Muralishankar R

Scopus Publications

Optimal Joint Scheduling and Congestion Window Estimation for Congestion Avoidance in Mobile Opportunistic Networks
Gourish Goudar, R. Muralishankar
Journal of Network and Systems Management, 2025
Full-Duplex Non-Orthogonal Multiple Access in Relay-Assisted Power Line Communication
Roopesh Ramesh, Sanjeev Gurugopinath, R. Muralishankar
2025 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2025 Proceedings, 2025
The performance of non-orthogonal multiple access (NOMA) in a full-duplex (FD) relay-assisted power line communication (PLC) network is analyzed in this paper. A cooperative PLC system involving a source, relay, and destination node where the relay node is in FD mode for enhancing the spectral efficiency is taken into account. The performance of the system is measured in terms of outage probability using the log-normal fading channel model. We also elaborate on the power distribution optimization between the relay and destination nodes in the FD-NOMA case for minimization of outage probability. Outcomes show that the FD-NOMA scheme enjoys superior performance gains over its half-duplex counterpart for outage probability reduction. The paper also introduces the significance of the relay location and self-interference cancellation efficiency in overall system performance.
Multi-User Non-Orthogonal Multiple Access in Power Line Communications Under Generalized Gaussian Noise
Roopesh Ramesh, Sanjeev Gurugopinath, R. Muralishankar
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2025
We present an analysis on multi-user pairwise error probability (PEP) performance of non-orthogonal multiple access (NOMA)-aided power line communication (PLC) systems, under heavy-tailed noise. In contrast to the typical assumption in the literature, we consider the successive interference cancellation procedure in NOMA to be imperfect. We model the heavy-tailed noise as generalized Gaussian (GG) distribution with a heavy-tail parameter, and discuss the impact of this parameter on the PEP performance. We validate our analysis using Monte Carlo simulations, and disucss the impact of the lognormal fading parameters in PEP. Further, we numerically show that the diversity order on PEP is equal to the user order for larger values of the heavy-tail parameter, which is similar to that observed in an additive white Gaussian noise scenario.
A Correlation Profile-Based Adaptive Weighing in Mel-DCT Filter Banks for Voice Activity Detection
Narendra K. C, R. Muralishankar, Sanjeev Gurugopinath, Prasanta Kumar Ghosh
Proceedings of the National Conference on Communications Ncc, 2025
In this study, we propose an adaptive scheme to dynamically select filters using a novel correlation profile across the modified Mel-DCT (MMD) filter bank which is used to compute the weighted average of the frequency-domain long-term differential entropy (FLDE) and MMD-FLDE features for voice activity detection (VAD) applications. We conduct extensive experiments using the SWITCH-BOARD corpus and noise samples from the NOISEX-92. leveraging this combination of correlation profile-based Mel-DCT filters and long-term speech characteristics. Our results show that the proposed technique outperforms the FLDE and MMD-FLDE methods, in terms of detection accuracy, speech hit rate and noise hit rate, particularly for stationary and heavy noise cases with an improvement of over five percent in the accuracy for machine gun noise in particular. Moreover, we compare the performance of the proposed technique with the robust VAD (rVAD) algorithm, and the results indicate that the proposed method outperforms the rVAD algorithm in terms of speech hit rate.
Vasicek and Van Es entropy-based spectrum sensing for cognitive radios
Sutapa Sarkar, R. Muralishankar, Sanjeev Gurugopinath
Iet Networks, 2024
Accurate detection of spectrum holes is a useful requirement for cognitive radios that improves the efficiency of spectrum usage. The authors propose three novel, simple, and entropy‐based detectors for spectrum sensing in cognitive radio. The authors evaluate the probability of detection of these three detectors: Vasicek's entropy detector, truncated Vasicek's entropy detector, and Van Es' entropy detector, over a predefined probability of false‐alarm. In particular, the authors provide the approximate and asymptotic test statistics for these detectors in the presence and absence of Nakagami‐m fading, noise variance uncertainty, and optimised detection threshold. Furthermore, the authors provide a detailed comparison study among all the detectors via Monte Carlo simulations and justify authors results through real‐world data. The authors’ experimental results establish a superior performance of truncated Vasicek's entropy detector over Vasicek's entropy detector, energy detector, differential entropy detector and Van Es' entropy detector in practically viable scenarios.
Norm-based spectrum sensing for cognitive radios under generalised Gaussian noise
Arati Halaki, Sutapa Sarkar, Sanjeev Gurugopinath, R. Muralishankar
Iet Networks, 2023
Cognitive radio (CR) systems are configured to dynamically assess the spectrum utilisation and contribute towards an improved spectrum efficiency. Hence, accurate detection of the incumbent signal in a given channel, popularly known as spectrum sensing (SS), is essential for CR. Here, in the domain of SS, the authors introduce a new goodness‐of‐fit test (GoFT) founded on p‐norm of the observations at the receiver node. To capture the heavy‐tailed nature of noise distribution in practical communication channels, the authors utilise generalised Gaussian distribution (GGD) as a noise model. A novel p‐norm detector (PND) and a geometric power detector (GPD) is proposed and corresponding probability density function (PDF) under GGD is derived. Via Monte Carlo simulations, the authors show a match of the derived PDFs with the simulation results. Using Neyman‐Pearson framework the performances of PND and GPD are compared with an existing differential entropy detector (DED), the well‐known energy detector (ED) and joint correlation and energy detector (CED) under GGD noise model. Evaluation of proposed PND and GPD utilising Monte Carlo simulations indicate a superior performance. Further, the experiments employing real‐world data establish superiority of the proposed detectors as compared to existing techniques. The authors derive and implement an optimised threshold for PND, providing further improvement in performance.
Outage Analysis of Single-Stage Relay NOMA Over Power Line Communication Under Impulsive Noise
Roopesh Ramesh, R. Muralishankar, Sanjeev Gurugopinath
2023 9th International Conference on Signal Processing and Communication ICSC 2023, 2023
In this paper, we study and present the performance based on the outage probability of a single-stage, relay-aided, cooperative non-orthogonal multiple access over power line communication under a Bernoulli-Gaussian impulsive noise. The network setup comprises one source node and two destination nodes, among which one of the nodes closer to the source acts as a relay, transmitting a symbol from the source node to the destination. The relay considered is a decoded-and-forward relay, which helps to transmit the source symbol to the destination over two-time frames. We derive the mathematical expressions to determine outage probabilities at the relay and destination nodes within the studied network. Furthermore, we develop an optimization problem related to the power distribution coefficient at the source, deriving the corresponding mathematical expressions. To validate our analysis, we employ simulations and numerical methods.
Dual-Stage NOMA for Relay-Enabled Power Line Communication Under Bernoulli-Gaussian Noise
Roopesh Ramesh, R. Muralishankar, Sanjeev Gurugopinath
2023 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2023, 2023
This paper studies the performance of outage probability in a dual-stage (DS) cooperative non-orthogonal multiple access (NOMA) system for power line communication (PLC) in the presence of Bernoulli-Gaussian impulsive noise. The network configuration includes a single source node and two destination nodes among which one the node act as a decode-and-forward (DF) relay node. This is similar to the existing research on DS NOMA setup, which primarily focused on additive white Gaussian noise. In this setup, both the source and relay transmit data to the destination node using NOMA across two consecutive time frames. We provide mathematical expressions for outage probabilities at both nodes in the context of Bernoulli-Gaussian noise. Furthermore, our analysis is validated using numerical methods and Monte Carlo simulations. Additionally, our results indicate that a DS-NOMA configuration exhibits lower outage probability when compared to a single-stage NOMA setup.
Robust voice activity detection based on weighted average of long-term quadratic Renyi and differential entropies
R. Muralishankar, Debayan Ghosh, Sanjeev Gurugopinath
Digital Signal Processing A Review Journal, 2022
Generalized energy-based spectrum sensing: Active threshold correction under noise uncertainty
Sutapa Sarkar, R. Muralishankar, Sanjeev Gurugopinath
2021 2nd International Conference for Emerging Technology Incet 2021, 2021
In this paper, we propose an active threshold correction (ATC) for the generalized energy detector (GED) under spectrum sensing to alleviate the effects of noise variance uncertainty (NVU) and fading. First, we derive the probability of false alarm and the probability of detection for GED, with an arbitrary exponent, assuming that the test statistic follows a gamma distribution and under Nakagami-m fading. Next, we present the effect of NVU on the performance of GED. Later, we derive an analytic expression for the corresponding ATC under NVU and Nakagami-m fading. Through numerical method, we show that by employing the ATC, the performance of GED improves in the presence of NVU and fading. Moreover, in the considered setup for GED, we show that the GED outperforms the energy detector.
Deep Neural Network Architectures for Spectrum Sensing Using Signal Processing Features
Shreeram Suresh Chandra, Akshay Upadhye, Purushothaman Saravanan, Sanjeev Gurugopinath, R. Muralishankar
2021 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2021 Proceedings, 2021
A Novel Modified Mel-DCT Filter Bank Structure with Application to Voice Activity Detection
R. Muralishankar, Debayan Ghosh, Sanjeev Gurugopinath
IEEE Signal Processing Letters, 2020
Enhanced Performance of Generalized Energy Based Spectrum Sensing with Adaptive Threshold
Sutapa Sarkar, R. Muralishankar, Sanjeev Gurugopinath
4th International Conference on Electrical Electronics Communication Computer Technologies and Optimization Techniques Iceeccot 2019, 2019
Asymptotic analysis of generalized energy-based spectrum sensing in cognitive radios
Sutapa Sarkar, R. Muralishankar, Sanjeev Gurugopinath
2019 5th IEEE International Wie Conference on Electrical and Computer Engineering Wiecon Ece 2019 Proceedings, 2019
Performance Comparison of Joint Correlation and Improved Energy Detection for Spectrum Sensing
Arati Halaki, Sanjeev Gurugopinath, R. Muralishankar
2019 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2019 Proceedings, 2019
Voice Activity Detection Using Novel Teager Energy Based Band Spectral Entropy
Raveesh Hegde, R. Muralishankar
Proceedings of the 4th International Conference on Communication and Electronics Systems Icces 2019, 2019
Capacity Analysis of a Narrowband Powerline Communication Channel under Impulsive Noise
Roopesh R., B.S. Sushma, Sanjeev Gurugopinath, R. Muralishankar
2019 11th International Conference on Communication Systems and Networks Comsnets 2019, 2019
Recovering bits from thin air: Demodulation of bandpass sampled noisy signals for space IoT
Sujay Narayana, R. Muralishankar, R. Venkatesha Prasad, Vijay S. Rao
IPSN 2019 Proceedings of the 2019 Information Processing in Sensor Networks, 2019
Dynamic threshold correction based on the exact statistics of energy detection in spectrum sensing
Sutapa Sarkar, R. Muralishankar, Sanjeev Gurugopinath
2019 International Conference on Wireless Communications Signal Processing and Networking Wispnet 2019, 2019
Performance characterization of broadband powerline communication for internet-of-things
B.S. Sushma, R. Roopesh, Sanjeev Gurugopinath, R. Muralishankar
2019 International Conference on Wireless Communications Signal Processing and Networking Wispnet 2019, 2019
Entropy-based spectrum sensing under symmetric alpha stable impulsive noise
Arati Halaki, C.A. Manohar, Sanjeev Gurugopinath, R. Muralishankar
2019 International Conference on Wireless Communications Signal Processing and Networking Wispnet 2019, 2019
An objective measure to assess musical noise using connected time-frequency regions
Ajey Saligrama, H.G. Ranjani, R. Muralishankar, H. N. Shankar
25th National Conference on Communications Ncc 2019, 2019
Performance Comparison of Cepstral Features for Language Identification using Convolutional Recurrent Neural Networks
Shashank Satyanarayana, Narendra K.C., Sanjeev Gurugopinath, R. Muralishankar, R. Kumaraswamy
Indicon 2018 15th IEEE India Council International Conference, 2018
Geometric power detector for spectrum sensing under symmetric alpha stable noise
Sanjeev Gurugopinath, R. Muralishankar
Electronics Letters, 2018
Speech enhancement in modulation domain using discriminative random fields
Ajey Saligrama, H. G. Ranjani, H. N. Shankar, R. Muralishankar
2017 IEEE International Symposium on Signal Processing and Information Technology Isspit 2017, 2018
Robust voice activity detection using frequency domain long-term differential entropy
Debayan Ghosh, R Muralishankar, Sanjeev Gurugopinath
Proceedings of the Annual Conference of the International Speech Communication Association Interspeech, 2018
Robust spectrum sensing based on spectral flatness measure
S. Gurugopinath, R. Muralishankar, H.N. Shankar
Electronics Letters, 2017
Differential Entropy-Driven Spectrum Sensing under Generalized Gaussian Noise
Sanjeev Gurugopinath, Rangarao Muralishankar, H.N. Shankar
IEEE Communications Letters, 2016
Speech enhancement using Discriminative Random Fields
Ajey Saligrama, Ranjani H. G., H. N. Shankarz, R. Muralishankarx
IEEE Region 10 Annual International Conference Proceedings TENCON, 2016
Spectrum sensing in the presence of Cauchy noise through differential entropy
Sanjeev Gurugopinath, R. Muralishankar, H. N. Shankar
2016 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2016 Proceedings, 2016
Adaptive attitude control of the spherical drone on SO(3)
Ashutosh Simha, Manasa Tallam, H.N. Shankar, R. Muralishankar, Simha H.N.L.N.
2016 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2016 Proceedings, 2016
Differential entropy driven Goodness-of-Fit test for spectrum sensing
Sanjeev Gurugopinath, Rangarao Muralishankar, H. N. Shankar
Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering Lnicst, 2015
Maximizing the fair allocation of opportunistic spectrum for CR ad hoc networks
V. S. Rao, R. V. Prasad, R. Muralishankar, I. G. M. M. Niemegeers
Globecom IEEE Global Telecommunications Conference, 2011
Relative robustness of detection to detector threshold by joint optimization in WSN over fading MAC and with non-identical sensor SNRs
H. N. Shankar, R. Muralishankar, Aniketh Venkat, Sahana Prasad, Shonali Balakrishna, Sindhura Chayapathy, Manisha Sinha
Proceedings 2011 Annual IEEE India Conference Engineering Sustainable Solutions Indicon 2011, 2011
Optimal power allocation over a fading MAC with varying observation SNRS in resource constrained wireless sensor network
R. Muralishankar, H. N. Shankar, Aniketh Venkat, Manisha Sinha
IEEE International Conference on Communications, 2011
Order statistics for voice activity detection in VoIP
R. Muralishankar, R. V. Prasad, S. Vijay, H. N. Shankar
IEEE International Conference on Communications, 2010
Perceptual-MVDR based analysis-synthesis of pitch synchronous frames for pitch modification
R. Muralishankar, M. Ravi Shanker, A. G. Ramakrishnan
2008 IEEE International Conference on Multimedia and Expo Icme 2008 Proceedings, 2008
A comparative analysis of noise robust speech features extracted from all-pass based warping with MFCC in a noisy phoneme recognition
R. Muralishankar, Douglas O'Shaughnessy
Proceedings the 3rd International Conference on Digital Telecommunications Icdt 2008, 2008
Multiparty audioconferencing on wireless networks
Broadband Mobile Multimedia Techniques and Applications, 2008
A holistic study of voip session quality - The knobs that control
R. Venkatesha Prasad, Vijay S. Rao, H. N. Shankar, Przemyslaw Pawelczak, R. Muralishankar, Ignas Niemegeers
2008 5th IEEE Consumer Communications and Networking Conference Ccnc 2008, 2008
Bauer method of MVDR spectral factorization for pitch modification in the source domain
M. Ravi Shanker, R. Muralishankar, A. G. Ramakrishnan
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2007
Theoretical complex cepstrum of DCT and warped DCT filters
R. Muralishankar, Abhijeet Sangwan, Douglas O'Shaughnessy
IEEE Signal Processing Letters, 2007
A performance analysis of features from complex cepstra of warped DST, DCT and DHT filters for phoneme recognition
R. Muralishankar, H. N. Shankar, Douglas O'Shaughnessy
2007 15th International Conference on Digital Signal Processing DSP 2007, 2007
Complex cepstrum of discrete Hartley and warped discrete Hartley filters
R. Muralishankar, Abhijeet Sangwan
2006 IEEE 12th Digital Signal Processing Workshop and 4th IEEE Signal Processing Education Workshop, 2006
Voice activity detection for VoIP-An information theoretic approach
R. V. Prasad, R. Muralishankar, S. Vijay, H. N. Shankar, Przemyslaw Pawelczak, Ignas Niemegeers
Globecom IEEE Global Telecommunications Conference, 2006
Warped discrete cosine transform cepstrum: A new feature for speech processing
13th European Signal Processing Conference Eusipco 2005, 2005
Performance analysis of the warped discrete cosine transform cepstrum with MFCC using different classifiers
A. Sangwan, R. Muralishankar, D. O'Shaughnessy
2005 IEEE Workshop on Machine Learning for Signal Processing, 2005
Statistical properties of the warped discrete cosine transform cepstrum compared with MFCC
9th European Conference on Speech Communication and Technology, 2005
Toward better automatic speech recognition
Canadian Acoustics Acoustique Canadienne, 2005
Pseudo complex cepstrum using discrete cosine transform
R. Muralishankar, A. G. Ramakrishnan
International Journal of Speech Technology, 2005
Subspace-based speaker-independent vowel recognition
R. Muralishankar, D. O'Shaughnessy
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2005
Time-scaling of speech and music using independent subspace analysis
2004 International Conference on Signal Processing and Communications Spcom, 2004
Modification of pitch using DCT in the source domain
R. Muralishankar, A.G. Ramakrishnan, P. Prathibha
Speech Communication, 2004
Time-scaling of speech using independent subspace analysis
8th International Conference on Spoken Language Processing ICSLP 2004, 2004
Wavelet-based estimation of hemodynamic response function
R. Srikanth, R. Muralishankar, A. G. Ramakrishnan
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2004
Subspace and hypothesis based effective segmentation of Co-articulated basic-units for concatenative speech synthesis
IEEE Region 10 Annual International Conference Proceedings TENCON, 2003
Subspace based vowel-consonant segmentation
R. Muralishankar, A. Vijaya krishna, A.G. Ramakrishnan
IEEE Workshop on Statistical Signal Processing Proceedings, 2003
DCT based pseudo complex cepstrum
R. Muralishankar, A. G. Ramakrishnan
ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2002
Warped-LP residual resampling using DCT for pitch modification
7th International Conference on Spoken Language Processing ICSLP 2002, 2002
A complete text-to-speech synthesis system in Tamil
G.L. Jayavardhana Rama, A.G. Ramakrishnan, R. Muralishankar, R. Prathibha
Proceedings of 2002 IEEE Workshop on Speech Synthesis, 2002

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Scopus Publications