Capacity bounds and power allocation for underwater acoustic relay channels with ISI

Choudhuri, Chiranjib; Mitra, Urbashi

doi:10.1145/1654130.1654136

Cited by 14 publications

(23 citation statements)

References 24 publications

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“…2, we investigate the effect of significant channel taps' locations on the capacity. We assume ten significant taps (i.e., m = 10) with uniform PDP and consider the following Γ vectors to indicate the location of significant taps: [1,2,3,4,5,6,7,8,9,10] • Γ2 = [1,2,3,4,5,6,7,8,11,20] • Γ3 = [1,2,3,4,5,6,11,20,47,128] • Γ4 = [1,15,29,43,57,71,85,99,113,127] In Γ 1 , the locations of significant taps are consecutive, and Γ 4 represents the case of equally spaced taps. It is observed from Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Although capacity calculations for terrestrial wireless radiofrequency channels have been extensively studied, the literature on the capacity of UWA channels is sporadic [2][3][4]. These works, either implicitly [2] or explicitly [4] consider an OFDM-based multi-carrier architecture and assume frequency-flat channel for each narrow sub-band.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, single-carrier systems over frequency-selective channels are subject to ISI which needs to be taken into account in the performance analysis. In [3], Choudhuri capacity bounds for the UWA relay channels with ISI and investigate optimum power allocation. They however consider some idealistic assumptions such as Thorp's formula [5] for the path loss which depends on only distance and frequency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Information theoretic analysis and optimization of underwater acoustic communication systems

Nouri

Uysal

2013

Eurocon 2013

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Due to copyright restrictions, the access to the full text of this article is only available via subscription.In this paper, we investigate the information theoretical limits of underwater acoustic channel with intersymbol interference. Under the assumptions of sparse and frequency-selective Rician fading channel and non-white correlated Gaussian ambient noise, we derive an expression for underwater channel capacity which we later use to optimize the carrier frequency and input signaling. We further demonstrate the effect of several system and environmental parameters such as distance, temperature, depth, etc. on the capacity.TÜBİTA

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Information theoretic analysis and optimization of underwater acoustic communication systems

Nouri

Uysal

2013

Eurocon 2013

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“…Proof: To prove this Lemma, we need to show that Eqn. (16) subject to the power constraints (2) and rate constraint (17) is maximized by a diagonal Ψ S and Ψ R . We prove this by showing that diagonal Ψ S and Ψ R maximizes the objective function Eqn.…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

Capacity Bounds for Relay Channels With Intersymbol Interference and Colored Gaussian Noise

Choudhuri

Mitra

2014

IEEE Trans. Inform. Theory

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The capacity of a relay channel with inter-symbol interference (ISI) and additive colored Gaussian noise is examined under an input power constraint. Prior results are used to show that the capacity of this channel can be computed by examining the circular degraded relay channel in the limit of infinite block length. The current work provides single letter expressions for the achievable rates with decodeand-forward (DF) and compress-and-forward (CF) processing employed at the relay. Additionally, the cut-set bound for the relay channel is generalized for the ISI/colored Gaussian noise scenario. All results hinge on showing the optimality of the decomposition of the relay channel with ISI/colored Gaussian noise into an equivalent collection of coupled parallel, scalar, memoryless relay channels. The region of optimality of the DF and CF achievable rates are also discussed. Optimal power allocation strategies are also discussed for the two lower bounds and the cut-set upper bound. As the maximizing power allocations for DF and CF appear to be intractable, the desired cost functions are modified and then optimized. The resulting rates are illustrated through the computation of numerical examples.Chiranjib Choudhuri (cchoudhu@usc.edu) and Urbashi Mitra (ubli@usc.edu) are with the Ming Hsieh

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“…So we use an equivalent channel model, circular relay channel model, defined in [8]. We take advantage of the fact that the lower bound for block Markov encoding (see [20]) of circular relay channel is the same as that of our original channel in the limit of infinite codeword length. Theorem 1.…”

Section: Achievable Ratementioning

confidence: 99%

On the capacity of the symbol-asynchronous relay channel

Choudhuri

Mitra

2009

2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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The capacity of the symbol-asynchronous single-relay channel is investigated. Symbol asynchronism implies that the codewords transmitted from the relay and the source do not coincide in time at the destination. Due to propagation or implementation effects (e.g. missynchronized clocks), symbol asynchronism occurs in many practical situations. In ad hoc scenarios, such as sensor networks, achieving perfect synchronism is challenging (due to the distributed nature of the network). Herein it is shown that the symbol-asynchronous single-relay channel can be modeled as a MIMO relay channel with memory. First, lower and upper bounds on the capacity are derived under the assumption that the receivers know the symbol epochs and the transmitters know their mutual offset. Next, the case where the transmitters only know that the mutual offsets that parameterize the channel belong to an uncertainty set and receivers have no knowledge of the symbol epochs is examined. Conditions for characterizing capacity (when the upper and lower bounds match) are also derived. Simple asynchronous coding schemes for the relay channel are also explored. The proposed coding scheme can be extended to the relay with memory. Bilayer LDPC codes are used to replicate the binning strategy of the relay and MLC coding with MSD decoding is used to counter the effect of memory.

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Capacity bounds and power allocation for underwater acoustic relay channels with ISI

Cited by 14 publications

References 24 publications

Information theoretic analysis and optimization of underwater acoustic communication systems

Information theoretic analysis and optimization of underwater acoustic communication systems

Capacity Bounds for Relay Channels With Intersymbol Interference and Colored Gaussian Noise

On the capacity of the symbol-asynchronous relay channel

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