Results 1  10
of
32
Determinant maximization with linear matrix inequality constraints
 SIAM Journal on Matrix Analysis and Applications
, 1998
"... constraints ..."
The capacity of channels with feedback
 IEEE Trans. Information Theory
, 2009
"... We introduce a general framework for treating channels with memory and feedback. First, we generalize Massey’s concept of directed information [23] and use it to characterize the feedback capacity of general channels. Second, we present coding results for Markov channels. This requires determining a ..."
Abstract

Cited by 38 (2 self)
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We introduce a general framework for treating channels with memory and feedback. First, we generalize Massey’s concept of directed information [23] and use it to characterize the feedback capacity of general channels. Second, we present coding results for Markov channels. This requires determining appropriate sufficient statistics at the encoder and decoder. Third, a dynamic programming framework for computing the capacity of Markov channels is presented. Fourth, it is shown that the average cost optimality equation (ACOE) can be viewed as an implicit singleletter characterization of the capacity. Fifth, scenarios
Writing on colored paper
 in Proc. of ISIT
, 2001
"... A Gaussian channel, when corrupted by an additive Gaussian interfering signal whose complete sample sequence is known noncausally to the transmitter but not to the receiver, has the same capacity as if the interfering signal were not present. This is true even when the noise and interference are no ..."
Abstract

Cited by 24 (4 self)
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A Gaussian channel, when corrupted by an additive Gaussian interfering signal whose complete sample sequence is known noncausally to the transmitter but not to the receiver, has the same capacity as if the interfering signal were not present. This is true even when the noise and interference are not necessarily stationary or ergodic. 1
Finite State Channels with TimeInvariant Deterministic Feedback
"... We consider capacity of discretetime channels with feedback for the general case where the feedback is a timeinvariant deterministic function of the output samples. Under the assumption that the channel states take values in a finite alphabet, we find a sequence of achievable rates and a sequence ..."
Abstract

Cited by 23 (13 self)
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We consider capacity of discretetime channels with feedback for the general case where the feedback is a timeinvariant deterministic function of the output samples. Under the assumption that the channel states take values in a finite alphabet, we find a sequence of achievable rates and a sequence of upper bounds on the capacity. The achievable rates and the upper bounds are computable for any N, and the limits of the sequences exist. We show that when the probability of the initial state is positive for all the channelstates, then the capacity is the limit of the achievablerate sequence. We further show that when the channel is stationary, indecomposable and has no intersymbol interference (ISI), its capacity is given by the limit of the maximum of the (normalized) directed information between the input X N and the output Y N, i.e., 1 C = lim N→ ∞ N max I(XN → Y N), where the maximization is taken over the causal conditioning probability Q(x N z N−1) defined in this paper. The main idea for obtaining the results is to add causality into Gallager’s results [1] on finite state channels. The capacity results are used to show that the sourcechannel separation theorem holds for timeinvariant determinist feedback, and if the state of the channel is known both at the encoder and the decoder, then feedback does not increase capacity.
Feedback capacity of stationary Gaussian channels
"... The capacity of stationary additive Gaussian noise channels with feedback is characterized as the solution to a variational problem. Toward this end, it is proved that the optimal feedback coding scheme is stationary. When specialized to the firstorder autoregressive movingaverage noise spectrum, ..."
Abstract

Cited by 22 (4 self)
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The capacity of stationary additive Gaussian noise channels with feedback is characterized as the solution to a variational problem. Toward this end, it is proved that the optimal feedback coding scheme is stationary. When specialized to the firstorder autoregressive movingaverage noise spectrum, this variational characterization yields a closedform expression for the feedback capacity. In particular, this result shows that the celebrated Schalkwijk–Kailath coding scheme achieves the feedback capacity for the firstorder autoregressive movingaverage Gaussian channel, resolving a longstanding open problem studied by Butman, Schalkwijk– Tiernan, Wolfowitz, Ozarow, Ordentlich, Yang–Kavčić–Tatikonda, and others. 1 Introduction and
The feedback capacity of the firstorder moving average Gaussian channel. Accepted by
 IEEE Trans. Inform. Theory
, 2006
"... Abstract—Despite numerous bounds and partial results, the feedback capacity of the stationary nonwhite Gaussian additive noise channel has been open, even for the simplest cases such as the firstorder autoregressive Gaussian channel studied by Butman, Tiernan and Schalkwijk, Wolfowitz, Ozarow, and ..."
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Cited by 15 (2 self)
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Abstract—Despite numerous bounds and partial results, the feedback capacity of the stationary nonwhite Gaussian additive noise channel has been open, even for the simplest cases such as the firstorder autoregressive Gaussian channel studied by Butman, Tiernan and Schalkwijk, Wolfowitz, Ozarow, and more recently, Yang, Kavčić, and Tatikonda. Here we consider another simple special case of the stationary firstorder moving average additive Gaussian noise channel and find the feedback capacity in closed form. Specifically, the channel is given by = + =12... where the input satisfies a power constraint and the noise is a firstorder moving average Gaussian process defined by = 1 + 1 with white Gaussian innovations =0 1... We show that the feedback capacity of this channel is. We wish to communicate a message index reliably over the channel. The channel output is causally fed back to the transmitter. We specify a code with the codewords1 satisfying the expected power constraint The proband decoding function ability of error is defined by FB = log 0 where 0 is the unique positive root of the equation
A relationship between quantization and watermarking rates in the presence of Gaussian attacks, Institute for Systems Research technical
, 2001
"... Abstract—A system which embeds watermarks in ..."
Fountain Capacity
, 2006
"... Fountain codes have been successfully employed for reliable and efficient transmission of information via erasure channels with unknown erasure rates. This paper introduces the notion of fountain capacity for arbitrary channels, and shows that it is equal to the conventional Shannon capacity for st ..."
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Cited by 7 (1 self)
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Fountain codes have been successfully employed for reliable and efficient transmission of information via erasure channels with unknown erasure rates. This paper introduces the notion of fountain capacity for arbitrary channels, and shows that it is equal to the conventional Shannon capacity for stationary memoryless channels. In contrast, when the channel is not stationary or has memory, Shannon capacity and fountain capacity need not be equal.
The effect of asynchronism on the total capacity of Gaussian multipleaccess channels
 IEEE Trans. Inform. Theory
, 1992
"... AbstractThe degradation due to complete asynchronism (at the codeword and symbol levels) in the total capacity, maximum ratesum, of white Gaussian multipleaccess channels is investigated. It is shown that asynchronism reduces the total capacity of a Kuser channel by at most a factor of K. Moreo ..."
Abstract

Cited by 5 (0 self)
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AbstractThe degradation due to complete asynchronism (at the codeword and symbol levels) in the total capacity, maximum ratesum, of white Gaussian multipleaccess channels is investigated. It is shown that asynchronism reduces the total capacity of a Kuser channel by at most a factor of K. Moreover, this bound is achieved, in asymptotically high signaltonoise ratios, by the TDMA signalling strategy. When the signalling strategies are optimally designed to maximize the asynchronous total capacity under bandwidth constraints, we find that in a twouser channel 1) for a certain set of signaltonoise ratios there is no degradation due to asynchronism, 2) for any bandwidth and signaltonoise ratios the asynchronous total capacity is at least 88 % of the synchronous total capacity, and 3) asynchronism has a vanishing small effect on total capacity for both low and high signaltonoise ratios.
DIGITAL WATERMARKING, FINGERPRINTING AND COMPRESSION: An . . .
, 2002
"... The ease with which digital data can be duplicated and distributed over the media and the Internet has raised many concerns about copyright infringement. In many situations, multimedia data (e.g., images, music, movies, etc) are illegally circulated, thus violating intellectual property rights. In a ..."
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Cited by 5 (1 self)
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The ease with which digital data can be duplicated and distributed over the media and the Internet has raised many concerns about copyright infringement. In many situations, multimedia data (e.g., images, music, movies, etc) are illegally circulated, thus violating intellectual property rights. In an attempt to overcome this problem, watermarking has been suggested in the literature as the most effective means for copyright protection and authentication. Watermarking is the procedure whereby information (pertaining to owner and/or copyright) is embedded into host data, such that it is: (i) hidden, i.e., not perceptually visible; and (ii) recoverable, even after a (possibly malicious) degradation of the protected work. In this thesis, we prove some theoretical results that establish the fundamental limits of a general class of watermarking schemes. The main focus of this thesis is the problem of joint watermarking and compression of images, which can be briefly described as follows: due to bandwidth or storage constraints, a watermarked image is distributed in quantized form, using RQ bits per image dimension, and is subject to some additional degradation (possibly due to malicious attacks). The hidden message carries RW bits per