Pub Date : 2025-01-09DOI: 10.1109/TIT.2025.3527859
Theshani Nuradha;Mark M. Wilde
A quantum generalized divergence by definition satisfies the data-processing inequality; as such, the relative decrease in such a divergence under the action of a quantum channel is at most one. This relative decrease is formally known as the contraction coefficient of the channel and the divergence. Interestingly, there exist combinations of channels and divergences for which the contraction coefficient is strictly less than one. Furthermore, understanding the contraction coefficient is fundamental for the study of statistical tasks under privacy constraints. To this end, here we establish upper bounds on contraction coefficients for the hockey-stick divergence under privacy constraints, where privacy is quantified with respect to the quantum local differential privacy (QLDP) framework, and we fully characterize the contraction coefficient for the trace distance under privacy constraints. Using the machinery developed, we also determine an upper bound on the contraction of both the Bures distance and quantum relative entropy relative to the normalized trace distance, under QLDP constraints. Next, we apply our findings to establish bounds on the sample complexity of quantum hypothesis testing under privacy constraints. Furthermore, we study various scenarios in which the sample complexity bounds are tight while providing order-optimal quantum channels that achieve those bounds. Lastly, we show how private quantum channels provide fairness and Holevo information stability in quantum learning settings.
{"title":"Contraction of Private Quantum Channels and Private Quantum Hypothesis Testing","authors":"Theshani Nuradha;Mark M. Wilde","doi":"10.1109/TIT.2025.3527859","DOIUrl":"https://doi.org/10.1109/TIT.2025.3527859","url":null,"abstract":"A quantum generalized divergence by definition satisfies the data-processing inequality; as such, the relative decrease in such a divergence under the action of a quantum channel is at most one. This relative decrease is formally known as the contraction coefficient of the channel and the divergence. Interestingly, there exist combinations of channels and divergences for which the contraction coefficient is strictly less than one. Furthermore, understanding the contraction coefficient is fundamental for the study of statistical tasks under privacy constraints. To this end, here we establish upper bounds on contraction coefficients for the hockey-stick divergence under privacy constraints, where privacy is quantified with respect to the quantum local differential privacy (QLDP) framework, and we fully characterize the contraction coefficient for the trace distance under privacy constraints. Using the machinery developed, we also determine an upper bound on the contraction of both the Bures distance and quantum relative entropy relative to the normalized trace distance, under QLDP constraints. Next, we apply our findings to establish bounds on the sample complexity of quantum hypothesis testing under privacy constraints. Furthermore, we study various scenarios in which the sample complexity bounds are tight while providing order-optimal quantum channels that achieve those bounds. Lastly, we show how private quantum channels provide fairness and Holevo information stability in quantum learning settings.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 3","pages":"1851-1873"},"PeriodicalIF":2.2,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-09DOI: 10.1109/TIT.2025.3527887
Peng-Jun Wan;Pengpeng Chen
A power assignment over parallel Gaussian broadcast channels splits a power budget at the access point among all channel-user pairs subject to per-channel upper-bounds on the sum-power, and yields a rate allocation to all channel-user pairs. Its weighted energy efficiency (WEE) is the ratio of its weighted sum-rate over its sum-power plus a fixed positive overhead. The problem Max-WEE seeks a power assignment maximizing the WEE. Special variants of Max-WEE with unit weights or two users per channel have been extensively studied in the literature. But none of the existing algorithms for those special variants have known bounds on running time, mainly because they follow the general-purposed methods for fractional programming. In this paper, we first derive fundamental properties and closed-form expressions of maximum WEE. Then we devise a simple water-filling algorithm for Max-WEE. Assuming all users are presorted by weight, the water-filling algorithm has linear complexity in the number of channel-user pairs. Under a mild presorting condition, we further develop a linear-complexity algorithm for Max-WEE subject to rate demand.
{"title":"Maximizing Weighted Energy Efficiency Over Parallel Gaussian Broadcast Channels","authors":"Peng-Jun Wan;Pengpeng Chen","doi":"10.1109/TIT.2025.3527887","DOIUrl":"https://doi.org/10.1109/TIT.2025.3527887","url":null,"abstract":"A power assignment over parallel Gaussian broadcast channels splits a power budget at the access point among all channel-user pairs subject to per-channel upper-bounds on the sum-power, and yields a rate allocation to all channel-user pairs. Its weighted energy efficiency (WEE) is the ratio of its weighted sum-rate over its sum-power plus a fixed positive overhead. The problem Max-WEE seeks a power assignment maximizing the WEE. Special variants of Max-WEE with unit weights or two users per channel have been extensively studied in the literature. But none of the existing algorithms for those special variants have known bounds on running time, mainly because they follow the general-purposed methods for fractional programming. In this paper, we first derive fundamental properties and closed-form expressions of maximum WEE. Then we devise a simple water-filling algorithm for Max-WEE. Assuming all users are presorted by weight, the water-filling algorithm has linear complexity in the number of channel-user pairs. Under a mild presorting condition, we further develop a linear-complexity algorithm for Max-WEE subject to rate demand.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 3","pages":"2245-2256"},"PeriodicalIF":2.2,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/TIT.2025.3526689
Jieao Zhu;Zhongzhichao Wan;Linglong Dai;Tie Jun Cui
Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of EM channel characteristics, degrees-of-freedom, and system capacity. However, these works do not clarify how to integrate EIT knowledge into the design and optimization of wireless systems. To fill in this gap, in this paper, we propose an EIT-based statistical channel model with simplified parameterization. Thanks to the simplified closed-form expression of the EMCF, it can be readily applied to various channel modeling and inference tasks. Specifically, by averaging the solutions of Maxwell’s equations over a tunable von Mises distribution, we obtain a spatio-temporal correlation function (STCF) model of the EM channel, which we name as the EMCF. Furthermore, by tuning the parameters of the EMCF, we propose an EIT-based covariance estimator (EIT-Cov) to accurately capture the channel covariance. Since classical MMSE estimators can exploit prior information contained in the channel covariance matrix, we further propose the EIT-MMSE channel estimator by substituting EMCF for the covariance matrix. Simulation results show that both the proposed EIT-Cov covariance estimator and the EIT-MMSE channel estimator outperform their baseline algorithms, thus proving that EIT is beneficial to wireless communication systems.
{"title":"Electromagnetic Information Theory-Based Statistical Channel Model for Improved Channel Estimation","authors":"Jieao Zhu;Zhongzhichao Wan;Linglong Dai;Tie Jun Cui","doi":"10.1109/TIT.2025.3526689","DOIUrl":"https://doi.org/10.1109/TIT.2025.3526689","url":null,"abstract":"Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of EM channel characteristics, degrees-of-freedom, and system capacity. However, these works do not clarify how to integrate EIT knowledge into the design and optimization of wireless systems. To fill in this gap, in this paper, we propose an EIT-based statistical channel model with simplified parameterization. Thanks to the simplified closed-form expression of the EMCF, it can be readily applied to various channel modeling and inference tasks. Specifically, by averaging the solutions of Maxwell’s equations over a tunable von Mises distribution, we obtain a spatio-temporal correlation function (STCF) model of the EM channel, which we name as the EMCF. Furthermore, by tuning the parameters of the EMCF, we propose an EIT-based covariance estimator (EIT-Cov) to accurately capture the channel covariance. Since classical MMSE estimators can exploit prior information contained in the channel covariance matrix, we further propose the EIT-MMSE channel estimator by substituting EMCF for the covariance matrix. Simulation results show that both the proposed EIT-Cov covariance estimator and the EIT-MMSE channel estimator outperform their baseline algorithms, thus proving that EIT is beneficial to wireless communication systems.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 3","pages":"1777-1793"},"PeriodicalIF":2.2,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/TIT.2025.3526747
Roni Con;Ryan Gabrys;Eitan Yaakobi
In this work we consider a generalization of the well-studied problem of coding for “stuck-at” errors, which we refer to as “strong stuck-at” codes. In the traditional framework of stuck-at codes, the task involves encoding a message into a one-dimensional binary vector. However, a certain number of the bits in this vector are ‘frozen’, meaning they are fixed at a predetermined value and cannot be altered by the encoder. The decoder, aware of the proportion of frozen bits but not their specific positions, is responsible for deciphering the intended message. We consider a more challenging version of this problem where the decoder does not know also the fraction of frozen bits. We construct explicit and efficient encoding and decoding algorithms that get arbitrarily close to capacity in this scenario. Furthermore, to the best of our knowledge, our construction is the first, fully explicit construction of stuck-at codes that approach capacity.
{"title":"One Code Fits All: Strong Stuck-At Codes for Versatile Memory Encoding","authors":"Roni Con;Ryan Gabrys;Eitan Yaakobi","doi":"10.1109/TIT.2025.3526747","DOIUrl":"https://doi.org/10.1109/TIT.2025.3526747","url":null,"abstract":"In this work we consider a generalization of the well-studied problem of coding for “stuck-at” errors, which we refer to as “strong stuck-at” codes. In the traditional framework of stuck-at codes, the task involves encoding a message into a one-dimensional binary vector. However, a certain number of the bits in this vector are ‘frozen’, meaning they are fixed at a predetermined value and cannot be altered by the encoder. The decoder, aware of the proportion of frozen bits but not their specific positions, is responsible for deciphering the intended message. We consider a more challenging version of this problem where the decoder does not know also the fraction of frozen bits. We construct explicit and efficient encoding and decoding algorithms that get arbitrarily close to capacity in this scenario. Furthermore, to the best of our knowledge, our construction is the first, fully explicit construction of stuck-at codes that approach capacity.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 3","pages":"1666-1675"},"PeriodicalIF":2.2,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/TIT.2024.3525012
Ching-Fang Li;I-Hsiang Wang
In the binary hypothesis testing problem, it is well known that sequentiality in taking samples eradicates the trade-off between two error exponents, yet implementing the optimal test requires the knowledge of the underlying distributions, say $P_{0}$ and $P_{1}$ . In the scenario where the knowledge of distributions is replaced by empirically observed statistics from the respective distributions, the gain of sequentiality is less understood when subject to universality constraints over all possible $P_{0},P_{1}$ . In this work, the gap is mended by a unified study on sequentiality in the universal binary classification problem, where the universality constraints are set on the expected stopping time as well as the type-I error exponent. The type-I error exponent is required to achieve a pre-set distribution-dependent constraint $lambda (P_{0},P_{1})$ for all $P_{0},P_{1}$ . Under the proposed framework, different sequential setups are investigated so that fair comparisons can be made with the fixed-length counterpart. By viewing these sequential classification problems as special cases of a general sequential composite hypothesis testing problem, the optimal type-II error exponents are characterized. Specifically, in the general sequential composite hypothesis testing problem subject to universality constraints, upper and lower bounds on the type-II error exponent are proved, and a sufficient condition for which the bounds coincide is given. The results for sequential classification problems are then obtained accordingly. With the characterization of the optimal error exponents, the benefit of sequentiality is shown both analytically and numerically by comparing the sequential and the fixed-length cases in representative examples of type-I exponent constraint $lambda $ .
在二元假设检验问题中，众所周知，取样的顺序性消除了两个误差指数之间的权衡，然而实施最优检验需要了解基本分布，例如 $P_{0}$ 和 $P_{1}$。在这种情况下，分布知识被从各自分布中观察到的经验统计数据所取代，当受到所有可能的 $P_{0},P_{1}$ 的普遍性约束时，顺序性的收益就不那么为人所知了。 在本研究中，通过对普遍二元分类问题中顺序性的统一研究弥补了这一空白，其中普遍性约束被设置为预期停止时间和 I 类错误指数。对于所有 $P_{0},P_{1}$，I 型误差指数需要达到预先设定的依赖分布的约束 $lambda (P_{0},P_{1})$ 。在所提出的框架下，对不同的顺序设置进行了研究，以便与固定长度的对应问题进行公平的比较。通过将这些顺序分类问题视为一般顺序复合假设检验问题的特例，可以确定最佳 II 型误差指数的特征。具体地说，在受普遍性约束的一般顺序复合假设检验问题中，证明了类型-II 误差指数的上界和下界，并给出了边界重合的充分条件。然后相应地得到了顺序分类问题的结果。有了最优误差指数的表征，在 I 型指数约束 $lambda $ 的代表性例子中，通过比较顺序和定长情况，从分析和数值两方面说明了顺序性的好处。
{"title":"A Unified Study on Sequentiality in Universal Classification With Empirically Observed Statistics","authors":"Ching-Fang Li;I-Hsiang Wang","doi":"10.1109/TIT.2024.3525012","DOIUrl":"https://doi.org/10.1109/TIT.2024.3525012","url":null,"abstract":"In the binary hypothesis testing problem, it is well known that sequentiality in taking samples eradicates the trade-off between two error exponents, yet implementing the optimal test requires the knowledge of the underlying distributions, say <inline-formula> <tex-math>$P_{0}$ </tex-math></inline-formula> and <inline-formula> <tex-math>$P_{1}$ </tex-math></inline-formula>. In the scenario where the knowledge of distributions is replaced by empirically observed statistics from the respective distributions, the gain of sequentiality is less understood when subject to universality constraints over all possible <inline-formula> <tex-math>$P_{0},P_{1}$ </tex-math></inline-formula>. In this work, the gap is mended by a unified study on sequentiality in the universal binary classification problem, where the universality constraints are set on the expected stopping time as well as the type-I error exponent. The type-I error exponent is required to achieve a pre-set distribution-dependent constraint <inline-formula> <tex-math>$lambda (P_{0},P_{1})$ </tex-math></inline-formula> for all <inline-formula> <tex-math>$P_{0},P_{1}$ </tex-math></inline-formula>. Under the proposed framework, different sequential setups are investigated so that fair comparisons can be made with the fixed-length counterpart. By viewing these sequential classification problems as special cases of a general sequential composite hypothesis testing problem, the optimal type-II error exponents are characterized. Specifically, in the general sequential composite hypothesis testing problem subject to universality constraints, upper and lower bounds on the type-II error exponent are proved, and a sufficient condition for which the bounds coincide is given. The results for sequential classification problems are then obtained accordingly. With the characterization of the optimal error exponents, the benefit of sequentiality is shown both analytically and numerically by comparing the sequential and the fixed-length cases in representative examples of type-I exponent constraint <inline-formula> <tex-math>$lambda $ </tex-math></inline-formula>.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 3","pages":"1546-1569"},"PeriodicalIF":2.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/TIT.2025.3525666
Guanhua Fang;Ping Li;Gennady Samorodnitsky
We study an important variant of the stochastic multi-armed bandit (MAB) problem, which takes penalization into consideration. Instead of directly maximizing cumulative expected reward, we need to balance between the total reward and fairness level. In this paper, we present some new insights into MAB and formulate the problem in the penalization framework, where a rigorous penalized regret can be well defined and a more sophisticated regret analysis is possible. Under such a framework, we propose a hard-threshold UCB-like algorithm, which enjoys many merits including the asymptotic fairness, nearly optimal regret, good tradeoff between reward and fairness. Both gap-dependent and gap-independent regret bounds have been established. Multiple insightful comments are given to illustrate the soundness of our theoretical analysis. Numerous experimental results corroborate the theory and show the usefulness of our formulation of the problem and our method to solve it.
{"title":"On Penalization in Stochastic Multi-Armed Bandits","authors":"Guanhua Fang;Ping Li;Gennady Samorodnitsky","doi":"10.1109/TIT.2025.3525666","DOIUrl":"https://doi.org/10.1109/TIT.2025.3525666","url":null,"abstract":"We study an important variant of the stochastic multi-armed bandit (MAB) problem, which takes penalization into consideration. Instead of directly maximizing cumulative expected reward, we need to balance between the total reward and fairness level. In this paper, we present some new insights into MAB and formulate the problem in the penalization framework, where a rigorous penalized regret can be well defined and a more sophisticated regret analysis is possible. Under such a framework, we propose a hard-threshold UCB-like algorithm, which enjoys many merits including the asymptotic fairness, nearly optimal regret, good tradeoff between reward and fairness. Both gap-dependent and gap-independent regret bounds have been established. Multiple insightful comments are given to illustrate the soundness of our theoretical analysis. Numerous experimental results corroborate the theory and show the usefulness of our formulation of the problem and our method to solve it.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 3","pages":"1909-1925"},"PeriodicalIF":2.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/TIT.2025.3525668
Hyojeong Choi;Hong-Yeop Song
In this paper, we propose a simple construction for binary $(n,k)$ linear codes using s-mark Golomb rulers. We prove that these codes are sequential-recovery locally repairable codes (LRCs) with availability 2, which can sequentially recover 5 erased symbols. We prove the necessary and sufficient condition for the proposed codes to be rate-optimal. We also prove the necessary and sufficient condition for the proposed codes to be dimension-optimal. Finally, we propose some variations of this constructions to obtain some 5-sequential recovery LRCs with availability 3. The proposed codes have higher rates and have more flexible choice for the lengths than other previously reported constructions.
{"title":"Optimal 5-Seq LRCs With Availability From Golomb Rulers","authors":"Hyojeong Choi;Hong-Yeop Song","doi":"10.1109/TIT.2025.3525668","DOIUrl":"https://doi.org/10.1109/TIT.2025.3525668","url":null,"abstract":"In this paper, we propose a simple construction for binary <inline-formula> <tex-math>$(n,k)$ </tex-math></inline-formula> linear codes using s-mark Golomb rulers. We prove that these codes are sequential-recovery locally repairable codes (LRCs) with availability 2, which can sequentially recover 5 erased symbols. We prove the necessary and sufficient condition for the proposed codes to be rate-optimal. We also prove the necessary and sufficient condition for the proposed codes to be dimension-optimal. Finally, we propose some variations of this constructions to obtain some 5-sequential recovery LRCs with availability 3. The proposed codes have higher rates and have more flexible choice for the lengths than other previously reported constructions.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 3","pages":"1689-1699"},"PeriodicalIF":2.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1109/TIT.2024.3520979
Xingyu Yan;Xinyu Zhang;Peng Zhao
To consider model uncertainty in global Fréchet regression and improve density response prediction, we propose a frequentist model averaging method. The weights are chosen by minimizing a cross-validation criterion based on Wasserstein distance. In the cases where all candidate models are misspecified, we prove that the corresponding model averaging estimator has asymptotic optimality, achieving the lowest possible Wasserstein distance. When there are correctly specified candidate models, we prove that our method asymptotically assigns all weights to the correctly specified models. Numerical results of extensive simulations and a real data analysis on intracerebral hemorrhage data strongly favour our method.
{"title":"Frequentist Model Averaging for Global Fréchet Regression","authors":"Xingyu Yan;Xinyu Zhang;Peng Zhao","doi":"10.1109/TIT.2024.3520979","DOIUrl":"https://doi.org/10.1109/TIT.2024.3520979","url":null,"abstract":"To consider model uncertainty in global Fréchet regression and improve density response prediction, we propose a frequentist model averaging method. The weights are chosen by minimizing a cross-validation criterion based on Wasserstein distance. In the cases where all candidate models are misspecified, we prove that the corresponding model averaging estimator has asymptotic optimality, achieving the lowest possible Wasserstein distance. When there are correctly specified candidate models, we prove that our method asymptotically assigns all weights to the correctly specified models. Numerical results of extensive simulations and a real data analysis on intracerebral hemorrhage data strongly favour our method.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 3","pages":"1994-2006"},"PeriodicalIF":2.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1109/TIT.2024.3522151
Cécile Bouette;Laura Luzzi;Ligong Wang
We study the fundamental limits of covert communications over general memoryless additive-noise channels. We assume that the legitimate receiver and the eavesdropper share the same channel and therefore see the same outputs. Under mild integrability assumptions, we find a general upper bound on the square-root scaling constant, which only involves the variance of the logarithm of the probability density function of the noise. Furthermore, we show that, under some additional assumptions, this upper bound is tight. We also provide upper bounds on the length of the secret key required to achieve the optimal scaling.
{"title":"Covert Communication Over Additive-Noise Channels","authors":"Cécile Bouette;Laura Luzzi;Ligong Wang","doi":"10.1109/TIT.2024.3522151","DOIUrl":"https://doi.org/10.1109/TIT.2024.3522151","url":null,"abstract":"We study the fundamental limits of covert communications over general memoryless additive-noise channels. We assume that the legitimate receiver and the eavesdropper share the same channel and therefore see the same outputs. Under mild integrability assumptions, we find a general upper bound on the square-root scaling constant, which only involves the variance of the logarithm of the probability density function of the noise. Furthermore, we show that, under some additional assumptions, this upper bound is tight. We also provide upper bounds on the length of the secret key required to achieve the optimal scaling.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 3","pages":"2157-2169"},"PeriodicalIF":2.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-27DOI: 10.1109/TIT.2024.3523775
Homa Nikbakht;Michèle Wigger;Shlomo Shamai;Jean-Marie Gorce;H. Vincent Poor
This paper proposes coding schemes and information-theoretic converse results for the transmission of heterogeneous delay-constrained traffic over interference networks with random user activity and random data arrivals. The heterogeneous delay-constrained traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent delay constraints. Even for the delay-tolerant traffic, the total number of cooperation rounds at transmitter and receiver sides is limited to D rounds. Each transmitter is assumed to be active with probability $rho in [{0,1}]$ , and we study two different models for traffic arrival, each model reflecting a different application type. In Model 1, each active transmitter sends a delay-tolerant message, and with probability $rho _{f} in [{0,1}]$ also transmits an additional delay-sensitive message; in Model 2, each active transmitter sends either a delay-sensitive message with probability $rho _{f}$ or a delay-tolerant message with probability $1- rho _{f}$ . For both models, we derive inner and outer bounds on the fundamental per-user multiplexing gain (MG) region of the symmetric Wyner network as well as inner bounds on the fundamental MG region of the hexagonal model. The per-user MG of an interference network describes the logarithmic growth of the largest average per-user rate that can be achieved over the network at high signal-to-noise ratios (SNR). Our inner and outer bounds on the per-user MG are generally close and coincide in special cases. They also show that when both transmitters and receivers can cooperate, then under Model 1, transmitting delay-sensitive messages hardly causes any penalty on the sum per-user MG, and under Model 2, operating at large delay-sensitive per-user MGs incurs no penalty on the delay-tolerant per-user MG and thus even increases the sum per-user MG. However, when only receivers can cooperate, the maximum delay-tolerant per-user MG that our bounds achieve at maximum delay-sensitive per-user MG is significantly decreased.
{"title":"Interference Networks With Random User Activity and Heterogeneous Delay Constraints","authors":"Homa Nikbakht;Michèle Wigger;Shlomo Shamai;Jean-Marie Gorce;H. Vincent Poor","doi":"10.1109/TIT.2024.3523775","DOIUrl":"https://doi.org/10.1109/TIT.2024.3523775","url":null,"abstract":"This paper proposes coding schemes and information-theoretic converse results for the transmission of heterogeneous delay-constrained traffic over interference networks with random user activity and random data arrivals. The heterogeneous delay-constrained traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent delay constraints. Even for the delay-tolerant traffic, the total number of cooperation rounds at transmitter and receiver sides is limited to D rounds. Each transmitter is assumed to be active with probability <inline-formula> <tex-math>$rho in [{0,1}]$ </tex-math></inline-formula>, and we study two different models for traffic arrival, each model reflecting a different application type. In Model 1, each active transmitter sends a delay-tolerant message, and with probability <inline-formula> <tex-math>$rho _{f} in [{0,1}]$ </tex-math></inline-formula> also transmits an additional delay-sensitive message; in Model 2, each active transmitter sends either a delay-sensitive message with probability <inline-formula> <tex-math>$rho _{f}$ </tex-math></inline-formula> or a delay-tolerant message with probability <inline-formula> <tex-math>$1- rho _{f}$ </tex-math></inline-formula>. For both models, we derive inner and outer bounds on the fundamental per-user multiplexing gain (MG) region of the symmetric Wyner network as well as inner bounds on the fundamental MG region of the hexagonal model. The per-user MG of an interference network describes the logarithmic growth of the largest average per-user rate that can be achieved over the network at high signal-to-noise ratios (SNR). Our inner and outer bounds on the per-user MG are generally close and coincide in special cases. They also show that when both transmitters and receivers can cooperate, then under Model 1, transmitting delay-sensitive messages hardly causes any penalty on the sum per-user MG, and under Model 2, operating at large delay-sensitive per-user MGs incurs no penalty on the delay-tolerant per-user MG and thus even increases the sum per-user MG. However, when only receivers can cooperate, the maximum delay-tolerant per-user MG that our bounds achieve at maximum delay-sensitive per-user MG is significantly decreased.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 2","pages":"1043-1076"},"PeriodicalIF":2.2,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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