Modulation and coding for linear Gaussian channels

Forney, G. David; Ungerboeck, G.

doi:10.1109/18.720542

Cited by 504 publications

(405 citation statements)

References 92 publications

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“…(25), since for the Gaussian additive noise holds J(0|R) = 1/N . A detailed review of AWGN channel capacity and its different approximations for different SNR regimes (including the high-noise approximation above) can be found in [37]. The conclusion that in the vanishing input-power limit the capacity of AWGN channel can be achieved by both discrete and N (0, P ) distributions is not so surprising in the light of some recent research on the AWGN channels [38].…”

Section: B Small Input Power Limitmentioning

confidence: 97%

Information capacity in the weak-signal approximation

Kostal

2010

Phys. Rev. E

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We derive an approximate expression for mutual information in a broad class of discrete-time stationary channels with continuous input, under the constraint of vanishing input amplitude or power. The approximation describes the input by its covariance matrix, while the channel properties are described by the Fisher information matrix. This separation of input and channel properties allows us to analyze the optimality conditions in a convenient way. We show that input correlations in memoryless channels do not affect channel capacity since their effect decreases fast with vanishing input amplitude or power. On the other hand, for channels with memory, properly matching the input covariances to the dependence structure of the noise may lead to almost noiseless information transfer, even for intermediate values of the noise correlations. Since many model systems described in mathematical neuroscience and biophysics operate in the high noise regime and weak-signal conditions, we believe, that the described results are of potential interest also to researchers in these areas.

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Section: B Small Input Power Limitmentioning

confidence: 97%

Information capacity in the weak-signal approximation

Kostal

2010

Phys. Rev. E

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“…In order to transmit at rates close to the coded modulation capacity, multi-level coding or non-binary codes are needed [4], [5]. Alternatively, in bit-interleaved coded modulation (BICM) binary codes are mapped with a binary mapping rule γ onto non-binary modulations [1], [2].…”

Section: Model and Assumptionsmentioning

confidence: 99%

Bit-Interleaved Coded Modulation in the Wideband Regime

Martinez

Fàbregas²,

Caire

2007

2007 IEEE International Symposium on Information Theory

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The wideband regime of bit-interleaved coded modulation (BICM) in Gaussian channels is studied.The Taylor expansion of the coded modulation capacity for generic signal constellations at low signal-tonoise ratio (SNR) is derived and used to determine the corresponding expansion for the BICM capacity.Simple formulas for the minimum energy per bit and the wideband slope are given. BICM is found to be suboptimal in the sense that its minimum energy per bit can be larger than the corresponding value for coded modulation schemes. The minimum energy per bit using standard Gray mapping on M -PAM or M 2 -QAM is given by a simple formula and shown to approach -0.34 dB as M increases. Using the low SNR expansion, a general trade-off between power and bandwidth in the wideband regime is used to show how a power loss can be traded off against a bandwidth gain.A. Martinez and F. Willems are with the

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“…In this paper we analyze a relatively simple transmission strategy for the Gaussian model based on hierarchical modulation [5]. The strategy allows for instantaneous and low complexity encoding and decoding.…”

Section: Transmission Schemementioning

confidence: 99%

“…Moreover, our scheme uses simple uncoded transmissions and is therefore suitable for implementation in a system that is constrained in detection complexity and transmission delay. We use a hierarchical type of modulation [5] to separate multiple information flows. Comparable approaches have been used in [6] on noiseless channels and in [7] on real-valued Gaussian channels.…”

Section: Introductionmentioning

confidence: 99%

Transmission Strategies for Gaussian Relay Networks Obtained from Deterministic Models

Schrammar

Skoglund

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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Abstract-A constant gap between the deterministic model of a class of a wireless relay network and its Gaussian model counterpart is derived. The method is constructive in the sense that a transmission solution in the deterministic model that obeys certain constraints can directly be translated into a transmission in the Gaussian model. We show that the rate in the Gaussian model is at most a constant gap below the rate in the deterministic model, and we derive an upper bound on this gap.

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Modulation and coding for linear Gaussian channels

Cited by 504 publications

References 92 publications

Information capacity in the weak-signal approximation

Information capacity in the weak-signal approximation

Bit-Interleaved Coded Modulation in the Wideband Regime

Transmission Strategies for Gaussian Relay Networks Obtained from Deterministic Models

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