Asynchronous capacity per unit cost

Chandar, Venkat; Tchamkerten, Aslan; Tse, David

doi:10.1109/isit.2010.5513323

Cited by 23 publications

(58 citation statements)

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“…Surprisingly, full output sampling is not necessary. In [1] it is shown that capacity per unit cost and minimum typical delay, established in [7] under full sampling, are (asymptotically) unaffected by a sparse output sampling as long as the sampling rate ρ is non-zero. SEQUENTIAL ANALYSIS: Decoding in the above communication setup can be cast into a change-detection sequential analysis framework.…”

Section: Related Workmentioning

confidence: 99%

“…When / ∈ X, k(x) > 0 for any x ∈ X. The other cases-investigated in [7] under full sampling-are either trivial (when X contains two or more zero cost symbols) or arguably unnatural (X contains a zero cost symbol that differs from or when ∈ X and X contains only nonzero cost symbols) and are omitted in this paper.…”

Section: )mentioning

confidence: 99%

“…. , 2 B }, carry information and are randomly generated in a quasi constant composition fashion according to an arbitrary (rate compatible) distribution in P(R) (the procedure is the same as for the codebook generation described in [7,Theorem 1]). …”

Section: Theorem 3 Proof Ideasmentioning

confidence: 99%

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Asynchronous capacity per unit cost under a receiver sampling constraint

Chandar

Tchamkerten

2015

2015 IEEE International Symposium on Information Theory (ISIT)

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In a recently proposed asynchronous communication setup, the receiver observes mostly pure background noise except for a brief and a priori unknown period of time when data is transmitted. Capacity per unit cost and minimum communication delay were characterized and shown to be unaffected by a sparse sampling at the receiver as long as the number of samples represents a constant fraction of the total channel outputs.This paper strengthens this result and shows that it continues to hold even if the sampling rate tends to zero. More specifically, if the sampling rate is ω(1/B), where B denotes the number of transmitted message bits, capacity per unit cost and minimum decoding delay are the same as under full output sampling. Conversely, if the sampling rate decreases as o(1/B), reliable communication is impossible.The main ingredient in the achievability argument is a time series sampling/detection procedure that detects transient changes in distribution. This procedure has the property of simultaneously minimizing detection delay and sampling rate, in the regime of vanishing false-alarm probability.

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Section: Related Workmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

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Asynchronous capacity per unit cost under a receiver sampling constraint

Chandar

Tchamkerten

2015

2015 IEEE International Symposium on Information Theory (ISIT)

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“…4 Note that there is a small abuse of notation as need not be a probability. 5 Note that should be interpreted as .…”

Section: Definition 2 (Asynchronous Capacity): For Givenmentioning

confidence: 99%

“…For this metric, Chandar et al [5], [6] establish the capacity per unit cost for the aforementioned bursty communication setup.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Communication: Capacity Bounds and Suboptimality of Training

Tchamkerten

Chandar

Wornell

2013

IEEE Trans. Inform. Theory

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Several aspects of the problem of asynchronous point-to-point communication without feedback are developed when the source is highly intermittent. In the system model of interest, the codeword is transmitted at a random time within a prescribed window whose length corresponds to the level of asynchronism between the transmitter and the receiver. The decoder operates sequentially and communication rate is defined as the ratio between the message size and the elapsed time between when transmission commences and when the decoder makes a decision. For such systems, general upper and lower bounds on capacity as a function of the level of asynchronism are established, and are shown to coincide in some nontrivial cases. From these bounds, several properties of this asynchronous capacity are derived. In addition, the performance of training-based schemes is investigated. It is shown that such schemes, which implement synchronization and information transmission on separate degrees of freedom in the encoding, cannot achieve the asynchronous capacity in general, and that the penalty is particularly significant in the high-rate regime.

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Energy‐efficient WirelessOFDMANetworks

Vannithamby

Talwar

2016

Towards 5G

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Asynchronous capacity per unit cost

Cited by 23 publications

References 4 publications

Asynchronous capacity per unit cost under a receiver sampling constraint

Asynchronous capacity per unit cost under a receiver sampling constraint

Asynchronous Communication: Capacity Bounds and Suboptimality of Training

Energy‐efficient WirelessOFDMANetworks

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