Pub Date : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840814
Bharat Runwal, Vivek, Sandeep Kumar
Graph neural network (GNN) is achieving remarkable performances in a variety of application domains. However, GNN is vulnerable to noise and adversarial attacks in input data. Making GNN robust against noises and adversarial attacks is an important problem. The existing defense methods for GNNs are computationally demanding, are not scalable, and are architecture dependent. In this paper, we propose a generic framework for robustifying GNN known as Weighted Laplacian GNN (RWLGNN). The method combines Weighted Graph Laplacian learning with the GNN implementation. The proposed method benefits from the positive semi-definiteness property of Laplacian matrix, feature smoothness, and latent features via formulating a unified optimization framework, which ensures the adversarial/noisy edges are discarded and connections in the graph are appropriately weighted. For demonstration, the experiments are conducted with Graph convolutional neural network(GCNN) architecture, however, the proposed framework is easily amenable to any existing GNN architecture. The simulation results with benchmark dataset establish the efficacy of the proposed method over the state-of-the-art methods, both in accuracy and computational efficiency.
{"title":"Robustifying GNN Via Weighted Laplacian","authors":"Bharat Runwal, Vivek, Sandeep Kumar","doi":"10.1109/SPCOM55316.2022.9840814","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840814","url":null,"abstract":"Graph neural network (GNN) is achieving remarkable performances in a variety of application domains. However, GNN is vulnerable to noise and adversarial attacks in input data. Making GNN robust against noises and adversarial attacks is an important problem. The existing defense methods for GNNs are computationally demanding, are not scalable, and are architecture dependent. In this paper, we propose a generic framework for robustifying GNN known as Weighted Laplacian GNN (RWLGNN). The method combines Weighted Graph Laplacian learning with the GNN implementation. The proposed method benefits from the positive semi-definiteness property of Laplacian matrix, feature smoothness, and latent features via formulating a unified optimization framework, which ensures the adversarial/noisy edges are discarded and connections in the graph are appropriately weighted. For demonstration, the experiments are conducted with Graph convolutional neural network(GCNN) architecture, however, the proposed framework is easily amenable to any existing GNN architecture. The simulation results with benchmark dataset establish the efficacy of the proposed method over the state-of-the-art methods, both in accuracy and computational efficiency.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130810975","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840855
A. Poreddy, Balasubramanyam Appina
In this paper, we develop a framework to assess the perceptual quality of Virtual Reality (VR) images by studying the joint dependencies between luminance and disparity pairs using Bivariate Generalized Gaussian Distribution (BGGD) model. We compute model parameters ($alpha, beta$) of BGGD at multi-scale and multi-orient steerable pyramid decomposition of the cube map projection (CMP) faces of both left and right views of a VR image. We learn Multivariate Gaussian (MVG) model parameters from BGGD features of CMP faces of pristine images as a reference quality representative. We compute Mahalanobis distance between pristine MVG model parameters and distorted image BGGD features to estimate the joint luminance and disparity quality score of a CMP face of a test VR image. We generate an inner map from saliency and phase congruency maps of CMP faces of both left and right views of a VR image. We compute entropy scores of the inner map to pool the joint luminance and disparity quality score of a VR image. Further, we apply IL-NIQE model on CMP faces to derive the overall spatial quality score of a test VR image. Finally, we pool the spatial IL-NIQE score and CMP face level quality score to estimate the overall quality score of a test VR image. The proposed model, dubbed Blind Virtual Reality Image Quality Evaluator (BVRIQE) delivered a consistent performance across all distortion types of the LIVE 3D VR IQA dataset.
本文采用双变量广义高斯分布(BGGD)模型，通过研究亮度和视差对之间的联合依赖关系，建立了一个评估虚拟现实(VR)图像感知质量的框架。我们在对VR图像的左右视图的立方体地图投影(CMP)面进行多尺度和多方向可定向金字塔分解时计算BGGD的模型参数($alpha, beta$)。我们从原始图像的CMP人脸的BGGD特征中学习多元高斯(MVG)模型参数作为参考质量代表。我们计算原始MVG模型参数和扭曲图像BGGD特征之间的马氏距离，以估计测试VR图像的CMP人脸的联合亮度和视差质量分数。我们从VR图像的左右视图的CMP面部的显着性和相位一致性映射中生成内部地图。我们计算内部映射的熵值得分，以汇集VR图像的亮度和视差质量联合得分。此外，我们将IL-NIQE模型应用于CMP人脸，以获得测试VR图像的整体空间质量分数。最后，我们将空间IL-NIQE评分和CMP面部水平质量评分汇总，以估计测试VR图像的总体质量评分。所提出的模型，被称为盲虚拟现实图像质量评估器(BVRIQE)，在LIVE 3D VR IQA数据集的所有失真类型中提供了一致的性能。
{"title":"BVRIQE: A Completely Blind No Reference Virtual Reality Image Quality Evaluator","authors":"A. Poreddy, Balasubramanyam Appina","doi":"10.1109/SPCOM55316.2022.9840855","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840855","url":null,"abstract":"In this paper, we develop a framework to assess the perceptual quality of Virtual Reality (VR) images by studying the joint dependencies between luminance and disparity pairs using Bivariate Generalized Gaussian Distribution (BGGD) model. We compute model parameters ($alpha, beta$) of BGGD at multi-scale and multi-orient steerable pyramid decomposition of the cube map projection (CMP) faces of both left and right views of a VR image. We learn Multivariate Gaussian (MVG) model parameters from BGGD features of CMP faces of pristine images as a reference quality representative. We compute Mahalanobis distance between pristine MVG model parameters and distorted image BGGD features to estimate the joint luminance and disparity quality score of a CMP face of a test VR image. We generate an inner map from saliency and phase congruency maps of CMP faces of both left and right views of a VR image. We compute entropy scores of the inner map to pool the joint luminance and disparity quality score of a VR image. Further, we apply IL-NIQE model on CMP faces to derive the overall spatial quality score of a test VR image. Finally, we pool the spatial IL-NIQE score and CMP face level quality score to estimate the overall quality score of a test VR image. The proposed model, dubbed Blind Virtual Reality Image Quality Evaluator (BVRIQE) delivered a consistent performance across all distortion types of the LIVE 3D VR IQA dataset.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132273832","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840820
Binoy Saha, Neha Shah, Sukhendu Das
Open-ended surveillance task for a robot in an unspecified environment using only an RGB camera, has not been addressed at length in literature. This is unlike the popular scenario of path planning where both the target and environments are often known. We focus on the task of a robot which needs to estimate a realistic depiction of the surrounding 3D environment, including the location of obstacles and free space to navigate in the scene within the view field. In this paper, we propose an unsupervised algorithm to iteratively compute an optimal direction for maximal unhindered movement in the scene. This task is challenging when presented with only a single RGB view of the scene, without the use of any online depth sensor. Our process combines cues from two deep-learning processes - semantic segmentation and depth map estimation, to automatically decide plausible robot movement paths while avoiding hindrance posed by objects in the scene. We make assumptions of the use of a low-end RGB USB camera, pre-set camera view direction (angle) and field of view, incremental movement of the robot in the view field, and iterative analysis of the scene, all catering to any open-ended (target-free) surveillance/patrolling applications. Inverse perspective geometry has been used to map the optimal direction estimated in the view field, to that on the floor of the scene for navigation. Results of evaluation using a dataset of videos of scenes captured from indoor (office, labs, meeting/class-rooms, corridors, lounge) environments, reveal the success of the proposed approach.
{"title":"Navigational Aid for Open-Ended Surveillance, by Fusing Estimated Depth and Scene Segmentation Maps, Using RGB Images of Indoor Scenes","authors":"Binoy Saha, Neha Shah, Sukhendu Das","doi":"10.1109/SPCOM55316.2022.9840820","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840820","url":null,"abstract":"Open-ended surveillance task for a robot in an unspecified environment using only an RGB camera, has not been addressed at length in literature. This is unlike the popular scenario of path planning where both the target and environments are often known. We focus on the task of a robot which needs to estimate a realistic depiction of the surrounding 3D environment, including the location of obstacles and free space to navigate in the scene within the view field. In this paper, we propose an unsupervised algorithm to iteratively compute an optimal direction for maximal unhindered movement in the scene. This task is challenging when presented with only a single RGB view of the scene, without the use of any online depth sensor. Our process combines cues from two deep-learning processes - semantic segmentation and depth map estimation, to automatically decide plausible robot movement paths while avoiding hindrance posed by objects in the scene. We make assumptions of the use of a low-end RGB USB camera, pre-set camera view direction (angle) and field of view, incremental movement of the robot in the view field, and iterative analysis of the scene, all catering to any open-ended (target-free) surveillance/patrolling applications. Inverse perspective geometry has been used to map the optimal direction estimated in the view field, to that on the floor of the scene for navigation. Results of evaluation using a dataset of videos of scenes captured from indoor (office, labs, meeting/class-rooms, corridors, lounge) environments, reveal the success of the proposed approach.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115132479","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840775
Pankaj Warule, S. Mishra, S. Deb
This work uses vowel-like region segments of speech to classify cold and non-cold speech signals. As various articulators are affected by the common cold, speech produced during the common cold gets affected. These changes in a speech during common cold can be used to classify cold and non-cold speech. Vowel-like region (VLR) in speech includes vowels, semi-vowels, and diphthongs phonemes. Vowel-like regions are the dominant part of the speech signal. Hence, we have considered only vowel-like regions for cold and non-cold speech classification. The VLRs are identified by locating the VLR onset point (VLROP) and end point (VLREP). The Hilbert envelope and zero frequency filtering methods are used for detection of VLROPs and VLREPs. Mel frequency cepstral coefficients (MFCCs) feature are extracted from VLRs, and the performance of these features are evaluated using a deep neural network. Features extracted from VLRs give comparable results to features extracted from complete active speech (CAS) signal. Compared to the CAS technique, the number of frames that needs to be processed utilizing VLRs is significantly less.
{"title":"Classification of Cold and Non-Cold Speech Using Vowel-Like Region Segments","authors":"Pankaj Warule, S. Mishra, S. Deb","doi":"10.1109/SPCOM55316.2022.9840775","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840775","url":null,"abstract":"This work uses vowel-like region segments of speech to classify cold and non-cold speech signals. As various articulators are affected by the common cold, speech produced during the common cold gets affected. These changes in a speech during common cold can be used to classify cold and non-cold speech. Vowel-like region (VLR) in speech includes vowels, semi-vowels, and diphthongs phonemes. Vowel-like regions are the dominant part of the speech signal. Hence, we have considered only vowel-like regions for cold and non-cold speech classification. The VLRs are identified by locating the VLR onset point (VLROP) and end point (VLREP). The Hilbert envelope and zero frequency filtering methods are used for detection of VLROPs and VLREPs. Mel frequency cepstral coefficients (MFCCs) feature are extracted from VLRs, and the performance of these features are evaluated using a deep neural network. Features extracted from VLRs give comparable results to features extracted from complete active speech (CAS) signal. Compared to the CAS technique, the number of frames that needs to be processed utilizing VLRs is significantly less.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"159 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128902608","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840822
Pratik Kar, V. Sukumaran, S. Sumitra
We consider the problem of designing posterior sampling based sequential optimization policies for maximizing a blackbox function subject to safety constraints. Posterior sampling algorithms, which are easier to implement, have met with empirical success for blackbox maximization problems without safety constraints. We consider whether posterior sampling algorithms which satisfy safety constraints have good performance with respect to achieving the global maxima while minimizing the number of safety constraint violations. We propose a safe Gaussian process Thompson Sampling algorithm for safe maximization of a blackbox function. The algorithm uses a sample estimate of safe set in order to meet safety constraints and uses a mutual information based acquisition function in order to improve the estimate of the safe set. We evaluate the performance of the proposed policy with respect to prior work using simulations. We observe that the proposed policy achieves similar behaviour compared to prior work for safety violations while achieving the global maximum.
{"title":"On safe sequential optimization using posterior sampling","authors":"Pratik Kar, V. Sukumaran, S. Sumitra","doi":"10.1109/SPCOM55316.2022.9840822","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840822","url":null,"abstract":"We consider the problem of designing posterior sampling based sequential optimization policies for maximizing a blackbox function subject to safety constraints. Posterior sampling algorithms, which are easier to implement, have met with empirical success for blackbox maximization problems without safety constraints. We consider whether posterior sampling algorithms which satisfy safety constraints have good performance with respect to achieving the global maxima while minimizing the number of safety constraint violations. We propose a safe Gaussian process Thompson Sampling algorithm for safe maximization of a blackbox function. The algorithm uses a sample estimate of safe set in order to meet safety constraints and uses a mutual information based acquisition function in order to improve the estimate of the safe set. We evaluate the performance of the proposed policy with respect to prior work using simulations. We observe that the proposed policy achieves similar behaviour compared to prior work for safety violations while achieving the global maximum.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128806593","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840835
A. Chandrasekar, Amey Shimpi, D. Naik
Visual Question Answering (VQA) is a problem at the intersection of Computer Vision (CV) and Natural Language Processing (NLP) which involves using natural language to respond to questions based on the context of images. The majority of existing methods focus on monolingual models, particularly those that only support English. This paper proposes a novel dataset alongside monolingual and multilingual models using the baseline and attention-based architectures with support for three Indic languages: Hindi, Kannada, and Tamil. We compare the performance of traditional (CNN + LSTM) approaches with current attention-based methods using the VQA v2 dataset. The proposed work achieves 51.618% accuracy for Hindi, 57.177% for Kannada, and 56.061% for the Tamil model.
{"title":"Indic Visual Question Answering","authors":"A. Chandrasekar, Amey Shimpi, D. Naik","doi":"10.1109/SPCOM55316.2022.9840835","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840835","url":null,"abstract":"Visual Question Answering (VQA) is a problem at the intersection of Computer Vision (CV) and Natural Language Processing (NLP) which involves using natural language to respond to questions based on the context of images. The majority of existing methods focus on monolingual models, particularly those that only support English. This paper proposes a novel dataset alongside monolingual and multilingual models using the baseline and attention-based architectures with support for three Indic languages: Hindi, Kannada, and Tamil. We compare the performance of traditional (CNN + LSTM) approaches with current attention-based methods using the VQA v2 dataset. The proposed work achieves 51.618% accuracy for Hindi, 57.177% for Kannada, and 56.061% for the Tamil model.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123184791","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840843
Shraddha Tripathi, O. Pandey, R. Hegde
One of the critical challenges of unmanned aerial vehicle (UAV)-assisted edge networks is to prolong the network lifetime with improved quality-of-service (QoS) at the sensor nodes (SNs). UAVs are typically resource-constrained with limited energy and communication capacity. Collaborative beamforming (CB) in a single antenna-mounted UAV network is an effective way of addressing the aforementioned challenges. Particularly, in this work, the optimal number of UAVs and their locations are obtained for CB, resulting in maximized network lifetime and improved signal-to-noise ratio (SNR). To meet the objective of maximizing network lifetime and SNR, antenna array gain is maximized by computing optimum spacing within the array. In this context, an optimization problem is formulated to jointly optimize the network lifetime and SNR. The proposed method minimizes the UAVs positioning error over time while forming the array. The method considers UAVs mobility parameters, the optimal number of collaborating UAVs, and UAVs power consumption as constraints. Finally, extensive simulation results show the effectiveness of the proposed method in terms of better network coverage, the minimum number of UAVs required, and maximum SNR compared to existing schemes.
{"title":"Joint Optimization of Network Lifetime and SNR in UAV-Assisted Edge Networks","authors":"Shraddha Tripathi, O. Pandey, R. Hegde","doi":"10.1109/SPCOM55316.2022.9840843","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840843","url":null,"abstract":"One of the critical challenges of unmanned aerial vehicle (UAV)-assisted edge networks is to prolong the network lifetime with improved quality-of-service (QoS) at the sensor nodes (SNs). UAVs are typically resource-constrained with limited energy and communication capacity. Collaborative beamforming (CB) in a single antenna-mounted UAV network is an effective way of addressing the aforementioned challenges. Particularly, in this work, the optimal number of UAVs and their locations are obtained for CB, resulting in maximized network lifetime and improved signal-to-noise ratio (SNR). To meet the objective of maximizing network lifetime and SNR, antenna array gain is maximized by computing optimum spacing within the array. In this context, an optimization problem is formulated to jointly optimize the network lifetime and SNR. The proposed method minimizes the UAVs positioning error over time while forming the array. The method considers UAVs mobility parameters, the optimal number of collaborating UAVs, and UAVs power consumption as constraints. Finally, extensive simulation results show the effectiveness of the proposed method in terms of better network coverage, the minimum number of UAVs required, and maximum SNR compared to existing schemes.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126522113","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}
We propose a variant of the voter model which captures salient features of opinion dynamics in a network consisting of individuals and bots. Key features of our model are that the influence of bots on the opinion evolution can be different from the influence of individuals in the network and that the opinion of bots does not evolve over time irrespective of the opinion of the rest of the network. We use the proposed model and tools from the theory of stochastic approximation and martingales to develop a method to accurately characterize the number of bots needed to achieve specific opinion-shaping targets as a function of various system parameters in a fully connected network.
{"title":"Opinion Dynamics in the Presence of Bots","authors":"Ashish Shukla, Neeraja Sahasrabudhe, Sharayu Moharir","doi":"10.1109/SPCOM55316.2022.9840793","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840793","url":null,"abstract":"We propose a variant of the voter model which captures salient features of opinion dynamics in a network consisting of individuals and bots. Key features of our model are that the influence of bots on the opinion evolution can be different from the influence of individuals in the network and that the opinion of bots does not evolve over time irrespective of the opinion of the rest of the network. We use the proposed model and tools from the theory of stochastic approximation and martingales to develop a method to accurately characterize the number of bots needed to achieve specific opinion-shaping targets as a function of various system parameters in a fully connected network.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126535690","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840768
G. Shaw, Shyam Sridharan, A. Prabhakar
We present a procedure to optimize the quantum bit error rate in coherent one-way quantum key distribution (COW-QKD) system. We built the testbed for COW-QKD, which supported a clock rate of 1 GHz. Temporal filtering was realized by varying gate delays applied to the single-photon detector and optimal selection of time window to receive logic bits. We observed that with adjustable temporal-filtering, we can improve on the quantum bit error rate, reducing it to 11.6%. The sifted key rate drops to less than 1 kbps, when we extend this QKD protocol over a distance of 150 km.
{"title":"Optimal temporal filtering for COW-QKD","authors":"G. Shaw, Shyam Sridharan, A. Prabhakar","doi":"10.1109/SPCOM55316.2022.9840768","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840768","url":null,"abstract":"We present a procedure to optimize the quantum bit error rate in coherent one-way quantum key distribution (COW-QKD) system. We built the testbed for COW-QKD, which supported a clock rate of 1 GHz. Temporal filtering was realized by varying gate delays applied to the single-photon detector and optimal selection of time window to receive logic bits. We observed that with adjustable temporal-filtering, we can improve on the quantum bit error rate, reducing it to 11.6%. The sifted key rate drops to less than 1 kbps, when we extend this QKD protocol over a distance of 150 km.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124995349","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840786
S. Kirubakaran, M. D. Selvaraj
Free Space Optics (FSO) is a promising choice for dealing with high data-rate applications, and it improves reliability by using RF communication as a backup link. In a practical scenario, the RF link is affected by co-channel interference. We investigate the performance of an interference-limited hybrid FSO/RF system. We consider a transmitter $(T_{X})$ and a receiver $(R_{X})$ with threshold-based switching selection at the RX. Atmospheric turbulence, path loss, and pointing errors influence the FSO link, which is modelled using the gamma-gamma distribution, whereas the RF link is modelled using the $kappa-mu$ distribution. We have derived the end-to-end symbol error rate and the outage probability for the interference limited system. Results show that interference increases the error rate and degrades the system performance. The impact of interference is high in the lower SNR’s and it is controlled by increasing the Signal to Interference Ratio (SIR) of RF link. To verify the results, Monte carlo simulations are used.
{"title":"Performance Analysis of Interference Limited Hybrid FSO/RF Systems","authors":"S. Kirubakaran, M. D. Selvaraj","doi":"10.1109/SPCOM55316.2022.9840786","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840786","url":null,"abstract":"Free Space Optics (FSO) is a promising choice for dealing with high data-rate applications, and it improves reliability by using RF communication as a backup link. In a practical scenario, the RF link is affected by co-channel interference. We investigate the performance of an interference-limited hybrid FSO/RF system. We consider a transmitter $(T_{X})$ and a receiver $(R_{X})$ with threshold-based switching selection at the RX. Atmospheric turbulence, path loss, and pointing errors influence the FSO link, which is modelled using the gamma-gamma distribution, whereas the RF link is modelled using the $kappa-mu$ distribution. We have derived the end-to-end symbol error rate and the outage probability for the interference limited system. Results show that interference increases the error rate and degrades the system performance. The impact of interference is high in the lower SNR’s and it is controlled by increasing the Signal to Interference Ratio (SIR) of RF link. To verify the results, Monte carlo simulations are used.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"503 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122756190","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：