Achievable rates for intermittent communication

Khoshnevisan, Mostafa; Laneman, J. Nicholas

doi:10.1109/isit.2012.6283479

Cited by 6 publications

(13 citation statements)

References 3 publications

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“…Note that if the channel is information stable, then this value is equal to the capacity of the channel. Using the achievability result in [1] for this channel model, (R, α) is achievable if…”

Section: Uniform Insertion Model a Channel Modelmentioning

confidence: 99%

“…where the function g(k, n) is defined in (1). Note that the function φ(a, b) quantifies the loss in capacity due to the uncertainty about the positions of the insertions, and cannot be negative.…”

Section: B Upper Bounds On the Capacitymentioning

confidence: 99%

“…Intermittent communication is introduced in [1], which models non-continuous transmission of encoded symbols through the channel. For some practical applications transmitting a codeword can be intermittent due to lack of synchronization, shortage of transmission energy, or burstiness of the system.…”

Section: Introductionmentioning

confidence: 99%

“…The focus of [1] and [2] is on obtaining achievable rates for intermittent communication by introducing the notion of partial divergence. In this paper, we focus on obtaining upper bounds for the capacity of intermittent communication.…”

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Upper bounds on the capacity of binary intermittent communication

Khoshnevisan

Laneman

2013

2013 Information Theory and Applications Workshop (ITA)

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Abstract-This paper focuses on obtaining upper bounds on the capacity of a special case of intermittent communication, introduced in [1], in which the channel is binary-input binaryoutput noiseless with i.i.d. number of zeros inserted in between the binary codeword symbols. Upper bounds are obtained by providing the encoder and the decoder with various amounts of side-information, and calculating or upper bounding the capacity of this genie-aided system. The results suggest that the linear scaling of the receive window with respect to the codeword length considered in the system model is relevant since the upper bounds imply a tradeoff between the capacity of the channel and the intermittency rate.

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Section: Uniform Insertion Model a Channel Modelmentioning

confidence: 99%

Section: B Upper Bounds On the Capacitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…

…”

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confidence: 99%

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Upper bounds on the capacity of binary intermittent communication

Khoshnevisan

Laneman

2013

2013 Information Theory and Applications Workshop (ITA)

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Intermittent communication and partial divergence

Khoshnevisan

Laneman

2012

2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Abstract-We formulate a model for intermittent communication in which the transmissions of information symbols are bursty or the channel is sporadically available. We consider a general scenario in which information and noise symbols are grouped into packets of length l, and noise packets are inserted in the input sequence of the channel, and the receiver does not know a priori the positions of the information packets. Depending on the scaling behavior of the packet length relative to the codeword length, we identify some interesting scenarios for the scaling behavior of the receive window relative to the codeword length, and find achievable rates using different decoding structures. Interestingly, some of the decoding structures are based on a generalization of the method of types and properties of partial divergence. The achievable rates and the numerical results confirm the intuitive idea that increasing the intermittency decreases the achievable rates by increasing the uncertainty about the positions of the information packets at the receiver.

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Energy and sampling constrained asynchronous communication

Tchamkerten

Chandar

Caire

2013

2013 IEEE International Symposium on Information Theory

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The minimum energy, and, more generally, the minimum cost, to transmit one bit of information has been recently derived for bursty communication when information is available infrequently at random times at the transmitter. This result assumes that the receiver is always in the listening mode and samples all channel outputs until it makes a decision. If the receiver is constrained to sample only a fraction ρ ∈ (0, 1] of the channel outputs, what is the cost penalty due to sparse output sampling?Remarkably, there is no penalty: regardless of ρ > 0 the asynchronous capacity per unit cost is the same as under full sampling, i.e., when ρ = 1. Moreover, there is not even a penalty in terms of decoding delay-the elapsed time between when information is available until when it is decoded. This latter result relies on the possibility to sample adaptively; the next sample can be chosen as a function of past samples. Under non-adaptive sampling, it is possible to achieve the full sampling asynchronous capacity per unit cost, but the decoding delay gets multiplied by 1/ρ. Therefore adaptive sampling strategies are of particular interest in the very sparse sampling regime.

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Achievable rates for intermittent communication

Cited by 6 publications

References 3 publications

Upper bounds on the capacity of binary intermittent communication

Upper bounds on the capacity of binary intermittent communication

Intermittent communication and partial divergence

Energy and sampling constrained asynchronous communication

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