Pub Date : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967039
A. Magro, Riccardo Chiello, C. Albanese, J. Baker, G. Comoretto, A. DeMarco, A. Gravina, R. Halsall, M. Roberts, K. Adami
The Low Frequency Aperture Array (LFAA) component of the Square Kilometer Array (SKA) involves the processing of 218 signal chains, which will be performed on custom FPGA boards, the Tile Processing Module (TPM). These TPMs, as well as firmware running on them, need to be managed, monitored and controlled by the rest of the system. This requires access to on-board devices and registers on running firmware. This paper presents the software framework which has been developed to automatically generate VHDL code that exposes information on the memory map of running firmware, as well as a flexible software infrastructure for interacting with the board.
{"title":"A software infrastructure for firmware-software interaction: The case of TPMs","authors":"A. Magro, Riccardo Chiello, C. Albanese, J. Baker, G. Comoretto, A. DeMarco, A. Gravina, R. Halsall, M. Roberts, K. Adami","doi":"10.1109/ICSIGSYS.2017.7967039","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967039","url":null,"abstract":"The Low Frequency Aperture Array (LFAA) component of the Square Kilometer Array (SKA) involves the processing of 218 signal chains, which will be performed on custom FPGA boards, the Tile Processing Module (TPM). These TPMs, as well as firmware running on them, need to be managed, monitored and controlled by the rest of the system. This requires access to on-board devices and registers on running firmware. This paper presents the software framework which has been developed to automatically generate VHDL code that exposes information on the memory map of running firmware, as well as a flexible software infrastructure for interacting with the board.","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125492122","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 : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967058
Sasa Maric, Audri Biswas, S. Reisenfeld
In this paper we present a method to diagnose and mitigate against primary user emulation attacks (PUEA) in cognitive radio networks. We develop a hybrid algorithm that uses a combination of compressed sensing and belief propagation to identify and combat PUEAs. We propose to use compressive sensing at the fusion centre to localise a primary user, then distribute the primary user location to secondary users in order to establish theoretical data for comparison and then use a variant of belief propagation at each secondary user to diagnose primary user emulation attacks. Using a central-distributed hybrid approach ensures that our algorithm is highly adaptive, accurate and simple to implement.
{"title":"A complete algorithm to diagnose and alleviate the effects of physical layer attacks","authors":"Sasa Maric, Audri Biswas, S. Reisenfeld","doi":"10.1109/ICSIGSYS.2017.7967058","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967058","url":null,"abstract":"In this paper we present a method to diagnose and mitigate against primary user emulation attacks (PUEA) in cognitive radio networks. We develop a hybrid algorithm that uses a combination of compressed sensing and belief propagation to identify and combat PUEAs. We propose to use compressive sensing at the fusion centre to localise a primary user, then distribute the primary user location to secondary users in order to establish theoretical data for comparison and then use a variant of belief propagation at each secondary user to diagnose primary user emulation attacks. Using a central-distributed hybrid approach ensures that our algorithm is highly adaptive, accurate and simple to implement.","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131864914","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 : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967027
Andy Triwinarko, I. Dayoub, Prasaja Wikanta
Vehicular Ad-hoc Networks (VANETs) is a wireless networking technology that can be used to support the emergence of Intelligent Transportation Systems (ITS). The deployment of these systems will allow connected vehicle to communicate each other and also with the road side infrastructures in order to avoid possible accidents and provide more comfort applications to the driver and passenger. The designers of applications in VANETs must consider its characteristics such as the high mobility of the vehicles, rapid change of topology and predicted paths. In addition, they must also consider several factors such as different quality of service (QoS) requirement for different type of applications and reliable transmission link quality. Two potential solutions that can be used to support the large and diverse applications in VANETs are Multiple-Input Multiple-Output (MIMO) processing techniques and cross-layer design among the original layers. This paper will review the benefit of employing those two technologies to improve the overall network performances in VANETs.
{"title":"Using MIMO and cross layer design for VANETs: A review","authors":"Andy Triwinarko, I. Dayoub, Prasaja Wikanta","doi":"10.1109/ICSIGSYS.2017.7967027","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967027","url":null,"abstract":"Vehicular Ad-hoc Networks (VANETs) is a wireless networking technology that can be used to support the emergence of Intelligent Transportation Systems (ITS). The deployment of these systems will allow connected vehicle to communicate each other and also with the road side infrastructures in order to avoid possible accidents and provide more comfort applications to the driver and passenger. The designers of applications in VANETs must consider its characteristics such as the high mobility of the vehicles, rapid change of topology and predicted paths. In addition, they must also consider several factors such as different quality of service (QoS) requirement for different type of applications and reliable transmission link quality. Two potential solutions that can be used to support the large and diverse applications in VANETs are Multiple-Input Multiple-Output (MIMO) processing techniques and cross-layer design among the original layers. This paper will review the benefit of employing those two technologies to improve the overall network performances in VANETs.","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"74 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132679260","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 : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967068
Nur Afny C. Andryani, D. Sudiana, D. Gunawan
Compressive Sensing (CS) framework is mathematical framework to recover the signal by having less measurement data compared to Shannon-Nyquist theorem. It indicates the underdetermined linear system where the dimension of measurement data is much lower compared to dimension of the projected data. The basic idea of CS is to shift the sensing load into image reconstruction load. Thus, even though the sensing process produces less measurement data subject to the recovery data dimension, the CS theoretically is able to perform good signal recovery. Theoretically, CS should be working for natural sparse signal or sparse in transform domain. Electrical Capacitance Volume Tomography (ECVT) imaging forms naturally underdetermined linear system since the dimension of capacitance as the measurement data is much lower compared to dimension of predicted permittivity distribution. In addition, the ECVT signal is naturally sparse. Thus, the compressive sensing framework is theoretically promising for ECVT imaging. This paper will introduce ECVT static imaging based on compressive sensing framework. The early simulations show that compressive sensing with l1 optimization on the sparse recovery succeed to eliminate the elongation error on ECVT imaging by ILBP (Iterative Learning Back Propagation).
{"title":"Electrical Capacitance Volume Tomography static imaging using Compressive Sensing with l1 sparse recovery","authors":"Nur Afny C. Andryani, D. Sudiana, D. Gunawan","doi":"10.1109/ICSIGSYS.2017.7967068","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967068","url":null,"abstract":"Compressive Sensing (CS) framework is mathematical framework to recover the signal by having less measurement data compared to Shannon-Nyquist theorem. It indicates the underdetermined linear system where the dimension of measurement data is much lower compared to dimension of the projected data. The basic idea of CS is to shift the sensing load into image reconstruction load. Thus, even though the sensing process produces less measurement data subject to the recovery data dimension, the CS theoretically is able to perform good signal recovery. Theoretically, CS should be working for natural sparse signal or sparse in transform domain. Electrical Capacitance Volume Tomography (ECVT) imaging forms naturally underdetermined linear system since the dimension of capacitance as the measurement data is much lower compared to dimension of predicted permittivity distribution. In addition, the ECVT signal is naturally sparse. Thus, the compressive sensing framework is theoretically promising for ECVT imaging. This paper will introduce ECVT static imaging based on compressive sensing framework. The early simulations show that compressive sensing with l1 optimization on the sparse recovery succeed to eliminate the elongation error on ECVT imaging by ILBP (Iterative Learning Back Propagation).","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132280303","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 : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967028
TaeIll Kim, Chulsung Park, Sungkwon Park
Recently, with the construction of the All IP network infrastructure, Hybrid Fiber Coaxial (HFC) cable network is undergoing a digital transition based on optical IP network. HFC is a communication technology in which fiber optic cables and coaxial cables are used in different parts of the network to transport broadband content such as video, data and voice. Accordingly, there is applying Radio Over IP (RoIP) technology that is a generic term that describes the application of Voice over IP (VoIP) on two-way radio networks. RoIP is a technology for transmitting radio frequency signals using a digital IP network. Transmission of RF signals using the IP network requires conversion to digital data. However, a large amount of data is a generated during the digital conversion process. This makes efficient data transmission impossible. In this paper, we reduce the amount of data by using up/down sampling and Huffman compression methods. By using this method, it can transmit RF signal efficiently. Then, we measured compression ratio and Error Vector Magnitude (EVM), which is a performance degradation index due to compression, for performance evaluation based on modulation.
{"title":"The application of compression methods for RoIP data transmission efficiency in the HFC network","authors":"TaeIll Kim, Chulsung Park, Sungkwon Park","doi":"10.1109/ICSIGSYS.2017.7967028","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967028","url":null,"abstract":"Recently, with the construction of the All IP network infrastructure, Hybrid Fiber Coaxial (HFC) cable network is undergoing a digital transition based on optical IP network. HFC is a communication technology in which fiber optic cables and coaxial cables are used in different parts of the network to transport broadband content such as video, data and voice. Accordingly, there is applying Radio Over IP (RoIP) technology that is a generic term that describes the application of Voice over IP (VoIP) on two-way radio networks. RoIP is a technology for transmitting radio frequency signals using a digital IP network. Transmission of RF signals using the IP network requires conversion to digital data. However, a large amount of data is a generated during the digital conversion process. This makes efficient data transmission impossible. In this paper, we reduce the amount of data by using up/down sampling and Huffman compression methods. By using this method, it can transmit RF signal efficiently. Then, we measured compression ratio and Error Vector Magnitude (EVM), which is a performance degradation index due to compression, for performance evaluation based on modulation.","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122613438","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 : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967056
Prerna Singh, R. Mukundan, Rex de Ryke
Speckle noise is the primary cause of degradation of quality, resolution and contrast in ultrasound (US) images. Speckle in ultrasound B-mode images is caused by additive and destructive interference of ultrasound signals received from scatterers. Methods for analysing and reducing noise in US images require accurate models of image formation that can generate ground truth data. Such synthetic images that have the essential noise characteristics of real ultrasound images would be valuable for testing and evaluation of speckle reduction algorithms. This paper introduces three sampling models: radial polar, uniform grid and radial uniform that could be used for generating synthetic images. The paper also outlines the implementation aspects using pseudo-codes, and provides a comparative analysis between the proposed models. Experimental results showing variations in noise features with model parameters are also given.
{"title":"Synthetic models of ultrasound image formation for speckle noise simulation and analysis","authors":"Prerna Singh, R. Mukundan, Rex de Ryke","doi":"10.1109/ICSIGSYS.2017.7967056","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967056","url":null,"abstract":"Speckle noise is the primary cause of degradation of quality, resolution and contrast in ultrasound (US) images. Speckle in ultrasound B-mode images is caused by additive and destructive interference of ultrasound signals received from scatterers. Methods for analysing and reducing noise in US images require accurate models of image formation that can generate ground truth data. Such synthetic images that have the essential noise characteristics of real ultrasound images would be valuable for testing and evaluation of speckle reduction algorithms. This paper introduces three sampling models: radial polar, uniform grid and radial uniform that could be used for generating synthetic images. The paper also outlines the implementation aspects using pseudo-codes, and provides a comparative analysis between the proposed models. Experimental results showing variations in noise features with model parameters are also given.","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128626405","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 : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967040
Husnawati, Gita Fadila Fitriana, S. Nurmaini
The olfactory system of swarm robot are needed to build reliable early gas leak detection, for decreasing the bad impact in the environment. This paper proposes hybrid methods related to locating the gas leak and identify the type of gas by using swarm robots. The propose hybrid methods combination with three algorithms and with three functions, such as the fuzzy logic system for swarm robot navigation, support vector machine (SVM) for gas identification, and particle swarm optimization (PSO) for route optimization. The result of this research shows the set of methods can be implemented to localize gas leak source at the indoor environment in a real experiment. This research is expected by using this method, the swarm robots have the ability to identify the source of the gas leak and localize the target in a short time without collision with the obstacle in the swarm environment.
{"title":"The development of hybrid methods in simple swarm robots for gas leak localization","authors":"Husnawati, Gita Fadila Fitriana, S. Nurmaini","doi":"10.1109/ICSIGSYS.2017.7967040","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967040","url":null,"abstract":"The olfactory system of swarm robot are needed to build reliable early gas leak detection, for decreasing the bad impact in the environment. This paper proposes hybrid methods related to locating the gas leak and identify the type of gas by using swarm robots. The propose hybrid methods combination with three algorithms and with three functions, such as the fuzzy logic system for swarm robot navigation, support vector machine (SVM) for gas identification, and particle swarm optimization (PSO) for route optimization. The result of this research shows the set of methods can be implemented to localize gas leak source at the indoor environment in a real experiment. This research is expected by using this method, the swarm robots have the ability to identify the source of the gas leak and localize the target in a short time without collision with the obstacle in the swarm environment.","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123890373","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 : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967059
Zhaowei Liu, Yung-Yao Chen, S. Hidayati, S. Chien, Feng-Chia Chang, K. Hua
The rapid growth of 3D model resources for 3D printing has created an urgent need for 3D model retrieval systems. Benefiting from the evolution of hardware devices, visualized 3D models can be easily rendered using a tablet computer or handheld mobile device. In this paper, we present a novel 3D model retrieval method involving view-based features and deep learning. Because 2D images are highly distinguishable, constructing a 3D model from multiple 2D views is one of the most common methods of 3D model retrieval. Normalization is typically challenging and time-consuming for view-based retrieval methods; however, this work utilized an unsupervised deep learning technique, called Autoencoder, to refine compact view-based features. Therefore, the proposed method is rotation-invariant, requiring only the normalization of the translation and the scale of the 3D models in the dataset. For robustness, we applied Fourier descriptors and Zernike moments to represent the 2D features. The experimental results testing our method on the online Princeton Shape Benchmark Dataset demonstrate more accurate retrieval performance than other existing methods.
{"title":"3D model retrieval based on deep Autoencoder neural networks","authors":"Zhaowei Liu, Yung-Yao Chen, S. Hidayati, S. Chien, Feng-Chia Chang, K. Hua","doi":"10.1109/ICSIGSYS.2017.7967059","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967059","url":null,"abstract":"The rapid growth of 3D model resources for 3D printing has created an urgent need for 3D model retrieval systems. Benefiting from the evolution of hardware devices, visualized 3D models can be easily rendered using a tablet computer or handheld mobile device. In this paper, we present a novel 3D model retrieval method involving view-based features and deep learning. Because 2D images are highly distinguishable, constructing a 3D model from multiple 2D views is one of the most common methods of 3D model retrieval. Normalization is typically challenging and time-consuming for view-based retrieval methods; however, this work utilized an unsupervised deep learning technique, called Autoencoder, to refine compact view-based features. Therefore, the proposed method is rotation-invariant, requiring only the normalization of the translation and the scale of the 3D models in the dataset. For robustness, we applied Fourier descriptors and Zernike moments to represent the 2D features. The experimental results testing our method on the online Princeton Shape Benchmark Dataset demonstrate more accurate retrieval performance than other existing methods.","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116934215","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 : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967062
Anh-Tuan Nguyen, T. Matsubara, T. Kurokawa
In the present paper, we consider a co-array as a coprime array or a nested array. Pal et al. proposed a method to extend a co-array to a larger virtual array, then implemented the spatial smoothing technique to construct the covariance matrix of a virtual uniform linear array (ULA). Thus subspace-based direction of arrival (DOA) estimation algorithms can be used to detect more sources than the number of array elements. However, since the subspace-based DOA estimation methods are applied, the DOA estimation accuracy depends on the performance of source number estimation. By employing a set of Toeplitz matrices, we propose a DOA estimation method for co-array, which does not need to know the number of sources prior to computing the spatial spectrum. Computer simulations are provided to demonstrate effectiveness of the proposed approach.
{"title":"DOA estimation method for co-arrays with unknown number of sources","authors":"Anh-Tuan Nguyen, T. Matsubara, T. Kurokawa","doi":"10.1109/ICSIGSYS.2017.7967062","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967062","url":null,"abstract":"In the present paper, we consider a co-array as a coprime array or a nested array. Pal et al. proposed a method to extend a co-array to a larger virtual array, then implemented the spatial smoothing technique to construct the covariance matrix of a virtual uniform linear array (ULA). Thus subspace-based direction of arrival (DOA) estimation algorithms can be used to detect more sources than the number of array elements. However, since the subspace-based DOA estimation methods are applied, the DOA estimation accuracy depends on the performance of source number estimation. By employing a set of Toeplitz matrices, we propose a DOA estimation method for co-array, which does not need to know the number of sources prior to computing the spatial spectrum. Computer simulations are provided to demonstrate effectiveness of the proposed approach.","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"970 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123077384","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 : 2017-05-01DOI: 10.1109/ICSIGSYS.2017.7967038
Juansyah, K. Anwar
In this paper, we propose header detection technique for massive internet of things (IoT) networks over Rayleigh fading channels. We consider coded random access (CRA) as a multiple access scheme for IoT to keep low computational complexity of detection, where header detection is of significant important. We perform header detection by computing cross correlation using Hadamard codes. Hadamard codes are chosen because of its simplicity to be generated, where the value of the matrix component is only ±1. To avoid data rate loss due to bits allocation to header, the length of the header should be kept small. We use Hadamard codes with size of 128×128 as a header for packets suffering from Rayleigh fading channels. We also use capture effect algorithm to improve detection performances when multiple IoT devices are transmitting at the same time-slot. Although the algorithms is simple, we found that header detection using Hadamard codes for massive IoT connections over Rayleigh fading channels is still providing high accuracy, which is suitable for future massive IoT wireless networks.
{"title":"Header detection for massive IoT wireless networks over Rayleigh fading channels","authors":"Juansyah, K. Anwar","doi":"10.1109/ICSIGSYS.2017.7967038","DOIUrl":"https://doi.org/10.1109/ICSIGSYS.2017.7967038","url":null,"abstract":"In this paper, we propose header detection technique for massive internet of things (IoT) networks over Rayleigh fading channels. We consider coded random access (CRA) as a multiple access scheme for IoT to keep low computational complexity of detection, where header detection is of significant important. We perform header detection by computing cross correlation using Hadamard codes. Hadamard codes are chosen because of its simplicity to be generated, where the value of the matrix component is only ±1. To avoid data rate loss due to bits allocation to header, the length of the header should be kept small. We use Hadamard codes with size of 128×128 as a header for packets suffering from Rayleigh fading channels. We also use capture effect algorithm to improve detection performances when multiple IoT devices are transmitting at the same time-slot. Although the algorithms is simple, we found that header detection using Hadamard codes for massive IoT connections over Rayleigh fading channels is still providing high accuracy, which is suitable for future massive IoT wireless networks.","PeriodicalId":212068,"journal":{"name":"2017 International Conference on Signals and Systems (ICSigSys)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125333856","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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