Pub Date : 2022-12-01DOI: 10.1109/MBITS.2023.3275885
A. Dooms, Carlo Emerencia, Alexander Lemmens
The security of popular public key-cryptographic protocols, such as RSA, Diffie–Hellman key exchange and the digital signature algorithm (DSA), is endangered by the advent of quantum computers. Shor brought a big breakthrough with his quantum algorithm that can be used to factor an arbitrarily large integer into the product of its prime factors, hence jeopardizing the security of RSA, and that at the same time also solves the Discrete Logarithm Problem, which raises issues for certain Diffie–Hellman-based cryptosystems and digital signatures. It is hence crucial to upgrade our current tools for postquantum cryptography: it should be infeasible, even using quantum algorithms, to break the new cryptosystems. Popular candidates include for example elliptic curve or lattice-based cryptography, but they share something in common: they are specific cases of the more general Hidden Subgroup and connected Hidden Shift Problem.
{"title":"Shaping Postquantum Cryptography: The Hidden Subgroup and Shift Problems","authors":"A. Dooms, Carlo Emerencia, Alexander Lemmens","doi":"10.1109/MBITS.2023.3275885","DOIUrl":"https://doi.org/10.1109/MBITS.2023.3275885","url":null,"abstract":"The security of popular public key-cryptographic protocols, such as RSA, Diffie–Hellman key exchange and the digital signature algorithm (DSA), is endangered by the advent of quantum computers. Shor brought a big breakthrough with his quantum algorithm that can be used to factor an arbitrarily large integer into the product of its prime factors, hence jeopardizing the security of RSA, and that at the same time also solves the Discrete Logarithm Problem, which raises issues for certain Diffie–Hellman-based cryptosystems and digital signatures. It is hence crucial to upgrade our current tools for postquantum cryptography: it should be infeasible, even using quantum algorithms, to break the new cryptosystems. Popular candidates include for example elliptic curve or lattice-based cryptography, but they share something in common: they are specific cases of the more general Hidden Subgroup and connected Hidden Shift Problem.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134349726","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 : 2022-12-01DOI: 10.1109/MBITS.2023.3262237
L. Dolecek, E. Soljanin
For secure practical systems, quantum key distribution (QKD) must provide high key rates over long distances. Time-entanglement-based QKD promises to increase the secret key rate and distribution distances compared to other QKD implementations. This article describes the major steps in QKD protocols, focusing on the nascent QKD technology based on high-dimensional time-bin entangled photons. We overview the state-of-the-art from the information and coding theory perspective. In particular, we discuss the key rate loss due to single-photon detector imperfections. We hope the open questions posed and discussed in this article will inspire information and coding theorists to contribute to and impact fledgling quantum applications and influence future quantum communication systems.
{"title":"QKD Based on Time-Entangled Photons and Its Key-Rate Promise","authors":"L. Dolecek, E. Soljanin","doi":"10.1109/MBITS.2023.3262237","DOIUrl":"https://doi.org/10.1109/MBITS.2023.3262237","url":null,"abstract":"For secure practical systems, quantum key distribution (QKD) must provide high key rates over long distances. Time-entanglement-based QKD promises to increase the secret key rate and distribution distances compared to other QKD implementations. This article describes the major steps in QKD protocols, focusing on the nascent QKD technology based on high-dimensional time-bin entangled photons. We overview the state-of-the-art from the information and coding theory perspective. In particular, we discuss the key rate loss due to single-photon detector imperfections. We hope the open questions posed and discussed in this article will inspire information and coding theorists to contribute to and impact fledgling quantum applications and influence future quantum communication systems.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133631452","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 : 2022-12-01DOI: 10.1109/MBITS.2023.3285848
H. Pfister, C. Piveteau, J. Renes, Narayanan Rengaswamy
This article reviews belief propagation (BP) for classical inference problems and describes its extension to quantum systems, which is known as BP with quantum messages (BPQM). Since BP plays a key role in many low-complexity decoders for error-correcting codes, BPQM enables the practical extension of these decoders to classical quantum channels, such as the pure state channel.
{"title":"Belief Propagation for Classical and Quantum Systems: Overview and Recent Results","authors":"H. Pfister, C. Piveteau, J. Renes, Narayanan Rengaswamy","doi":"10.1109/MBITS.2023.3285848","DOIUrl":"https://doi.org/10.1109/MBITS.2023.3285848","url":null,"abstract":"This article reviews belief propagation (BP) for classical inference problems and describes its extension to quantum systems, which is known as BP with quantum messages (BPQM). Since BP plays a key role in many low-complexity decoders for error-correcting codes, BPQM enables the practical extension of these decoders to classical quantum channels, such as the pure state channel.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"PP 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126446794","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 : 2022-11-01DOI: 10.1109/MBITS.2022.3216536
S. K. Mohammed, R. Hadani, A. Chockalingam, R. Calderbank
Orthogonal time frequency space (OTFS) is a framework for communication and active sensing that processes signals in the delay-Doppler (DD) domain. This article explores three key features of the OTFS framework, and explains their value to applications. The first feature is a compact and sparse DD domain parameterization of the wireless channel, where the parameters map directly to physical attributes of the reflectors that comprise the scattering environment, and as a consequence these parameters evolve predictably. The second feature is a novel waveform/modulation technique, matched to the DD channel model, that embeds information symbols in the DD domain. The relation between channel inputs and outputs is localized, non-fading, and predictable, even in the presence of significant delay and Doppler spread, and as a consequence the channel can be efficiently acquired and equalized. By avoiding fading, the post equalization signal to noise ratio remains constant across all information symbols in a packet, so that bit error performance is superior to contemporary multicarrier waveforms. Further, the OTFS carrier waveform is a localized pulse in the DD domain, making it possible to separate reflectors along both delay and Doppler simultaneously, and to achieve a high-resolution DD radar image of the environment. In other words, the DD parameterization provides a common mathematical framework for communication and radar. This is the third feature of the OTFS framework, and it is ideally suited to intelligent transportation systems involving self-driving cars and unmanned ground/aerial vehicles, which are self/network controlled. The OTFS waveform is able to support stable and superior performance over a wide range of user speeds. In the emerging 6G systems and standards, it is ideally suited to support mobility-on-demand envisaged in next generation cellular and WiFi systems, as well as high-mobility use cases. Finally, the compactness and predictability of the OTFS input–output relation makes it a natural fit for machine learning and AI algorithms designed for the intelligent nonmyopic management of control plane resources in future mobile networks.
{"title":"OTFS—A Mathematical Foundation for Communication and Radar Sensing in the Delay-Doppler Domain","authors":"S. K. Mohammed, R. Hadani, A. Chockalingam, R. Calderbank","doi":"10.1109/MBITS.2022.3216536","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3216536","url":null,"abstract":"Orthogonal time frequency space (OTFS) is a framework for communication and active sensing that processes signals in the delay-Doppler (DD) domain. This article explores three key features of the OTFS framework, and explains their value to applications. The first feature is a compact and sparse DD domain parameterization of the wireless channel, where the parameters map directly to physical attributes of the reflectors that comprise the scattering environment, and as a consequence these parameters evolve predictably. The second feature is a novel waveform/modulation technique, matched to the DD channel model, that embeds information symbols in the DD domain. The relation between channel inputs and outputs is localized, non-fading, and predictable, even in the presence of significant delay and Doppler spread, and as a consequence the channel can be efficiently acquired and equalized. By avoiding fading, the post equalization signal to noise ratio remains constant across all information symbols in a packet, so that bit error performance is superior to contemporary multicarrier waveforms. Further, the OTFS carrier waveform is a localized pulse in the DD domain, making it possible to separate reflectors along both delay and Doppler simultaneously, and to achieve a high-resolution DD radar image of the environment. In other words, the DD parameterization provides a common mathematical framework for communication and radar. This is the third feature of the OTFS framework, and it is ideally suited to intelligent transportation systems involving self-driving cars and unmanned ground/aerial vehicles, which are self/network controlled. The OTFS waveform is able to support stable and superior performance over a wide range of user speeds. In the emerging 6G systems and standards, it is ideally suited to support mobility-on-demand envisaged in next generation cellular and WiFi systems, as well as high-mobility use cases. Finally, the compactness and predictability of the OTFS input–output relation makes it a natural fit for machine learning and AI algorithms designed for the intelligent nonmyopic management of control plane resources in future mobile networks.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115838559","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 : 2022-11-01DOI: 10.1109/MBITS.2023.3237514
A. Gamal, R. Gray
This article highlights some of Katalin Marton's main contributions to rate distortion theory, probability theory, and multiuser information theory and their lasting impact on the work of many other researchers.
{"title":"Katalin Marton’s Lasting Legacy","authors":"A. Gamal, R. Gray","doi":"10.1109/MBITS.2023.3237514","DOIUrl":"https://doi.org/10.1109/MBITS.2023.3237514","url":null,"abstract":"This article highlights some of Katalin Marton's main contributions to rate distortion theory, probability theory, and multiuser information theory and their lasting impact on the work of many other researchers.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131286289","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 : 2022-11-01DOI: 10.1109/mbits.2022.3205873
U. Thomas, R. Laroia
As a child, Rajiv Laroia was sure he wanted to be a physicist and work in academia.
小时候，拉吉夫·拉罗亚(Rajiv Laroia)就确定自己想成为一名物理学家，在学术界工作。
{"title":"Q&A: Rajiv Laroia on Applying Information Theory to Wireless Communications and Helping Computers See Better","authors":"U. Thomas, R. Laroia","doi":"10.1109/mbits.2022.3205873","DOIUrl":"https://doi.org/10.1109/mbits.2022.3205873","url":null,"abstract":"As a child, Rajiv Laroia was sure he wanted to be a physicist and work in academia.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134119924","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 : 2022-11-01DOI: 10.1109/MBITS.2022.3210891
J. Kieffer, E. Yang
A data string can be represented with the help of context-free grammar such that the string is the unique string belonging to the language of the grammar. One can then losslessly compress the string indirectly by encoding the grammar into a unique binary codeword. This approach to data compression, called grammar-based data compression, can also be employed to losslessly compress graphical data structures, which are graphs in which every vertex carries a data label. Under mild restrictions, grammar-based data compression schemes are universal compressors, meaning that they perform at least as well as any finite-state compression scheme. Some of the theory of universal grammar-based compressors is surveyed. Applications of grammar-based compressors to various areas, such as bioinformatics and data networks, are discussed. Future directions for grammar-based compression research are outlined, including compression issues arising in highly repetitive databases and issues concerning the compression of sparse graphical data.
{"title":"Survey of Grammar-Based Data Structure Compression","authors":"J. Kieffer, E. Yang","doi":"10.1109/MBITS.2022.3210891","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3210891","url":null,"abstract":"A data string can be represented with the help of context-free grammar such that the string is the unique string belonging to the language of the grammar. One can then losslessly compress the string indirectly by encoding the grammar into a unique binary codeword. This approach to data compression, called grammar-based data compression, can also be employed to losslessly compress graphical data structures, which are graphs in which every vertex carries a data label. Under mild restrictions, grammar-based data compression schemes are universal compressors, meaning that they perform at least as well as any finite-state compression scheme. Some of the theory of universal grammar-based compressors is surveyed. Applications of grammar-based compressors to various areas, such as bioinformatics and data networks, are discussed. Future directions for grammar-based compression research are outlined, including compression issues arising in highly repetitive databases and issues concerning the compression of sparse graphical data.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128377386","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 : 2022-10-05DOI: 10.1109/MBITS.2022.3212978
Wei Yu, Foad Sohrabi, Tao Jiang
Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing the system according to the model. The advent of modern machine learning techniques, specifically deep neural networks, has opened up opportunities for data-driven system design and optimization. This article draws examples from the optimization of reconfigurable intelligent surface, distributed channel estimation and feedback for multiuser beamforming, and active sensing for millimeter wave initial alignment to illustrate that a data-driven design that bypasses explicit channel modeling can often discover excellent solutions to communication system design and optimization problems that are otherwise computationally difficult to solve. We show that by performing an end-to-end training of a deep neural network using a large number of channel samples, a machine learning-based approach can potentially provide significantly system-level improvements as compared to the traditional model-based approach for solving optimization problems. The key to the successful applications of machine learning techniques are in choosing the appropriate neural network architecture to match the underlying problem structure.
{"title":"Role of Deep Learning in Wireless Communications","authors":"Wei Yu, Foad Sohrabi, Tao Jiang","doi":"10.1109/MBITS.2022.3212978","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3212978","url":null,"abstract":"Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing the system according to the model. The advent of modern machine learning techniques, specifically deep neural networks, has opened up opportunities for data-driven system design and optimization. This article draws examples from the optimization of reconfigurable intelligent surface, distributed channel estimation and feedback for multiuser beamforming, and active sensing for millimeter wave initial alignment to illustrate that a data-driven design that bypasses explicit channel modeling can often discover excellent solutions to communication system design and optimization problems that are otherwise computationally difficult to solve. We show that by performing an end-to-end training of a deep neural network using a large number of channel samples, a machine learning-based approach can potentially provide significantly system-level improvements as compared to the traditional model-based approach for solving optimization problems. The key to the successful applications of machine learning techniques are in choosing the appropriate neural network architecture to match the underlying problem structure.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129613362","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 : 2022-10-01DOI: 10.1109/mbits.2022.3206279
Miguel Rodrigues
Recent years have witnessed the emergence of various sophisticated information processing tools—including some involving artificial intelligence —that are capable of interrogating increasingly complex datasets in order to tackle challenges arising in a wide range of application domains.
{"title":"Special Issue on Information Processing in the Arts and Humanities","authors":"Miguel Rodrigues","doi":"10.1109/mbits.2022.3206279","DOIUrl":"https://doi.org/10.1109/mbits.2022.3206279","url":null,"abstract":"Recent years have witnessed the emergence of various sophisticated information processing tools—including some involving artificial intelligence —that are capable of interrogating increasingly complex datasets in order to tackle challenges arising in a wide range of application domains.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122076429","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 : 2022-10-01DOI: 10.1109/MBITS.2022.3197559
Shira Faigenbaum-Golovin, Arie Shaus, B. Sober
Ancient texts are unique evidence providing a glimpse into the thoughts, day-to-day life, and culture of people of long-gone eras. Paleography, the study of writing, aims at documenting the inscriptions, transliterating the texts, reconstructing their historical context, and studying the evolution of writing itself. The digital revolution gave rise to computational paleography, introducing new tools of data acquisition, image processing, and machine learning. Herein, we will provide an introduction to the emerging field of computational paleography through the lens of ancient Hebrew inscriptions, dating from the Iron Age through the Middle Ages. The years that passed since their composition had a great effect on their preservation level, including blurs, stains, and erosions; moreover, some documents tend to fade in the years after their discovery. Therefore, it is of paramount importance to promptly document ancient inscriptions using the most suitable imaging techniques, such as visible, infra-red, or multispectral imaging. Image analysis and processing techniques, such as binarizations, letter segmentation, and letters’ prior estimation are valuable in their own right or may serve as a stage for subsequent tasks. We will also discuss automatic handwriting analysis and writers’ identification, which could shed light on the historical background of the inscriptions.
{"title":"Computational Handwriting Analysis of Ancient Hebrew Inscriptions—A Survey","authors":"Shira Faigenbaum-Golovin, Arie Shaus, B. Sober","doi":"10.1109/MBITS.2022.3197559","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3197559","url":null,"abstract":"Ancient texts are unique evidence providing a glimpse into the thoughts, day-to-day life, and culture of people of long-gone eras. Paleography, the study of writing, aims at documenting the inscriptions, transliterating the texts, reconstructing their historical context, and studying the evolution of writing itself. The digital revolution gave rise to computational paleography, introducing new tools of data acquisition, image processing, and machine learning. Herein, we will provide an introduction to the emerging field of computational paleography through the lens of ancient Hebrew inscriptions, dating from the Iron Age through the Middle Ages. The years that passed since their composition had a great effect on their preservation level, including blurs, stains, and erosions; moreover, some documents tend to fade in the years after their discovery. Therefore, it is of paramount importance to promptly document ancient inscriptions using the most suitable imaging techniques, such as visible, infra-red, or multispectral imaging. Image analysis and processing techniques, such as binarizations, letter segmentation, and letters’ prior estimation are valuable in their own right or may serve as a stage for subsequent tasks. We will also discuss automatic handwriting analysis and writers’ identification, which could shed light on the historical background of the inscriptions.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121156181","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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