Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179587
R. Gowriprasad., K. Murty
Onset detection is an important first step in music analysis. We propose a pre-processing scheme for improved onset detection of complex strokes of Indian percussion instruments with resonance characteristics. In this work, we explore the onset detection of Tabla (Indian percussion instrument) strokes. The resonance characteristics of tabla strokes poses challenges to onset detection. In such cases, the energy-based and spectral flux-based onset detectors are often inaccurate on the raw signal. We propose an onset detection algorithm addressing these challenges using Linear Prediction (LP) analysis and Hilbert envelope (HE) in tandem. Tabla signal is modeled using LP, and its residual highlights the onset time instances very well. Unipolar nature of HE on top of LP residual further enhances the onset instances. Onset detection is performed using energy based, spectral flux based and the state of the art Machine Learning based onset detectors on the Hilbert envelope of LP residual (HELP). Experiments were performed on tabla solo played at various tempi and the results show that the HELP based approach provides 12% relative improvement in F-measures compared to the performance on raw tabla signal.
{"title":"Onset detection of Tabla Strokes using LP Analysis","authors":"R. Gowriprasad., K. Murty","doi":"10.1109/SPCOM50965.2020.9179587","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179587","url":null,"abstract":"Onset detection is an important first step in music analysis. We propose a pre-processing scheme for improved onset detection of complex strokes of Indian percussion instruments with resonance characteristics. In this work, we explore the onset detection of Tabla (Indian percussion instrument) strokes. The resonance characteristics of tabla strokes poses challenges to onset detection. In such cases, the energy-based and spectral flux-based onset detectors are often inaccurate on the raw signal. We propose an onset detection algorithm addressing these challenges using Linear Prediction (LP) analysis and Hilbert envelope (HE) in tandem. Tabla signal is modeled using LP, and its residual highlights the onset time instances very well. Unipolar nature of HE on top of LP residual further enhances the onset instances. Onset detection is performed using energy based, spectral flux based and the state of the art Machine Learning based onset detectors on the Hilbert envelope of LP residual (HELP). Experiments were performed on tabla solo played at various tempi and the results show that the HELP based approach provides 12% relative improvement in F-measures compared to the performance on raw tabla signal.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115690940","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 : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179534
Rima Deka, Sanya Anees
In this paper the performance of decode-and-forward (DF) based mixed visible light communication-free space optical communication-visible light communication (VLC-FSO-VLC) co-operative system is presented. In the proposed system, the FSO link is used for providing the outdoor connectivity and VLC environment is used for indoor connectivity. The FSO link is characterized by the impact of Double Generalized Gamma (DGG) distributed atmospheric turbulence and Rayleigh distributed pointing errors and VLC environment follows the Lambertian radiation pattern of the light-emitting-diode (LED). For this system, novel mathematical expressions are derived for probability density function and cumulative distribution function of the system’s signal-to-noise-ratio (SNR). Using these SNR statistics, the closed-form analytical expressions of average bit-error rate (BER) for binary modulation techniques considering the possibility of perfect decoding by the DF relays are obtained. The results show that the BER of the considered VLC-FSOVLC system gives a better performance while using coherent binary phase shift keying (CBPSK) modulation technique as compared to other modulation techniques.
{"title":"Performance Analysis of DF Based mixed VLC-FSO-VLC System","authors":"Rima Deka, Sanya Anees","doi":"10.1109/SPCOM50965.2020.9179534","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179534","url":null,"abstract":"In this paper the performance of decode-and-forward (DF) based mixed visible light communication-free space optical communication-visible light communication (VLC-FSO-VLC) co-operative system is presented. In the proposed system, the FSO link is used for providing the outdoor connectivity and VLC environment is used for indoor connectivity. The FSO link is characterized by the impact of Double Generalized Gamma (DGG) distributed atmospheric turbulence and Rayleigh distributed pointing errors and VLC environment follows the Lambertian radiation pattern of the light-emitting-diode (LED). For this system, novel mathematical expressions are derived for probability density function and cumulative distribution function of the system’s signal-to-noise-ratio (SNR). Using these SNR statistics, the closed-form analytical expressions of average bit-error rate (BER) for binary modulation techniques considering the possibility of perfect decoding by the DF relays are obtained. The results show that the BER of the considered VLC-FSOVLC system gives a better performance while using coherent binary phase shift keying (CBPSK) modulation technique as compared to other modulation techniques.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126143583","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 : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179530
A. Shukla, B. Manoj, M. Bhatnagar
In this paper, we propose a virtual full-duplex (VFD) multi-hop cooperative relaying network using buffer-aided halfduplex (HD) relays. Firstly, for a given multi-hop network, the links are partitioned into three groups and then information is transmitted over a single time slot using two selected best links, which enhances the network coverage and reliability of the system. The Markov chain approach is used to analyze the state transition matrix which models the evolution of buffer states. An analytical expression of steady state probability is obtained, using which the outage probability of the system is evaluated. Numerical results validate our analytical findings and it shows that the proposed relaying scheme offers a better outage performance as compared to that of the conventional buffer-aided max-link relay selection scheme in a multi-hop communication system.
{"title":"Virtual Full-Duplex Relaying in a Buffer-Aided Multi-Hop Cooperative Network","authors":"A. Shukla, B. Manoj, M. Bhatnagar","doi":"10.1109/SPCOM50965.2020.9179530","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179530","url":null,"abstract":"In this paper, we propose a virtual full-duplex (VFD) multi-hop cooperative relaying network using buffer-aided halfduplex (HD) relays. Firstly, for a given multi-hop network, the links are partitioned into three groups and then information is transmitted over a single time slot using two selected best links, which enhances the network coverage and reliability of the system. The Markov chain approach is used to analyze the state transition matrix which models the evolution of buffer states. An analytical expression of steady state probability is obtained, using which the outage probability of the system is evaluated. Numerical results validate our analytical findings and it shows that the proposed relaying scheme offers a better outage performance as compared to that of the conventional buffer-aided max-link relay selection scheme in a multi-hop communication system.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123330735","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 : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179542
Bejjipuram Sombabu, Sharayu Moharir
We consider the task of scheduling updates from multiple sources to a central monitoring station via a shared communication channel. Each source harvests energy from nature to measure a time-varying quantity and report these measurements to the monitoring station. Prior work in this area focuses on the setting where energy arrivals are assumed to be independent across time. Motivated by the time-correlation in energy generated by many renewable energy sources, we use a Markov process to model the energy arrivals. The goal is to minimize the time average of the weighted sum of the ages-of-information of the sources. We use Whittle’s relaxation and propose a modification of the Whittle Index to design a scheduling policy. We show that our policy outperforms other natural policies via simulations.
{"title":"Age-of-Information Aware Scheduling under Markovian Energy Arrivals","authors":"Bejjipuram Sombabu, Sharayu Moharir","doi":"10.1109/SPCOM50965.2020.9179542","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179542","url":null,"abstract":"We consider the task of scheduling updates from multiple sources to a central monitoring station via a shared communication channel. Each source harvests energy from nature to measure a time-varying quantity and report these measurements to the monitoring station. Prior work in this area focuses on the setting where energy arrivals are assumed to be independent across time. Motivated by the time-correlation in energy generated by many renewable energy sources, we use a Markov process to model the energy arrivals. The goal is to minimize the time average of the weighted sum of the ages-of-information of the sources. We use Whittle’s relaxation and propose a modification of the Whittle Index to design a scheduling policy. We show that our policy outperforms other natural policies via simulations.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122594566","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 : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179491
Mrinmoy Bhattacharjee, S. Prasanna, P. Guha
Applications that perform enhancement of speech containing background music require a critical preprocessing step that can efficiently detect such segments. This work proposes such a preprocessing method to detect speech with background music that is mixed at different SNR levels. A bag-of-words approach is proposed in this work. Representative dictionaries from speech and music data are first learned. The signals are processed as spectrograms of 1s intervals. Rows of these spectrograms are used to learn separate speech and music dictionaries. This work proposes a weighting scheme to reduce confusion by suppressing codewords of one class that have similarities to the other class. The proposed feature is a weighted histogram of 1s audio intervals obtained from the learned dictionaries. The classification is performed using a deep neural network classifier. The proposed approach is validated against a baseline and benchmarked over two publicly available datasets. The proposed feature shows promising results, both individually and in combination with the baseline.
{"title":"Classification of Speech vs. Speech with Background Music","authors":"Mrinmoy Bhattacharjee, S. Prasanna, P. Guha","doi":"10.1109/SPCOM50965.2020.9179491","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179491","url":null,"abstract":"Applications that perform enhancement of speech containing background music require a critical preprocessing step that can efficiently detect such segments. This work proposes such a preprocessing method to detect speech with background music that is mixed at different SNR levels. A bag-of-words approach is proposed in this work. Representative dictionaries from speech and music data are first learned. The signals are processed as spectrograms of 1s intervals. Rows of these spectrograms are used to learn separate speech and music dictionaries. This work proposes a weighting scheme to reduce confusion by suppressing codewords of one class that have similarities to the other class. The proposed feature is a weighted histogram of 1s audio intervals obtained from the learned dictionaries. The classification is performed using a deep neural network classifier. The proposed approach is validated against a baseline and benchmarked over two publicly available datasets. The proposed feature shows promising results, both individually and in combination with the baseline.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127007034","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 : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179546
P. Vishnu, C. S. Ramalingam
In this paper we propose a method for sinusoidal frequency estimation that improves upon our previously proposed LSF-based algorithm that used at most 5p candidate points, where p is the number of sinusoids present. In this paper we propose the following improvements: (i) reduced the number of candidate frequencies to at most 2p points, (ii) reduced the method’s threshold to equal that of ML, and (iii) reduced the computational burden by switching to methods like ESPRIT when the SNR is above threshold. Since neither the SNR nor the threshold is known, we estimate them from the data. The proposed reduction-in-threshold step can be applied to EPUMA (proposed Qian et al.), with which we compare our results. For the well-known two-sinusoid example the proposed method has the same threshold as that of ML; ML performance is also achieved when tested on a new, three-sinusoid example.
{"title":"An Improved LSF-based Algorithm for Sinusoidal Frequency Estimation that Achieves Maximum Likelihood Performance","authors":"P. Vishnu, C. S. Ramalingam","doi":"10.1109/SPCOM50965.2020.9179546","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179546","url":null,"abstract":"In this paper we propose a method for sinusoidal frequency estimation that improves upon our previously proposed LSF-based algorithm that used at most 5p candidate points, where p is the number of sinusoids present. In this paper we propose the following improvements: (i) reduced the number of candidate frequencies to at most 2p points, (ii) reduced the method’s threshold to equal that of ML, and (iii) reduced the computational burden by switching to methods like ESPRIT when the SNR is above threshold. Since neither the SNR nor the threshold is known, we estimate them from the data. The proposed reduction-in-threshold step can be applied to EPUMA (proposed Qian et al.), with which we compare our results. For the well-known two-sinusoid example the proposed method has the same threshold as that of ML; ML performance is also achieved when tested on a new, three-sinusoid example.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131442574","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 : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179588
P. Naik, M. Gaonkar, Veena Thenkanidiyoor, A. D. Dileep
Query-by-Example based spoken term detection (QbE-STD) to audio search involves matching an audio query with the reference utterances to find the relevant utterances. QbE-STD involves computing a matching matrix between a query and reference utterance using a suitable metric. In this work we propose to use kernel based matching by considering histogram intersection kernel (HIK) as a matching metric. A CNN-based approach to QbE-STD involves first converting a matching matrix to a corresponding size-normalized image and classifying the image as relevant or not [6]. In this work, we propose to train a CNN-based classifier using size-normalized images instead of splitting them into subimages as in [6]. Training approach proposed in this work is expected to be more effective since there is less chance of a CNN based classifier getting confused. The effectiveness of the proposed kernel based matching and novel training approach is studied using TIMIT dataset.
{"title":"Kernel based Matching and a Novel training approach for CNN-based QbE-STD","authors":"P. Naik, M. Gaonkar, Veena Thenkanidiyoor, A. D. Dileep","doi":"10.1109/SPCOM50965.2020.9179588","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179588","url":null,"abstract":"Query-by-Example based spoken term detection (QbE-STD) to audio search involves matching an audio query with the reference utterances to find the relevant utterances. QbE-STD involves computing a matching matrix between a query and reference utterance using a suitable metric. In this work we propose to use kernel based matching by considering histogram intersection kernel (HIK) as a matching metric. A CNN-based approach to QbE-STD involves first converting a matching matrix to a corresponding size-normalized image and classifying the image as relevant or not [6]. In this work, we propose to train a CNN-based classifier using size-normalized images instead of splitting them into subimages as in [6]. Training approach proposed in this work is expected to be more effective since there is less chance of a CNN based classifier getting confused. The effectiveness of the proposed kernel based matching and novel training approach is studied using TIMIT dataset.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115836445","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 : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179579
A. Koul, G. V. Anand, Sanjeev Gurugopinath, K. Nathwani
Several superresolution source localization algorithms based on the sparse signal reconstruction framework have been developed in recent years. These methods also offer other advantages such as immunity to noise coherence and robustness to reduction in the number of snapshots. The application of these methods is mostly limited to the problem of one dimensional (1-D) direction-of-arrival estimation. In this paper, we have developed 2-D and 3-D versions of two sparse signal reconstruction methods, viz. $ell_{1}$-SVD and re-weighted $ell_{1}$-SVD, and applied them to the problem of 3-D localization of underwater acoustic sources. A vertical linear array is used for estimation of range and depth and a horizontal cross-shaped array is used for bearing estimation. It is shown that the $ell_{1}$-SVD and re-weighted $ell_{1}$-SVD processors outperform the widely used MUSIC and Bartlett processors.
{"title":"Three-Dimensional Underwater Acoustic Source Localization by Sparse Signal Reconstruction Techniques","authors":"A. Koul, G. V. Anand, Sanjeev Gurugopinath, K. Nathwani","doi":"10.1109/SPCOM50965.2020.9179579","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179579","url":null,"abstract":"Several superresolution source localization algorithms based on the sparse signal reconstruction framework have been developed in recent years. These methods also offer other advantages such as immunity to noise coherence and robustness to reduction in the number of snapshots. The application of these methods is mostly limited to the problem of one dimensional (1-D) direction-of-arrival estimation. In this paper, we have developed 2-D and 3-D versions of two sparse signal reconstruction methods, viz. $ell_{1}$-SVD and re-weighted $ell_{1}$-SVD, and applied them to the problem of 3-D localization of underwater acoustic sources. A vertical linear array is used for estimation of range and depth and a horizontal cross-shaped array is used for bearing estimation. It is shown that the $ell_{1}$-SVD and re-weighted $ell_{1}$-SVD processors outperform the widely used MUSIC and Bartlett processors.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"15 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116400306","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 : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179611
P. Pokala, C. Seelamantula
We propose an improvement of the projected fast iterative soft-thresholding algorithm (pFISTA) and smoothing FISTA (SFISTA) to achieve faster convergence and improved reconstruction accuracy. The pFISTA addresses the problem of compressed sensing magnetic resonance imaging (CS-MRI) reconstruction under tight frames and considers standard $ell_{1}$ norm minimization. The $ell_{1} -$norm weighs each component in a sparse vector equally. However, this is restrictive. We employ the weighted $ell_{1} -$regularizer, defined over a complex-domain as the sparsity-promoting function in CS-MRI reconstruction. The weighted $ell_{1} -$regularizer assigns different weights to the components in a sparse vector to improve upon reconstruction accuracy. The optimization objective in CS-MRI is a real-valued function defined over a complex-domain and is therefore not holomorphic. We derive an algorithm, namely, projected weighted iterative soft-thresholding algorithm (pWISTA) based on Wirtinger calculus to solve the weighted $ell_{1} -$regularized CS-MRI reconstruction under tight frames. We show that the proximal operator for the weighted $ell_{1}$ regularizer over a complex-domain is the soft-thresholding operator, but with a different threshold for each component. We also incorporate Nesterov’s momentum into the pWISTA update to obtain the projected weighted fast iterative soft-thresholding algorithm (pWFISTA), which result in accelerated optimization as shown by the experimental results.
{"title":"Accelerated Weighted ℓ1-Minimization for MRI Reconstruction Under Tight Frames in Complex Domain","authors":"P. Pokala, C. Seelamantula","doi":"10.1109/SPCOM50965.2020.9179611","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179611","url":null,"abstract":"We propose an improvement of the projected fast iterative soft-thresholding algorithm (pFISTA) and smoothing FISTA (SFISTA) to achieve faster convergence and improved reconstruction accuracy. The pFISTA addresses the problem of compressed sensing magnetic resonance imaging (CS-MRI) reconstruction under tight frames and considers standard $ell_{1}$ norm minimization. The $ell_{1} -$norm weighs each component in a sparse vector equally. However, this is restrictive. We employ the weighted $ell_{1} -$regularizer, defined over a complex-domain as the sparsity-promoting function in CS-MRI reconstruction. The weighted $ell_{1} -$regularizer assigns different weights to the components in a sparse vector to improve upon reconstruction accuracy. The optimization objective in CS-MRI is a real-valued function defined over a complex-domain and is therefore not holomorphic. We derive an algorithm, namely, projected weighted iterative soft-thresholding algorithm (pWISTA) based on Wirtinger calculus to solve the weighted $ell_{1} -$regularized CS-MRI reconstruction under tight frames. We show that the proximal operator for the weighted $ell_{1}$ regularizer over a complex-domain is the soft-thresholding operator, but with a different threshold for each component. We also incorporate Nesterov’s momentum into the pWISTA update to obtain the projected weighted fast iterative soft-thresholding algorithm (pWFISTA), which result in accelerated optimization as shown by the experimental results.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"148 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120849310","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 : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179525
Lalhruaizela Chhangte, E. Viterbo, D. Manjunath, N. Karamchandani
HTTP based video streaming has become the de facto standard for video content delivery across different video streaming services. However, video content delivery continues to be challenged at the wireless edge by inadequate and highly variable bandwidth. In this paper, we describe WiCode, a platform that improves HTTP based video content delivery at the WiFi edge. WiCode uses coded delivery at the WiFi AP to reduce data transmissions in order to improve the perceived performance of video streaming at the users. WiCode performs index coding on video segments to reduce the number of bits transmitted. Further, it also performs index coding on UDP packets that are retransmitted to reduce the number of bits transmitted. This paper describes the design and implementation of WiCode, and the practical gains achievable due to employing coded delivery in a real system taking into account the overheads introduced by WiCode. The WiCode module at the client side is a browser plugin that does not require any client side device configuration changes. We also show the effect of variable and fixed length segment size on the perceived performance of WiCode.
{"title":"Index Coding at the WiFi Edge: An Implementation Study for Video Delivery","authors":"Lalhruaizela Chhangte, E. Viterbo, D. Manjunath, N. Karamchandani","doi":"10.1109/SPCOM50965.2020.9179525","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179525","url":null,"abstract":"HTTP based video streaming has become the de facto standard for video content delivery across different video streaming services. However, video content delivery continues to be challenged at the wireless edge by inadequate and highly variable bandwidth. In this paper, we describe WiCode, a platform that improves HTTP based video content delivery at the WiFi edge. WiCode uses coded delivery at the WiFi AP to reduce data transmissions in order to improve the perceived performance of video streaming at the users. WiCode performs index coding on video segments to reduce the number of bits transmitted. Further, it also performs index coding on UDP packets that are retransmitted to reduce the number of bits transmitted. This paper describes the design and implementation of WiCode, and the practical gains achievable due to employing coded delivery in a real system taking into account the overheads introduced by WiCode. The WiCode module at the client side is a browser plugin that does not require any client side device configuration changes. We also show the effect of variable and fixed length segment size on the perceived performance of WiCode.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124849344","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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