The Experts below are selected from a list of 11982 Experts worldwide ranked by ideXlab platform
Navin Kashyap - One of the best experts on this subject based on the ideXlab platform.
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the feedback capacity of the Binary erasure channel with a no consecutive ones input constraint
IEEE Transactions on Information Theory, 2016Co-Authors: Oron Sabag, Haim H Permuter, Navin KashyapAbstract:The input-constrained erasure channel with feedback is considered, where the Binary input sequence contains no consecutive ones, i.e., it satisfies the $(1,\infty )$ -RLL constraint. We derive the capacity for this setting, which can be expressed as $C_{\epsilon }=\max _{0\leq p\leq 0.5} \frac {(1-\epsilon )H_{b}(p)}{1+(1-\epsilon )p}$ , where $\epsilon $ is the erasure probability and $ H_{b}(\cdot )$ is the Binary Entropy function. Moreover, we prove that a priori knowledge of the erasure at the encoder does not increase the feedback capacity. The feedback capacity was calculated using an equivalent dynamic programming (DP) formulation with an optimal average-reward that is equal to the capacity. Furthermore, we obtained an optimal encoding procedure from the solution of the DP, leading to a capacity-achieving, zero-error coding scheme for our setting. DP is, thus, shown to be a tool not only for solving optimization problems, such as capacity calculation, but also for constructing optimal coding schemes. The derived capacity expression also serves as the only non-trivial upper bound known on the capacity of the input-constrained erasure channel without feedback, a problem that is still open.
Wojciech Szpankowski - One of the best experts on this subject based on the ideXlab platform.
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capacity of a structural Binary symmetric channel
International Symposium on Information Theory, 2013Co-Authors: Lan V Truong, Wojciech SzpankowskiAbstract:Information theory traditionally deals with the problem of transmitting sequences over a communication channel and finding the maximum number of messages that a transmitter can send so that the receiver recovers these messages with arbitrarily small probability of error. However, databases of various sorts have come into existence in recent years that require the transmission of new sources of data (e.g., graphs and sets) over communication channels. Here, we investigate a communication model transmitting Erdos-Renyi (unlabeled) graphs to a destination over a Binary Symmetric Channel (BSC). We find the capacity of such a channel - called the Structural Binary Symmetric Channel (SBSC) - to be C = 1 - h(e) where h(e) is the Binary Entropy of the error bit rate e.
Oron Sabag - One of the best experts on this subject based on the ideXlab platform.
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the feedback capacity of the Binary erasure channel with a no consecutive ones input constraint
IEEE Transactions on Information Theory, 2016Co-Authors: Oron Sabag, Haim H Permuter, Navin KashyapAbstract:The input-constrained erasure channel with feedback is considered, where the Binary input sequence contains no consecutive ones, i.e., it satisfies the $(1,\infty )$ -RLL constraint. We derive the capacity for this setting, which can be expressed as $C_{\epsilon }=\max _{0\leq p\leq 0.5} \frac {(1-\epsilon )H_{b}(p)}{1+(1-\epsilon )p}$ , where $\epsilon $ is the erasure probability and $ H_{b}(\cdot )$ is the Binary Entropy function. Moreover, we prove that a priori knowledge of the erasure at the encoder does not increase the feedback capacity. The feedback capacity was calculated using an equivalent dynamic programming (DP) formulation with an optimal average-reward that is equal to the capacity. Furthermore, we obtained an optimal encoding procedure from the solution of the DP, leading to a capacity-achieving, zero-error coding scheme for our setting. DP is, thus, shown to be a tool not only for solving optimization problems, such as capacity calculation, but also for constructing optimal coding schemes. The derived capacity expression also serves as the only non-trivial upper bound known on the capacity of the input-constrained erasure channel without feedback, a problem that is still open.
Tony Q S Quek - One of the best experts on this subject based on the ideXlab platform.
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capacity of energy harvesting Binary symmetric channels with a sigma rho power constraint
IEEE Transactions on Communications, 2018Co-Authors: Zhengchuan Chen, Guido Carlo Ferrante, Howard H Yang, Tony Q S QuekAbstract:Capacity of energy harvesting communications with deterministic energy arrival and finite battery size is investigated. An abstraction of the physical layer is considered, where Binary sequences are transmitted through a Binary symmetric channel, and a cost function is associated with the transmission of each symbol. Upper and lower bounds on the channel capacity are derived for the general case by studying the normalized exponent of the cardinality of the set of feasible input sequences. Several upper bounds on the exponent are proposed by studying supersets of the feasible set. Lower bounds are derived by applying the Binary Entropy-power inequality and by using specific signaling schemes based on a save-and-transmit strategy. Numerical results are presented for several values of the energy arrival rate and battery size, validating the usefulness of the capacity bounds established for the energy harvesting channels.
Lan V Truong - One of the best experts on this subject based on the ideXlab platform.
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capacity of a structural Binary symmetric channel
International Symposium on Information Theory, 2013Co-Authors: Lan V Truong, Wojciech SzpankowskiAbstract:Information theory traditionally deals with the problem of transmitting sequences over a communication channel and finding the maximum number of messages that a transmitter can send so that the receiver recovers these messages with arbitrarily small probability of error. However, databases of various sorts have come into existence in recent years that require the transmission of new sources of data (e.g., graphs and sets) over communication channels. Here, we investigate a communication model transmitting Erdos-Renyi (unlabeled) graphs to a destination over a Binary Symmetric Channel (BSC). We find the capacity of such a channel - called the Structural Binary Symmetric Channel (SBSC) - to be C = 1 - h(e) where h(e) is the Binary Entropy of the error bit rate e.