Pub Date : 2021-07-27DOI: 10.1109/NCC52529.2021.9530010
P. Das, S. Meher
In healthcare, microscopic analysis of blood-cells is considered significant in diagnosing acute lymphocytic leukemia (ALL). Manual microscopic analysis is an error-prone and timetaking process. Hence, there is a need for automatic leukemia diagnosis. Transfer learning is becoming an emerging medical image processing technique because of its superior performance in small databases, unlike traditional deep learning techniques. In this paper, we have suggested a new transfer-learning-based automatic ALL detection method. A light-weight, highly computationally efficient SqueezNet is applied to classify malignant and benign with promising classification performance. Channel shuffling and pointwise-group convolution boost its performance and make it faster. The proposed method is validated on the standard ALLIDB1 and ALLIDB2 databases. The experimental results show that in most cases, the proposed ALL detection model outperforms Xception, NasNetMobile, VGG19, and ResNet50 with promising quantitative performance.
{"title":"Transfer Learning-Based Automatic Detection of Acute Lymphocytic Leukemia","authors":"P. Das, S. Meher","doi":"10.1109/NCC52529.2021.9530010","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530010","url":null,"abstract":"In healthcare, microscopic analysis of blood-cells is considered significant in diagnosing acute lymphocytic leukemia (ALL). Manual microscopic analysis is an error-prone and timetaking process. Hence, there is a need for automatic leukemia diagnosis. Transfer learning is becoming an emerging medical image processing technique because of its superior performance in small databases, unlike traditional deep learning techniques. In this paper, we have suggested a new transfer-learning-based automatic ALL detection method. A light-weight, highly computationally efficient SqueezNet is applied to classify malignant and benign with promising classification performance. Channel shuffling and pointwise-group convolution boost its performance and make it faster. The proposed method is validated on the standard ALLIDB1 and ALLIDB2 databases. The experimental results show that in most cases, the proposed ALL detection model outperforms Xception, NasNetMobile, VGG19, and ResNet50 with promising quantitative performance.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116224414","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 : 2021-07-27DOI: 10.1109/NCC52529.2021.9530171
L. V, A. R, S. G.
This work proposes a method to improve the automated classification and detection of macular diseases using retinal Optical Coherence Tomography (OCT) images by utilizing the fusion of two pre trained deep learning networks. The concatenation of feature vectors extracted from each of the pre trained deep learning model is performed to obtain a long feature vector of the fused network. The experimental results proved that the fusion of two Deep Convolution Neural Network (DCNN) achieves better classification accuracy compared to the individual DCNN models on the same dataset. The automated retinal OCT image classification can assist the large-scale screening and the diagnosis recommendation for an ophthalmologist.
{"title":"Automated Macular Disease Detection using Retinal Optical Coherence Tomography images by Fusion of Deep Learning Networks","authors":"L. V, A. R, S. G.","doi":"10.1109/NCC52529.2021.9530171","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530171","url":null,"abstract":"This work proposes a method to improve the automated classification and detection of macular diseases using retinal Optical Coherence Tomography (OCT) images by utilizing the fusion of two pre trained deep learning networks. The concatenation of feature vectors extracted from each of the pre trained deep learning model is performed to obtain a long feature vector of the fused network. The experimental results proved that the fusion of two Deep Convolution Neural Network (DCNN) achieves better classification accuracy compared to the individual DCNN models on the same dataset. The automated retinal OCT image classification can assist the large-scale screening and the diagnosis recommendation for an ophthalmologist.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122971424","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 : 2021-07-27DOI: 10.1109/NCC52529.2021.9530119
Chothmal Kumawat, Vinod Pankajakshan
Identification of the type of chroma subsampling in a decompressed JPEG color image stored in a lossless format is important in forensic analysis. It is useful in many forensic scenarios like detecting localized forgery and estimating the quantization step sizes in the chroma planes for source camera identification. In this work, we propose a machine learning-based method capable of identifying the chroma subsampling used in the compression process. The method is based on detecting the change in adjacent pixel correlations due to upsampling process in JPEG decompression. These changes in the correlation are measured using the two-sample Kolmogorov-Smirnov (KS) test statistic in different directions. The experimental results show the efficacy of the proposed method in identifying the chroma subsampling scheme.
{"title":"Forensics of Decompressed JPEG Color Images Based on Chroma Subsampling","authors":"Chothmal Kumawat, Vinod Pankajakshan","doi":"10.1109/NCC52529.2021.9530119","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530119","url":null,"abstract":"Identification of the type of chroma subsampling in a decompressed JPEG color image stored in a lossless format is important in forensic analysis. It is useful in many forensic scenarios like detecting localized forgery and estimating the quantization step sizes in the chroma planes for source camera identification. In this work, we propose a machine learning-based method capable of identifying the chroma subsampling used in the compression process. The method is based on detecting the change in adjacent pixel correlations due to upsampling process in JPEG decompression. These changes in the correlation are measured using the two-sample Kolmogorov-Smirnov (KS) test statistic in different directions. The experimental results show the efficacy of the proposed method in identifying the chroma subsampling scheme.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"11 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182363","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 : 2021-07-27DOI: 10.1109/NCC52529.2021.9530092
Srinivasulu Jogi, Priyanka Kokil
This article considers the global stability analysis of interfered nonlinear digital filtering schemes implemented with fixed-point arithmetic. The proposed approach uses Hankel norm to verify the reduction of undesired memory effects of previous inputs (ringing) on future responses in nonlinear digital filters with saturation overflow nonlinearity and external disturbance. Also, the proposed criterion verifies the asymptotic stability of nonlinear digital filter without external disturbance. With the obtained results, it is shown that the suggested criterion is less restrictive than the existing criterion in the literature. By using Lyapunov stability theory, sector-based saturation nonlinearity, and Lipschitz continuity, the approach is framed in linear matrix inequality (LMI)-constraints. The efficacy, validity, and reduced conservatism of presented criterion are tested with two numerical examples.
{"title":"Improved Hankel Norm Criterion for Interfered Nonlinear Digital Filters Subjected to Hardware Constraints","authors":"Srinivasulu Jogi, Priyanka Kokil","doi":"10.1109/NCC52529.2021.9530092","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530092","url":null,"abstract":"This article considers the global stability analysis of interfered nonlinear digital filtering schemes implemented with fixed-point arithmetic. The proposed approach uses Hankel norm to verify the reduction of undesired memory effects of previous inputs (ringing) on future responses in nonlinear digital filters with saturation overflow nonlinearity and external disturbance. Also, the proposed criterion verifies the asymptotic stability of nonlinear digital filter without external disturbance. With the obtained results, it is shown that the suggested criterion is less restrictive than the existing criterion in the literature. By using Lyapunov stability theory, sector-based saturation nonlinearity, and Lipschitz continuity, the approach is framed in linear matrix inequality (LMI)-constraints. The efficacy, validity, and reduced conservatism of presented criterion are tested with two numerical examples.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114292015","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 : 2021-07-27DOI: 10.1109/NCC52529.2021.9530164
P. Barik, Ashu Dayal Chaurasiya, R. Datta, Chetna Singhal
Device-to-Device (D2D) communication has been proven an efficient technique in the present and upcoming cellular networks for improving network performance. Many a time, a direct D2D link may not be available due to longer distance or poor channel quality between two devices. Multi-hop D2D is an effective solution to overcome this limitation of direct D2D communication. Here relay devices help in forwarding data from transmitters to the receivers through single or multiple hops. However, finding suitable fixed relays and their locations is a complex problem, which does not have an efficient solution. In this paper, we have used UAVs (drones) that act as relays for forwarding data between two devices. The proposed approach serves more out of direct range D2D users resulting in a reduced churn rate of the system. We find the trajectory of such UAVs with the help of active user prediction using Neural Networks (NN) to serve all the D2D users by increasing the coverage range of D2D communications. We have estimated the number of active D2D users in every zone covered by each drone and intra and inter-drone communication trajectories. It is also shown that the packet loss ratio remains within the acceptable limit for the proposed trajectories of the UAVs by choosing a sufficient buffer length.
{"title":"Trajectory Prediction of UAVs for Relay-assisted D2D Communication Using Machine Learning","authors":"P. Barik, Ashu Dayal Chaurasiya, R. Datta, Chetna Singhal","doi":"10.1109/NCC52529.2021.9530164","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530164","url":null,"abstract":"Device-to-Device (D2D) communication has been proven an efficient technique in the present and upcoming cellular networks for improving network performance. Many a time, a direct D2D link may not be available due to longer distance or poor channel quality between two devices. Multi-hop D2D is an effective solution to overcome this limitation of direct D2D communication. Here relay devices help in forwarding data from transmitters to the receivers through single or multiple hops. However, finding suitable fixed relays and their locations is a complex problem, which does not have an efficient solution. In this paper, we have used UAVs (drones) that act as relays for forwarding data between two devices. The proposed approach serves more out of direct range D2D users resulting in a reduced churn rate of the system. We find the trajectory of such UAVs with the help of active user prediction using Neural Networks (NN) to serve all the D2D users by increasing the coverage range of D2D communications. We have estimated the number of active D2D users in every zone covered by each drone and intra and inter-drone communication trajectories. It is also shown that the packet loss ratio remains within the acceptable limit for the proposed trajectories of the UAVs by choosing a sufficient buffer length.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121578920","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 : 2021-07-27DOI: 10.1109/NCC52529.2021.9530075
Ajay Salas, Sarath Babu, B. S. Manoj
Next generation communication infrastructures are characterized by customized network environments deployed for meeting the application/user specific needs as well as for achieving the required Quality-of-Service (QoS). The surge of mobile devices and their applications form a major bottleneck in realizing the QoS due to the resource constraints in mobile devices and the uncertain mobility pattern of users. Delay/Disruption Tolerant Networking (DTN) approaches are employed to cope with the issues in dynamic wireless environments such as intermittent connectivity, high error rate and packet loss, and network heterogeneity. However, the overhead required in terms of protocols, memory, and computational power in traditional DTN approaches may not be suitable for energy-constrained mobile devices. Therefore, we propose a Light-Weight DTN (LWDTN) framework for resource-constrained delay/disruption-prone wireless environments. We follow the traditional custody-transfer approach in designing the LWDTN framework with three types of bundles involving minimal header fields. The experimental results from a DTN testbed show the efficacy of LWDTN in delivering around 80% packets within the feasible time.
{"title":"A Light-Weight Delay Tolerant Networking Framework for Resource-Constrained Environments","authors":"Ajay Salas, Sarath Babu, B. S. Manoj","doi":"10.1109/NCC52529.2021.9530075","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530075","url":null,"abstract":"Next generation communication infrastructures are characterized by customized network environments deployed for meeting the application/user specific needs as well as for achieving the required Quality-of-Service (QoS). The surge of mobile devices and their applications form a major bottleneck in realizing the QoS due to the resource constraints in mobile devices and the uncertain mobility pattern of users. Delay/Disruption Tolerant Networking (DTN) approaches are employed to cope with the issues in dynamic wireless environments such as intermittent connectivity, high error rate and packet loss, and network heterogeneity. However, the overhead required in terms of protocols, memory, and computational power in traditional DTN approaches may not be suitable for energy-constrained mobile devices. Therefore, we propose a Light-Weight DTN (LWDTN) framework for resource-constrained delay/disruption-prone wireless environments. We follow the traditional custody-transfer approach in designing the LWDTN framework with three types of bundles involving minimal header fields. The experimental results from a DTN testbed show the efficacy of LWDTN in delivering around 80% packets within the feasible time.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127717315","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 : 2021-07-27DOI: 10.1109/NCC52529.2021.9530045
Anchal Yadav, P. Babu, Monika Agrwal, S. Joshi
The techniques like fMRI, CT scans, etc are used to localize the activity in the brain. Though these techniques have a high spatial resolution they are very expensive and uncomfortable for the patients. On the other hand, EEG signals can be obtained quite comfortably but suffer from low spatial resolution. A lot of research is being done to effectively extract spatial information from EEG signals. Many inverse techniques like MNE, LORETA, sLORETA, etc are available. All these methods can detect only a few sources and their performance degrades at low SNR. In this paper, covariance-based methods are used to estimate the location of brain activity from EEG signals such as SPICE (sparse iterative covariance-based estimation), and LIKES (likelihood-based estimation of sparse parameters). Intense simulation work has been presented to show that the proposed methods outperform the state-of-the-art methods.
{"title":"Brain Source Localization with covariance fitting approaches","authors":"Anchal Yadav, P. Babu, Monika Agrwal, S. Joshi","doi":"10.1109/NCC52529.2021.9530045","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530045","url":null,"abstract":"The techniques like fMRI, CT scans, etc are used to localize the activity in the brain. Though these techniques have a high spatial resolution they are very expensive and uncomfortable for the patients. On the other hand, EEG signals can be obtained quite comfortably but suffer from low spatial resolution. A lot of research is being done to effectively extract spatial information from EEG signals. Many inverse techniques like MNE, LORETA, sLORETA, etc are available. All these methods can detect only a few sources and their performance degrades at low SNR. In this paper, covariance-based methods are used to estimate the location of brain activity from EEG signals such as SPICE (sparse iterative covariance-based estimation), and LIKES (likelihood-based estimation of sparse parameters). Intense simulation work has been presented to show that the proposed methods outperform the state-of-the-art methods.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132218283","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 : 2021-07-27DOI: 10.1109/NCC52529.2021.9530035
Jagabandhu Mishra, Ayush Agarwal, S. Prasanna
Spoken language diarization (SLD) is a task to perform automatic segmentation and labeling of the languages present in a given code-switched speech utterance. Inspiring from the way humans perform SLD (i.e capturing the language specific long term information), this work has proposed an acoustic-phonetic approach to perform SLD. This acoustic phonetic approach consists of an attention based neural network modelling to capture the language specific information and a Gaussian smoothing approach to locate the language change points. From the experimental study, it has been observed that the proposed approach performs better when dealing with code-switched segment containing monolingual segments of longer duration. However, the performance of the approach decreases with decrease in the monolingual segment duration. This issue poses a challenge in the further exploration of the proposed approach.
{"title":"Spoken Language Diarization Using an Attention based Neural Network","authors":"Jagabandhu Mishra, Ayush Agarwal, S. Prasanna","doi":"10.1109/NCC52529.2021.9530035","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530035","url":null,"abstract":"Spoken language diarization (SLD) is a task to perform automatic segmentation and labeling of the languages present in a given code-switched speech utterance. Inspiring from the way humans perform SLD (i.e capturing the language specific long term information), this work has proposed an acoustic-phonetic approach to perform SLD. This acoustic phonetic approach consists of an attention based neural network modelling to capture the language specific information and a Gaussian smoothing approach to locate the language change points. From the experimental study, it has been observed that the proposed approach performs better when dealing with code-switched segment containing monolingual segments of longer duration. However, the performance of the approach decreases with decrease in the monolingual segment duration. This issue poses a challenge in the further exploration of the proposed approach.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134015020","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 : 2021-07-27DOI: 10.1109/NCC52529.2021.9530110
K. S. Sanila, R. Neelakandan
Receive spatial modulation (RSM) is one of the most promising paradigms that significantly reduces the receiver's computational complexity. However, to assure the linear precoding operation at the transmitter side, RSM systems have to be under-determined. We propose a transmission scheme that divides antennas at the transmitter into Gt transmit antenna groups (TAGs) and antennas at the receiver into Gr receive antenna groups (RAGs) for exploiting the SM concept at the transceiver ends. Additionally, we extend the notion of generalized spatial modulation (GSM) to a new precoding-aided massive multiple-input multiple-output (mMIMO) system and formulate the structure, particularly in an activated antenna group at the transmitter and receiver. We refer to it as an enhanced receive GSM (ERGSM) system. The antenna grouping makes the proposed GRSM based scheme suitable for both the underdetermined and over-determined massive MIMO architectures according to the distribution of the number of TAGs and RAGs and thus increases the resilience of the system. We project a low complexity sub-optimal detection algorithm for the proposed scheme. Further, we computed the complex calculations required for the system and compared them to the other conventional techniques. Also, we present numerical results to substantiate our ideas.
{"title":"Enhanced Precoding aided Generalized Spatial Modulation for Massive MIMO Systems","authors":"K. S. Sanila, R. Neelakandan","doi":"10.1109/NCC52529.2021.9530110","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530110","url":null,"abstract":"Receive spatial modulation (RSM) is one of the most promising paradigms that significantly reduces the receiver's computational complexity. However, to assure the linear precoding operation at the transmitter side, RSM systems have to be under-determined. We propose a transmission scheme that divides antennas at the transmitter into Gt transmit antenna groups (TAGs) and antennas at the receiver into Gr receive antenna groups (RAGs) for exploiting the SM concept at the transceiver ends. Additionally, we extend the notion of generalized spatial modulation (GSM) to a new precoding-aided massive multiple-input multiple-output (mMIMO) system and formulate the structure, particularly in an activated antenna group at the transmitter and receiver. We refer to it as an enhanced receive GSM (ERGSM) system. The antenna grouping makes the proposed GRSM based scheme suitable for both the underdetermined and over-determined massive MIMO architectures according to the distribution of the number of TAGs and RAGs and thus increases the resilience of the system. We project a low complexity sub-optimal detection algorithm for the proposed scheme. Further, we computed the complex calculations required for the system and compared them to the other conventional techniques. Also, we present numerical results to substantiate our ideas.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126570751","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 : 2021-07-27DOI: 10.1109/NCC52529.2021.9530026
Purushottama Lingadevaru, Bethi Pardhasaradhi, P. Srihari, G. Sharma
Passive radar detects targets using the reflections of electromagnetic signals illuminated by unintended sources of opportunity in the given surveillance region. The illuminators of opportunity (IOO) like FM, DVB, DAB, LTE, WiMax, and radio frequency signals are used for the passive radar depending on the availability, frequency of operation and, type of application. This paper proposes the upcoming 5G New Radio waveform (5G NR) as an IOO for passive bistatic radar. The 5G NR waveform is used to perform parametric analysis of passive bistatic radar. The radar parameters like range resolution, velocity resolution, range product, maximum unambiguous PRF, and Cassini's ovals are investigated. Further, the 5G NR IOO is compared against existing LTE and other IOOs. Simulation results reveals that all the radar parameters are outperforming for the 5G NR waveform, claiming that 5G NR is a potential candidate for the future IOO.
{"title":"Analysis of 5G New Radio Waveform as an Illuminator of Opportunity for Passive Bistatic Radar","authors":"Purushottama Lingadevaru, Bethi Pardhasaradhi, P. Srihari, G. Sharma","doi":"10.1109/NCC52529.2021.9530026","DOIUrl":"https://doi.org/10.1109/NCC52529.2021.9530026","url":null,"abstract":"Passive radar detects targets using the reflections of electromagnetic signals illuminated by unintended sources of opportunity in the given surveillance region. The illuminators of opportunity (IOO) like FM, DVB, DAB, LTE, WiMax, and radio frequency signals are used for the passive radar depending on the availability, frequency of operation and, type of application. This paper proposes the upcoming 5G New Radio waveform (5G NR) as an IOO for passive bistatic radar. The 5G NR waveform is used to perform parametric analysis of passive bistatic radar. The radar parameters like range resolution, velocity resolution, range product, maximum unambiguous PRF, and Cassini's ovals are investigated. Further, the 5G NR IOO is compared against existing LTE and other IOOs. Simulation results reveals that all the radar parameters are outperforming for the 5G NR waveform, claiming that 5G NR is a potential candidate for the future IOO.","PeriodicalId":414087,"journal":{"name":"2021 National Conference on Communications (NCC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132567032","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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