Nearest neighbor decoding for additive non-Gaussian noise channels

Lapidoth, Amos

doi:10.1109/18.532892

Cited by 198 publications

(190 citation statements)

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“…We show that the dispersion term depends on the non-Gaussian noise only through its second and fourth moments, thus complementing the capacity result (Lapidoth, 1996), which depends only on the second moment. Furthermore, we characterize the second-order asymptotics of point-to-point codes over K-sender interference networks with non-Gaussian additive noise.…”

mentioning

confidence: 62%

“…Note that in traditional channel-coding analyses [2], [3], the probability of error is averaged only over W and Z n . Similar to [5], the additional averaging over the codebook C is required here to establish ensemble-tightness results for the two classes of Gaussian codebooks considered in this paper.…”

Section: Point-to-point Channels a System Model And Definitionsmentioning

confidence: 99%

“…Lapidoth [5] provided a "first-order-asymptotics" answer to this question by showing that the Gaussian capacity C(P ) is also the largest rate achievable over unit-variance nonGaussian stationary ergodic additive channels when i.i.d. or spherical Gaussian codebooks and NN decoding are used.…”

Section: Introductionmentioning

confidence: 99%

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The dispersion of nearest-neighbor decoding for additive non-Gaussian channels

Scarlett

Tan

Durisi

2016

2016 IEEE International Symposium on Information Theory (ISIT)

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Abstract-We study the second-order asymptotics of information transmission using random Gaussian codebooks and nearest neighbor (NN) decoding over a power-limited stationary memoryless additive non-Gaussian noise channel. We show that the dispersion term depends on the non-Gaussian noise only through its second and fourth moments, thus complementing the capacity result (Lapidoth, 1996), which depends only on the second moment. Furthermore, we characterize the second-order asymptotics of point-to-point codes over K-sender interference networks with non-Gaussian additive noise. Specifically, we assume that each user's codebook is Gaussian and that NN decoding is employed, i.e., that interference from the K − 1 unintended users (Gaussian interfering signals) is treated as noise at each decoder. We show that while the first-order term in the asymptotic expansion of the maximum number of messages depends on the power of the interferring codewords only through their sum, this does not hold for the second-order term.

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confidence: 62%

Section: Point-to-point Channels a System Model And Definitionsmentioning

confidence: 99%

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The dispersion of nearest-neighbor decoding for additive non-Gaussian channels

Scarlett

Tan

Durisi

2016

2016 IEEE International Symposium on Information Theory (ISIT)

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“…As shown in [2] and the references therein, the Gaussian distribution is the worst memoryless noise possible given a specified noise power. Lapidoth [10] further showed that irrespective of the noise distribution and even regardless of whether the noise is i.i.d., the capacity assuming i.i.d. Gaussian noise is achievable with nearest-neighbor decoding and no rate above the i.i.d.…”

Section: B Approximation Of Noise In Successive Decodingsmentioning

confidence: 99%

Incremental redundancy: A comparison of a sphere-packing analysis and convolutional codes

Chen

Seshadri

Wesel

2011

2011 Information Theory and Applications Workshop

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Abstract-Theoretical analysis has long indicated that feedback improves the error exponent but not the capacity of memoryless Gaussian channels. Chen et al. [1] demonstrated that a modified incremental redundancy scheme can use noiseless feedback to help short convolutional codes deliver the bit-error-rate performance of a long blocklength turbo code, but with much lower latency. This paper presents a code-independent analysis based on sphere-packing that approximates the throughput-vs.-latency achievable region possible with feedback and incremental redundancy for a specified AWGN SNR. Simulation results indicate that tail-biting convolutional codes employing feedback and incremental redundancy perform close to the sphere-packing approximation until the throughput reaches the limit of the system's ability to approach the channel capacity.

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“…codebooks, C bicm is also the largest rate that can be transmitted with vanishing error probability [17]. This capacity should be compared with the equivalent quantities on CM and MLC, given in Eqs.…”

Section: Achievable Rates With Bicmmentioning

confidence: 99%