modulus composite signal processing, this paper proposes the mechanism and method for phase spectrum decomposition based on spectral component companding. This mechanism proposes the conception of “Hypothesis Equimodular Vector”, designs the phase deflection model to calculate equalmodulus resultant vector through spectral component companding method, constructs equal modulus phase spectrum and its One-Side superposition. The equal modulus phase spectrum has features forOne-Side EMD and could be decomposed. This mechanism and method extend One-Side EMD use field from equal modulus to non-equal, which could gain higher generality and wider application prospect. The simulation testing proves that it could be used in the instantaneous time-frequency analysis for any two frequency components with higher reliability and accuracy.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116369310","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947153
Hongyang Lin, Junhu Zhu, Jianshan Peng, Dixia Zhu
Fuzzing [1] is a well-known technique which was employed to provide unexpected or random data as input to JavaScript engines in hopes of finding a security vulnerability. For effective fuzzing, the input must be both syntactically correct and uncommonly randomized for exceptions such as crashes, failing built-in code assertions, or potential memory leaks. In this work, we introduced system Deity which managed to resolve the conflict with innovative AST(Abstract Syntax Tree) [2] based tree mutation and generating methods. It leverages a high-level structural representation of intermediate process JavaScript code. Our evaluation demonstrates the effectiveness of Deity. For large-scale JavaScript engines (njs, mjs, Javascript-Core, ChakraCore, Espruino, Jerryscript) fuzzing, our results significantly show that Deity can improve code coverage and finding more deep rooted bugs (i.e., 35 new bugs, among which we discovered 21 new vulnerabilities with 3 CVEs assigned) over Superion and CodeAlchemist.
{"title":"Deity: Finding Deep Rooted Bugs in JavaScript Engines","authors":"Hongyang Lin, Junhu Zhu, Jianshan Peng, Dixia Zhu","doi":"10.1109/ICCT46805.2019.8947153","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947153","url":null,"abstract":"Fuzzing [1] is a well-known technique which was employed to provide unexpected or random data as input to JavaScript engines in hopes of finding a security vulnerability. For effective fuzzing, the input must be both syntactically correct and uncommonly randomized for exceptions such as crashes, failing built-in code assertions, or potential memory leaks. In this work, we introduced system Deity which managed to resolve the conflict with innovative AST(Abstract Syntax Tree) [2] based tree mutation and generating methods. It leverages a high-level structural representation of intermediate process JavaScript code. Our evaluation demonstrates the effectiveness of Deity. For large-scale JavaScript engines (njs, mjs, Javascript-Core, ChakraCore, Espruino, Jerryscript) fuzzing, our results significantly show that Deity can improve code coverage and finding more deep rooted bugs (i.e., 35 new bugs, among which we discovered 21 new vulnerabilities with 3 CVEs assigned) over Superion and CodeAlchemist.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116420857","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947128
Hongqiang Li, Yubing Zhang, Xu Zhao, Xiaoke Tang
The rapid development of Internet of Things (IoT) puts forward much requirements for wireless communication technology. Low Power Wide Area Networks (LPWAN) are designed for low bandwidth, low power, long range and large number of connected IoT applications. As one of the LPWAN, Low Power Long Range Transceiver (LoRa) described as a Frequency Shift Chirp Modulation (FSCM), is widely concerned and studied. In this paper, the signal reception of LoRa modulation in the wireless tree topology is analyzed, and the multi-user interference analysis proves that the multi-user interference has a great impact on the system performance. At the same time, we proposed Delay Coordinated Multiple Points Transmission (DCoMP). Multiple nodes close to each other send the same data to the target node. Due to the inaccuracy of synchronization between nodes, there will be a certain timing offset when sending signals to the same target node. After combining signals of multiple nodes according to different timing offset, the receiver performance of signals can be improved. The coordinated nodes can also actively adjust the signal sending timing according to the path time delay and processing delay, so as to improve the receiving performance of the signal merging algorithm of the receiver node. LoRa modulation improves the signal reception performance by adopting DCoMP transmission, thus improving the overall throughput of the system.
{"title":"Delay CoMP of LoRa Modulation in Wireless Tree Topology Network","authors":"Hongqiang Li, Yubing Zhang, Xu Zhao, Xiaoke Tang","doi":"10.1109/ICCT46805.2019.8947128","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947128","url":null,"abstract":"The rapid development of Internet of Things (IoT) puts forward much requirements for wireless communication technology. Low Power Wide Area Networks (LPWAN) are designed for low bandwidth, low power, long range and large number of connected IoT applications. As one of the LPWAN, Low Power Long Range Transceiver (LoRa) described as a Frequency Shift Chirp Modulation (FSCM), is widely concerned and studied. In this paper, the signal reception of LoRa modulation in the wireless tree topology is analyzed, and the multi-user interference analysis proves that the multi-user interference has a great impact on the system performance. At the same time, we proposed Delay Coordinated Multiple Points Transmission (DCoMP). Multiple nodes close to each other send the same data to the target node. Due to the inaccuracy of synchronization between nodes, there will be a certain timing offset when sending signals to the same target node. After combining signals of multiple nodes according to different timing offset, the receiver performance of signals can be improved. The coordinated nodes can also actively adjust the signal sending timing according to the path time delay and processing delay, so as to improve the receiving performance of the signal merging algorithm of the receiver node. LoRa modulation improves the signal reception performance by adopting DCoMP transmission, thus improving the overall throughput of the system.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"419 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123552931","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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