Annapurna Pradhan | Biju Patnaik University and Technology, Rourkela, Odisha, INDIA (original) (raw)

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Papers by Annapurna Pradhan

2021 Advanced Communication Technologies and Signal Processing (ACTS)

Ultra-reliable low latency communication (URLLC) is one of the key services offered by the 5G wir... more Ultra-reliable low latency communication (URLLC) is one of the key services offered by the 5G wireless networks. This service type is characterized by stringent high reliability and low latency criteria, thus require efficient multiple access techniques to facilitate various mission-critical services. Non-orthogonal multiple access (NOMA) effectively enhances spectral efficiency and ensures fairness by exploiting the power allocation at receivers compared to orthogonal multiple access (OMA). Therefore, it becomes suitable for facilitating URLLC service. The addition of the cooperative relaying technique to NOMA can enhance the reliability of the data transmission. This motivated us to analyze the performance of cooperative NOMA (C-NOMA) for short packet URLLC data transmission. Hence, we have proposed a C-NOMA based system using the decode and forward (DF) relaying technique and successive interference cancellation (SIC) at the receiver to minimize the overall error probability for URLLC. The minimization of overall outage probability has been achieved by optimizing power allocation at the receiver considering the decoding error and error due to queuing delay violation while ensuring fair throughput among users. The simulation study has shown that the proposed C-NOMA based short packet transmission improves overall outage probability, energy efficiency, and ensures fair throughput among users in comparison to only NOMA and OMA based techniques.

2021 Advanced Communication Technologies and Signal Processing (ACTS), 2021

The knowledge regarding the wireless channel parameters is essential for ensuring reliable end-to... more The knowledge regarding the wireless channel parameters is essential for ensuring reliable end-to-end communication. Therefore, correct channel estimation becomes essential to recover the transmitted message efficiently. In this paper, we have proposed a Convolutional Neural Network (CNN) based architecture to estimate the channel using the knowledge regarding pilot locations in the time and frequency domain. We have considered the time-frequency response of the pilot positions as an image and used deep learning (DL) based image processing techniques to recover the complete information for correct channel estimation. The quality of the image is enhanced by a CNN based super-resolution network. Then the output of the first CNN is passed through an image restoration network to have high resolution image in a pipe-lined architecture. Moreover, the performance of the proposed DL based channel estimation has been evaluated in terms of mean square error. It can be observed from the simulation results that the proposed two-stage CNN based approach outperforms other baseline channel estimation methods.

The demand for pervasive wireless access and the need for high data rates are expected to grow si... more The demand for pervasive wireless access and the need for high data rates are expected to grow significantly in the foreseeable future with the proliferation of novel resource demanding applications such as gaming, mobile TV, or personalized wireless services. The emergence of these new services leads to stringent requirements on the bit rate per square kilometer that next generation wireless networks are expected to deliver. This increasing trend has urged wireless and mobile operators to examine new ways for improving their coverage, boosting their network's capacity, and lowering their capital and operating expenditures (CAPEX and OPEX) of their mobile networks. A promising approach to solving this problem is through the deployment of Small Cell Networks (SCNs) and Heterogeneous network which represent a novel networking paradigm based on the idea of deploying short-range, low-power, and low-cost base stations operating in conjunction with the main macrocellular network infra...

2016 2nd International Conference on Computational Intelligence and Networks (CINE), 2016

The paper presents a modified framework for image compression using Burrow Wheeler Transform (BWT... more The paper presents a modified framework for image compression using Burrow Wheeler Transform (BWT). The BWT is widely used in text compression but very few attempts have been made to BWT in image compression. Just like text compression it will not directly apply to an image. After performing some recorder arrangement it has been applied successively in an image compression framework. In the proposed approach it is applied before entropy coding and it has observed that a significant amount of improvement in terms of compression efficiency is achieved as compared to the JPEG and wavelet based JPEG2000 approach. The proposed scheme is simulated along with other standard image coding scheme. Performance comparisons have been made with respect to PSNR in (db), rate distortion performance in terms of bit rate (bpp) vs. PSNR and compression ratio. In general, it is obtained that the proposed scheme has a superior performance as compared to its competent schemes.

Procedia Computer Science, 2015

Image compression has been a widely researched field for decades. Recently, there has been a grow... more Image compression has been a widely researched field for decades. Recently, there has been a growing interest in using basis selection algorithms for signal approximation and compression. Signal approximation using a linear combination of basis from an over-complete dictionary has proven to be an NP-hard problem. By selecting a smaller number of basis than the span of the signal, we achieve glossy compression in exchange for a small reconstruction error. For the past few decades orthogonal and biorthogonal complete dictionaries such as Discrete Cosine Transform (DCT) or wavelets were the dominant transform domain representations of signals. Over-complete dictionaries have been intensively studied and successfully applied to various application such as image de-noising, compression etc. The DCT and wavelet based compression methods suffer from blocking and ringing artefacts and also they are unable to capture the directional information. So, in this context an investigation has been made by using sparse coding method by Orthogonal Matching Pursuit (OMP) algorithm. In this proposed work, conventional DCT can be replaced by a set of trained dictionaries. For dictionary construction we have used a combination of DCT and Gabor basis and to encode the trained dictionary elements we have employed OMP algorithm. Experimental results demonstrate that the proposed method provides gains in Rate Distortion (RD) performance and improvements in perceptual quality.

Transactions on Machine Learning and Artificial Intelligence, 2014

For future broadband cellular communication networks wireless carriers has motivated the explorat... more For future broadband cellular communication networks wireless carriers has motivated the exploration of the under-utilized millimeter (mm-wave) frequency spectrum. The cellular mm wave propagation is densely populated in the world. It is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. This paper provides the overview of the recent worldwide activities for beyond 4G and 5G wireless standardization and technological aspects for millimeter wave communications. A complete characterization of the mm-wave link for next generation 5G mobile broadband remains elusive. The coverage, directionality and reliability of mm-wave communications will require new innovations in system design and communication technologies that are far from trivial. The transmission range and spatial selectivity's in the mm-wave bands especially in non line of sight channels can be increased by system design. So it require highly direction antennas and steerable antenna beams to compensate for the high propagation loss.

2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2020

The co-existence of enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication... more The co-existence of enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) services in emerging 5G technology are essential to support many diversified cutting edge applications with the requirement of high data rate and low latency. In this multiservice co-existence, optimizing resource allocation with efficient scheduling is a challenging task. The hard latency bound URLLC traffic selectively overlapped by puncturing the ongoing eMBB transmission. This results in a potential loss to the data rate of eMBB traffic. Therefore, we propose resource allocation based on a joint metric of the minimum achievable rate of eMBB and optimal URLLC placement demand for minimizing loss. The resource allocation to each URLLC users in any mini-slot is done based on the proposed metric.

The process of image compression has been the most researched area for decades. Image compression... more The process of image compression has been the most researched area for decades. Image compression is a necessity for the transmission of images and the storage of images in an efficient manner. This is because image compression represents image having less correlated pixels, eliminates redundancy and also removes irrelevant pixels. The most commonly known techniques for image compression are JPEG and JPEG 2000. But these two have certain drawbacks and thus various other techniques have been popping up, of late. Recently, a growing interest has been marked for the use of basis selection algorithms for signal approximation and compression. In the recent past, the orthogonal and bi-orthogonal complete dictionaries (like the Discrete Cosine Transform (DCT) or wavelets) have been the dominant transform domain representations. But, the DCT and the wavelet transform techniques experience blocking and ringing artefacts and also these are not capable of capturing directional

2021 Advanced Communication Technologies and Signal Processing (ACTS)

Ultra-reliable low latency communication (URLLC) is one of the key services offered by the 5G wir... more Ultra-reliable low latency communication (URLLC) is one of the key services offered by the 5G wireless networks. This service type is characterized by stringent high reliability and low latency criteria, thus require efficient multiple access techniques to facilitate various mission-critical services. Non-orthogonal multiple access (NOMA) effectively enhances spectral efficiency and ensures fairness by exploiting the power allocation at receivers compared to orthogonal multiple access (OMA). Therefore, it becomes suitable for facilitating URLLC service. The addition of the cooperative relaying technique to NOMA can enhance the reliability of the data transmission. This motivated us to analyze the performance of cooperative NOMA (C-NOMA) for short packet URLLC data transmission. Hence, we have proposed a C-NOMA based system using the decode and forward (DF) relaying technique and successive interference cancellation (SIC) at the receiver to minimize the overall error probability for URLLC. The minimization of overall outage probability has been achieved by optimizing power allocation at the receiver considering the decoding error and error due to queuing delay violation while ensuring fair throughput among users. The simulation study has shown that the proposed C-NOMA based short packet transmission improves overall outage probability, energy efficiency, and ensures fair throughput among users in comparison to only NOMA and OMA based techniques.

2021 Advanced Communication Technologies and Signal Processing (ACTS), 2021

The knowledge regarding the wireless channel parameters is essential for ensuring reliable end-to... more The knowledge regarding the wireless channel parameters is essential for ensuring reliable end-to-end communication. Therefore, correct channel estimation becomes essential to recover the transmitted message efficiently. In this paper, we have proposed a Convolutional Neural Network (CNN) based architecture to estimate the channel using the knowledge regarding pilot locations in the time and frequency domain. We have considered the time-frequency response of the pilot positions as an image and used deep learning (DL) based image processing techniques to recover the complete information for correct channel estimation. The quality of the image is enhanced by a CNN based super-resolution network. Then the output of the first CNN is passed through an image restoration network to have high resolution image in a pipe-lined architecture. Moreover, the performance of the proposed DL based channel estimation has been evaluated in terms of mean square error. It can be observed from the simulation results that the proposed two-stage CNN based approach outperforms other baseline channel estimation methods.

The demand for pervasive wireless access and the need for high data rates are expected to grow si... more The demand for pervasive wireless access and the need for high data rates are expected to grow significantly in the foreseeable future with the proliferation of novel resource demanding applications such as gaming, mobile TV, or personalized wireless services. The emergence of these new services leads to stringent requirements on the bit rate per square kilometer that next generation wireless networks are expected to deliver. This increasing trend has urged wireless and mobile operators to examine new ways for improving their coverage, boosting their network's capacity, and lowering their capital and operating expenditures (CAPEX and OPEX) of their mobile networks. A promising approach to solving this problem is through the deployment of Small Cell Networks (SCNs) and Heterogeneous network which represent a novel networking paradigm based on the idea of deploying short-range, low-power, and low-cost base stations operating in conjunction with the main macrocellular network infra...

2016 2nd International Conference on Computational Intelligence and Networks (CINE), 2016

The paper presents a modified framework for image compression using Burrow Wheeler Transform (BWT... more The paper presents a modified framework for image compression using Burrow Wheeler Transform (BWT). The BWT is widely used in text compression but very few attempts have been made to BWT in image compression. Just like text compression it will not directly apply to an image. After performing some recorder arrangement it has been applied successively in an image compression framework. In the proposed approach it is applied before entropy coding and it has observed that a significant amount of improvement in terms of compression efficiency is achieved as compared to the JPEG and wavelet based JPEG2000 approach. The proposed scheme is simulated along with other standard image coding scheme. Performance comparisons have been made with respect to PSNR in (db), rate distortion performance in terms of bit rate (bpp) vs. PSNR and compression ratio. In general, it is obtained that the proposed scheme has a superior performance as compared to its competent schemes.

Procedia Computer Science, 2015

Image compression has been a widely researched field for decades. Recently, there has been a grow... more Image compression has been a widely researched field for decades. Recently, there has been a growing interest in using basis selection algorithms for signal approximation and compression. Signal approximation using a linear combination of basis from an over-complete dictionary has proven to be an NP-hard problem. By selecting a smaller number of basis than the span of the signal, we achieve glossy compression in exchange for a small reconstruction error. For the past few decades orthogonal and biorthogonal complete dictionaries such as Discrete Cosine Transform (DCT) or wavelets were the dominant transform domain representations of signals. Over-complete dictionaries have been intensively studied and successfully applied to various application such as image de-noising, compression etc. The DCT and wavelet based compression methods suffer from blocking and ringing artefacts and also they are unable to capture the directional information. So, in this context an investigation has been made by using sparse coding method by Orthogonal Matching Pursuit (OMP) algorithm. In this proposed work, conventional DCT can be replaced by a set of trained dictionaries. For dictionary construction we have used a combination of DCT and Gabor basis and to encode the trained dictionary elements we have employed OMP algorithm. Experimental results demonstrate that the proposed method provides gains in Rate Distortion (RD) performance and improvements in perceptual quality.

Transactions on Machine Learning and Artificial Intelligence, 2014

For future broadband cellular communication networks wireless carriers has motivated the explorat... more For future broadband cellular communication networks wireless carriers has motivated the exploration of the under-utilized millimeter (mm-wave) frequency spectrum. The cellular mm wave propagation is densely populated in the world. It is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. This paper provides the overview of the recent worldwide activities for beyond 4G and 5G wireless standardization and technological aspects for millimeter wave communications. A complete characterization of the mm-wave link for next generation 5G mobile broadband remains elusive. The coverage, directionality and reliability of mm-wave communications will require new innovations in system design and communication technologies that are far from trivial. The transmission range and spatial selectivity's in the mm-wave bands especially in non line of sight channels can be increased by system design. So it require highly direction antennas and steerable antenna beams to compensate for the high propagation loss.

2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2020

The co-existence of enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication... more The co-existence of enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) services in emerging 5G technology are essential to support many diversified cutting edge applications with the requirement of high data rate and low latency. In this multiservice co-existence, optimizing resource allocation with efficient scheduling is a challenging task. The hard latency bound URLLC traffic selectively overlapped by puncturing the ongoing eMBB transmission. This results in a potential loss to the data rate of eMBB traffic. Therefore, we propose resource allocation based on a joint metric of the minimum achievable rate of eMBB and optimal URLLC placement demand for minimizing loss. The resource allocation to each URLLC users in any mini-slot is done based on the proposed metric.

The process of image compression has been the most researched area for decades. Image compression... more The process of image compression has been the most researched area for decades. Image compression is a necessity for the transmission of images and the storage of images in an efficient manner. This is because image compression represents image having less correlated pixels, eliminates redundancy and also removes irrelevant pixels. The most commonly known techniques for image compression are JPEG and JPEG 2000. But these two have certain drawbacks and thus various other techniques have been popping up, of late. Recently, a growing interest has been marked for the use of basis selection algorithms for signal approximation and compression. In the recent past, the orthogonal and bi-orthogonal complete dictionaries (like the Discrete Cosine Transform (DCT) or wavelets) have been the dominant transform domain representations. But, the DCT and the wavelet transform techniques experience blocking and ringing artefacts and also these are not capable of capturing directional