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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 6 вересня 2023

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1

Sahebi, Aria G., and S. Sandeep Pradhan. "Multilevel Channel Polarization for Arbitrary Discrete Memoryless Channels." IEEE Transactions on Information Theory 59, no. 12 (December 2013): 7839–57. http://dx.doi.org/10.1109/tit.2013.2282611.

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2

Huang, Da Zu, Zhi Gang Chen, Xin Li, and Ying Guo. "Quantum Polarization Codes for Capacity-Achieving in Discrete Memoryless Quantum Channel." Applied Mechanics and Materials 44-47 (December 2010): 2978–82. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2978.

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Анотація:

Quantum channel combining and splitting, called quantum channel polarization, is suggested to design qubit sequences that achieve the symmetric capacity for any given discrete memoryless quantum channels. The polarized quantum channels can be well-conditioned for quantum channel codes, through which one need to send data at rate 1 by employing quantum channels with capacity near 1 and at rate 0 by employing the remaining quantum channels.

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3

Steiner, M. "Constructive codes for arbitrary discrete memoryless channels." IEEE Transactions on Information Theory 40, no. 3 (May 1994): 929–34. http://dx.doi.org/10.1109/18.335905.

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4

Dabirnia, Mehdi, A. Korhan Tanc, Shahrouz Sharifi, and Tolga M. Duman. "Code Design for Discrete Memoryless Interference Channels." IEEE Transactions on Communications 66, no. 8 (August 2018): 3368–80. http://dx.doi.org/10.1109/tcomm.2018.2817233.

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5

Kurkoski, Brian M., and Hideki Yagi. "Quantization of Binary-Input Discrete Memoryless Channels." IEEE Transactions on Information Theory 60, no. 8 (August 2014): 4544–52. http://dx.doi.org/10.1109/tit.2014.2327016.

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6

Sreekumar, Sreejith, and Deniz Gunduz. "Distributed Hypothesis Testing Over Discrete Memoryless Channels." IEEE Transactions on Information Theory 66, no. 4 (April 2020): 2044–66. http://dx.doi.org/10.1109/tit.2019.2953750.

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7

Piantanida, Pablo, Gerald Matz, and Pierre Duhamel. "Outage Behavior of Discrete Memoryless Channels Under Channel Estimation Errors." IEEE Transactions on Information Theory 55, no. 9 (September 2009): 4221–39. http://dx.doi.org/10.1109/tit.2009.2025574.

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8

Zhang, Qiaosheng, and Vincent Y. F. Tan. "Covert Identification Over Binary-Input Discrete Memoryless Channels." IEEE Transactions on Information Theory 67, no. 8 (August 2021): 5387–403. http://dx.doi.org/10.1109/tit.2021.3089245.

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9

Grant, A. J., B. Rimoldi, R. L. Urbanke, and P. A. Whiting. "Rate-splitting multiple access for discrete memoryless channels." IEEE Transactions on Information Theory 47, no. 3 (March 2001): 873–90. http://dx.doi.org/10.1109/18.915637.

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10

Telatar, I. E. "Zero-error list capacities of discrete memoryless channels." IEEE Transactions on Information Theory 43, no. 6 (1997): 1977–82. http://dx.doi.org/10.1109/18.641560.

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11

Timo, Roy, Badri N. Vellambi, Alex Grant, and Khoa D. Nguyen. "Tracking Unstable Autoregressive Sources Over Discrete Memoryless Channels." IEEE Transactions on Information Theory 65, no. 12 (December 2019): 8140–63. http://dx.doi.org/10.1109/tit.2019.2930252.

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12

OOHAMA, Yasutada. "On Two Strong Converse Theorems for Discrete Memoryless Channels." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E98.A, no. 12 (2015): 2471–75. http://dx.doi.org/10.1587/transfun.e98.a.2471.

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13

Abou-Faycal, I. C., M. D. Trott, and S. Shamai. "The capacity of discrete-time memoryless Rayleigh-fading channels." IEEE Transactions on Information Theory 47, no. 4 (May 2001): 1290–301. http://dx.doi.org/10.1109/18.923716.

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14

Chen, Po-Ning, Hsuan-Yin Lin, and Stefan M. Moser. "Optimal Ultrasmall Block-Codes for Binary Discrete Memoryless Channels." IEEE Transactions on Information Theory 59, no. 11 (November 2013): 7346–78. http://dx.doi.org/10.1109/tit.2013.2276893.

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15

Gulcu, Talha Cihad, Min Ye, and Alexander Barg. "Construction of Polar Codes for Arbitrary Discrete Memoryless Channels." IEEE Transactions on Information Theory 64, no. 1 (January 2018): 309–21. http://dx.doi.org/10.1109/tit.2017.2765663.

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16

Bell, Mark R. "On the commutativity of discrete memoryless channels in cascade." Journal of the Franklin Institute 330, no. 6 (November 1993): 1101–11. http://dx.doi.org/10.1016/0016-0032(93)90067-5.

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17

Skoglund, M. "On channel-constrained vector quantization and index assignment for discrete memoryless channels." IEEE Transactions on Information Theory 45, no. 7 (1999): 2615–22. http://dx.doi.org/10.1109/18.796416.

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18

Phamdo, N., and N. Farvardin. "Optimal detection of discrete Markov sources over discrete memoryless channels-applications to combined source-channel coding." IEEE Transactions on Information Theory 40, no. 1 (1994): 186–93. http://dx.doi.org/10.1109/18.272478.

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19

He, Xuan, Kui Cai, Wentu Song, and Zhen Mei. "Dynamic Programming for Sequential Deterministic Quantization of Discrete Memoryless Channels." IEEE Transactions on Communications 69, no. 6 (June 2021): 3638–51. http://dx.doi.org/10.1109/tcomm.2021.3062838.

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20

Rezaeian, M., and A. Grant. "Computation of Total Capacity for Discrete Memoryless Multiple-Access Channels." IEEE Transactions on Information Theory 50, no. 11 (November 2004): 2779–84. http://dx.doi.org/10.1109/tit.2004.836661.

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21

Frèche, Guillaume, Matthieu Bloch, and Michel Barret. "Polar Codes for Covert Communications over Asynchronous Discrete Memoryless Channels." Entropy 20, no. 1 (December 22, 2017): 3. http://dx.doi.org/10.3390/e20010003.

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22

Jiang, Jinhua, Yan Xin, and H. Vincent Poor. "Achievable rates for discrete memoryless relay channels with generalised feedback." Transactions on Emerging Telecommunications Technologies 24, no. 2 (December 18, 2012): 212–31. http://dx.doi.org/10.1002/ett.2596.

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23

Dabirnia, Mehdi, Alfonso Martinez, and Albert Guillen i Fabregas. "A Recursive Quantizer Design Algorithm for Binary-Input Discrete Memoryless Channels." IEEE Transactions on Communications 69, no. 8 (August 2021): 5069–78. http://dx.doi.org/10.1109/tcomm.2021.3076174.

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24

Bennatan, A., and D. Burshtein. "On the Application of LDPC Codes to Arbitrary Discrete-Memoryless Channels." IEEE Transactions on Information Theory 50, no. 3 (March 2004): 417–38. http://dx.doi.org/10.1109/tit.2004.824917.

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25

Hamada, M. "Information Rates Achievable With Algebraic Codes on Quantum Discrete Memoryless Channels." IEEE Transactions on Information Theory 51, no. 12 (December 2005): 4263–77. http://dx.doi.org/10.1109/tit.2005.860824.

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26

Venkatesan, S., and V. Anantharam. "The common randomness capacity of a network of discrete memoryless channels." IEEE Transactions on Information Theory 46, no. 2 (March 2000): 367–87. http://dx.doi.org/10.1109/18.825797.

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27

Zhang, Yuan, and Cihan Tepedelenlioglu. "Analytical and Numerical Characterizations of Shannon Ordering for Discrete Memoryless Channels." IEEE Transactions on Information Theory 60, no. 1 (January 2014): 72–83. http://dx.doi.org/10.1109/tit.2013.2284771.

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28

Merhav, Neri. "Exponential Error Bounds on Parameter Modulation–Estimation for Discrete Memoryless Channels." IEEE Transactions on Information Theory 60, no. 2 (February 2014): 832–41. http://dx.doi.org/10.1109/tit.2013.2290119.

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29

Gu, Yujie, and Ofer Shayevitz. "On the Non-Adaptive Zero-Error Capacity of the Discrete Memoryless Two-Way Channel." Entropy 23, no. 11 (November 15, 2021): 1518. http://dx.doi.org/10.3390/e23111518.

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Анотація:

We study the problem of communicating over a discrete memoryless two-way channel using non-adaptive schemes, under a zero probability of error criterion. We derive single-letter inner and outer bounds for the zero-error capacity region, based on random coding, linear programming, linear codes, and the asymptotic spectrum of graphs. Among others, we provide a single-letter outer bound based on a combination of Shannon’s vanishing-error capacity region and a two-way analogue of the linear programming bound for point-to-point channels, which, in contrast to the one-way case, is generally better than both. Moreover, we establish an outer bound for the zero-error capacity region of a two-way channel via the asymptotic spectrum of graphs, and show that this bound can be achieved in certain cases.

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30

Bennatan, A., and D. Burshtein. "Design and analysis of nonbinary LDPC codes for arbitrary discrete-memoryless channels." IEEE Transactions on Information Theory 52, no. 2 (February 2006): 549–83. http://dx.doi.org/10.1109/tit.2005.862080.

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31

Venkatesan, S., and V. Anantharam. "The common randomness capacity of a pair of independent discrete memoryless channels." IEEE Transactions on Information Theory 44, no. 1 (1998): 215–24. http://dx.doi.org/10.1109/18.651022.

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32

Zhang, Zhonghao, Peng Zhangyou, Wang Jingran, and Liu Yang. "Threshold Saturation for Nonbinary SC-LDPC Ensembles on Discrete-Input Memoryless Channels." IEEE Communications Letters 20, no. 11 (November 2016): 2149–52. http://dx.doi.org/10.1109/lcomm.2016.2600662.

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33

Liu, Ruoheng, Ivana Maric, Predrag Spasojevic, and Roy D. Yates. "Discrete Memoryless Interference and Broadcast Channels With Confidential Messages: Secrecy Rate Regions." IEEE Transactions on Information Theory 54, no. 6 (June 2008): 2493–507. http://dx.doi.org/10.1109/tit.2008.921879.

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34

Brijpaul, K., and Bhu Dev Sharma. "An iterative method for computing the performance of discrete memoryless communication channels." Information Sciences 61, no. 1-2 (April 1992): 163–78. http://dx.doi.org/10.1016/0020-0255(92)90038-a.

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35

Granada, Imanol, Pedro Crespo, and Javier Garcia-Frías. "Combining the Burrows-Wheeler Transform and RCM-LDGM Codes for the Transmission of Sources with Memory at High Spectral Efficiencies." Entropy 21, no. 4 (April 8, 2019): 378. http://dx.doi.org/10.3390/e21040378.

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Анотація:

In this paper, we look at the problem of implementing high-throughput Joint Source- Channel (JSC) coding schemes for the transmission of binary sources with memory over AWGN channels. The sources are modeled either by a Markov chain (MC) or a hidden Markov model (HMM). We propose a coding scheme based on the Burrows-Wheeler Transform (BWT) and the parallel concatenation of Rate-Compatible Modulation and Low-Density Generator Matrix (RCM-LDGM) codes. The proposed scheme uses the BWT to convert the original source with memory into a set of independent non-uniform Discrete Memoryless (DMS) binary sources, which are then separately encoded, with optimal rates, using RCM-LDGM codes.

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36

Shahi, Sara, Daniela Tuninetti, and Natasha Devroye. "The Strongly Asynchronous Massive Access Channel." Entropy 25, no. 1 (December 29, 2022): 65. http://dx.doi.org/10.3390/e25010065.

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Анотація:

This paper considers the Strongly Asynchronous, Slotted, Discrete Memoryless, Massive Access Channel (SAS-DM-MAC) in which the number of users, the number of messages, and the asynchronous window length grow exponentially with the coding blocklength with their respective exponents. A joint probability of error is enforced, ensuring that all the users’ identities and messages are correctly identified and decoded. Achievability bounds are derived for the case that different users have similar channels, the case that users’ channels can be chosen from a set which has polynomially many elements in the blocklength, and the case with no restriction on the users’ channels. A general converse bound on the capacity region and a converse bound on the maximum growth rate of the number of users are derived. It is shown that reliable transmission with an exponential number of users with an exponential asynchronous exponent with joint error probability is possible at strictly positive rates.

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37

Veugen, T. "A simple class of capacity-achieving strategies for discrete memoryless channels with feedback." IEEE Transactions on Information Theory 42, no. 6 (1996): 2221–28. http://dx.doi.org/10.1109/18.556610.

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38

Uyematsu, T., and E. Okamoto. "A construction of codes with exponential error bounds on arbitrary discrete memoryless channels." IEEE Transactions on Information Theory 43, no. 3 (May 1997): 992–96. http://dx.doi.org/10.1109/18.568709.

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39

Xenoulis, Kostis, and Nicholas Kalouptsidis. "Refinement of the DS2 Bound and its Extensions for Discrete Memoryless Symmetric Channels." IEEE Communications Letters 18, no. 5 (May 2014): 861–64. http://dx.doi.org/10.1109/lcomm.2014.032014.132681.

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40

Li, Cheuk Ting, and Abbas El Gamal. "An Efficient Feedback Coding Scheme With Low Error Probability for Discrete Memoryless Channels." IEEE Transactions on Information Theory 61, no. 6 (June 2015): 2953–63. http://dx.doi.org/10.1109/tit.2015.2428234.

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41

Song, Xiaoxia, and Yong Li. "Compress-and-Forward for Relay Broadcast Channels without Common Messages." Cybernetics and Information Technologies 15, no. 2 (June 1, 2015): 97–110. http://dx.doi.org/10.1515/cait-2015-0031.

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Анотація:

Abstract This paper is connected with compress-and-forward strategy for two-user relay broadcast channels without common messages, where the relay node has private messages from the source, in addition to aiding traditional communication from the source to the destination. For this channel we derive two achievable rate regions based on the compress-and-forward strategy in cases of discrete memoryless channels and Gaussian channels, respectively. The numerical results for Gaussian relay broadcast channel show that the inner bound based on the compress-and-forward strategy improves when all the messages without peeling off any components are compressed and sent to the receiver. It also verifies that the inner bound based on compress-and-forward strategy is better than that based on decode-and-forward strategy, when the relay node is near to the sink node. Moreover, the rate region of the broadcast channel improves considerably when the collaboration between the two receivers is allowed. So the relay node can provide residual resources to help the communication between the source and the sink after its communication rate is satisfied, which gives some insights to select an available relay node in a practical communication system.

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42

Sigtermans, David. "A Path-Based Partial Information Decomposition." Entropy 22, no. 9 (August 29, 2020): 952. http://dx.doi.org/10.3390/e22090952.

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Анотація:

Based on the conceptual basis of information theory, we propose a novel mutual information measure—‘path-based mutual information’. This information measure results from the representation of a set of random variables as a probabilistic graphical model. The edges in this graph are modeled as discrete memoryless communication channels, that is, the underlying data is ergodic, stationary, and the Markov condition is assumed to be applicable. The associated multilinear stochastic maps, tensors, transform source probability mass functions into destination probability mass functions. This allows for an exact expression of the resulting tensor of a cascade of discrete memoryless communication channels in terms of the tensors of the constituting communication channels in the paths. The resulting path-based information measure gives rise to intuitive, non-negative, and additive path-based information components—redundant, unique, and synergistic information—as proposed by Williams and Beer. The path-based redundancy satisfies the axioms postulated by Williams and Beer, the identity axiom postulated by Harder, and the left monotonicity axiom postulated Bertschinger. The ordering relations between redundancies of different joint collections of sources, as captured in the redundancy lattices of Williams and Beer, follow from the data processing inequality. Although negative information components can arise, we speculate that these either result from unobserved variables, or from adding additional sources that are statistically independent from all other sources to a system containing only non-negative information components. This path-based approach illustrates that information theory provides the concepts and measures for a partial information decomposition.

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43

Wang, Bin, Jun Deng, Yanjing Sun, Wangmei Guo, and Guiguo Feng. "Secrecy Capacity of a Class of Erasure Wiretap Channels in WBAN." Sensors 18, no. 12 (November 26, 2018): 4135. http://dx.doi.org/10.3390/s18124135.

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Анотація:

In wireless body area networks (WBANs), the secrecy of personal health information is vulnerable to attacks due to the openness of wireless communication. In this paper, we study the security problem of WBANs, where there exists an attacker or eavesdropper who is able to observe data from part of sensors. The legitimate communication within the WBAN is modeled as a discrete memoryless channel (DMC) by establishing the secrecy capacity of a class of finite state Markov erasure wiretap channels. Meanwhile, the tapping of the eavesdropper is modeled as a finite-state Markov erasure channel (FSMEC). A pair of encoder and decoder are devised to make the eavesdropper have no knowledge of the source message, and enable the receiver to recover the source message with a small decoding error. It is proved that the secrecy capacity can be achieved by migrating the coding scheme for wiretap channel II with the noisy main channel. This method provides a new idea solving the secure problem of the internet of things (IoT).

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44

Yin, Xinxing, Zhi Xue, and Bin Dai. "Capacity-Equivocation Regions of the DMBCs with Noiseless Feedback." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/102069.

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Анотація:

The discrete memoryless broadcast channels (DMBCs) with noiseless feedback are studied. The entire capacity-equivocation regions of two models of the DMBCs with noiseless feedback are obtained. One is the degraded DMBCs with rate-limited feedback; the other is thelessandreversely less noisyDMBCs with causal feedback. In both models, two kinds of messages are transmitted. The common message is to be decoded by both the legitimate receiver and the eavesdropper, while the confidential message is only for the legitimate receiver. Our results generalize the secrecy capacity of the degraded wiretap channel with rate-limited feedback (Ardestanizadeh et al., 2009) and the restricted wiretap channel with noiseless feedback (Dai et al., 2012). Furthermore, we use a simpler and more intuitive deduction to get the single-letter characterization of the capacity-equivocation region, instead of relying on the recursive argument which is complex and not intuitive.

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45

Tomamichel, Marco, and Vincent Y. F. Tan. "A Tight Upper Bound for the Third-Order Asymptotics for Most Discrete Memoryless Channels." IEEE Transactions on Information Theory 59, no. 11 (November 2013): 7041–51. http://dx.doi.org/10.1109/tit.2013.2276077.

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46

Zhu, Fangfang, and Biao Chen. "Capacity Bounds and Sum Rate Capacities of a Class of Discrete Memoryless Interference Channels." IEEE Transactions on Information Theory 60, no. 7 (July 2014): 3763–72. http://dx.doi.org/10.1109/tit.2014.2322872.

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47

Scarlett, Jonathan, Anelia Somekh-Baruch, Alfonso Martinez, and Albert Guillen i Fabregas. "A Counter-Example to the Mismatched Decoding Converse for Binary-Input Discrete Memoryless Channels." IEEE Transactions on Information Theory 61, no. 10 (October 2015): 5387–95. http://dx.doi.org/10.1109/tit.2015.2468719.

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48

Kadampot, Ishaque Ashar, Mehrdad Tahmasbi, and Matthieu R. Bloch. "Multilevel-Coded Pulse-Position Modulation for Covert Communications Over Binary-Input Discrete Memoryless Channels." IEEE Transactions on Information Theory 66, no. 10 (October 2020): 6001–23. http://dx.doi.org/10.1109/tit.2020.3019996.

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49

Mercier, Hugues, Vahid Tarokh, and Fabrice Labeau. "Bounds on the Capacity of Discrete Memoryless Channels Corrupted by Synchronization and Substitution Errors." IEEE Transactions on Information Theory 58, no. 7 (July 2012): 4306–30. http://dx.doi.org/10.1109/tit.2012.2191682.

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50

Chang, Chain-I., Simon C. Fan, and Lee D. Davisson. "On numerical methods of calculating the capacity of continuous-input discrete-output memoryless channels." Information and Computation 86, no. 1 (May 1990): 1–13. http://dx.doi.org/10.1016/0890-5401(90)90022-a.

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