On the Excess Distortion Exponent for Memoryless Gaussian Source-Channel Pairs

Zhong, Yangfan; Alajaji, Fady; Campbell, L. L.

doi:10.1109/isit.2006.261928

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…The study of such lattices is currently under research. Exact comparison of schemes in high dimension will involve studying the achieved joint source-channel excess distortion exponent (see [31] for a recent work about this exponent in the Gaussian setting).…”

Section: Discussion: Delay and Complexitymentioning

confidence: 99%

Joint Wyner–Ziv/Dirty-Paper Coding by Modulo-Lattice Modulation

Kochman

Zamir

2009

IEEE Trans. Inform. Theory

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The combination of source coding with decoder side information (the Wyner-Ziv problem) and channel coding with encoder side information (the Gel'fand-Pinsker problem) can be optimally solved using the separation principle. In this work, we show an alternative scheme for the quadratic-Gaussian case, which merges source and channel coding. This scheme achieves the optimal performance by applying a modulo-lattice modulation to the analog source. Thus, it saves the complexity of quantization and channel decoding, and remains with the task of "shaping" only. Furthermore, for high signal-to-noise ratio (SNR), the scheme approaches the optimal performance using an SNR-independent encoder, thus it proves for this special case the feasibility of universal joint source-channel coding.Index Terms-Analog transmission, broadcast channel, joint source/channel coding, minimum mean-squared error (MMSE) estimation, modulo lattice modulation, unknown signal-to-noise ratio (SNR), writing on dirty paper, Wyner-Ziv (WZ) problem. I. INTRODUCTIONC ONSIDER the quadratic-Gaussian joint source/channel coding problem for the Wyner-Ziv (WZ) source [1] and Gel'fand-Pinsker channel [2], as depicted in Fig. 1. In the Wyner-Ziv setup, the source is jointly distributed with some side information (SI) known at the decoder. In the Gaussian case, the WZ-source sequence is given by(1)where the unknown source part, , is Gaussian independent and identically distributed (i.i.d.) with variance , while is an arbitrary SI sequence known at the decoder. In the Gel'fand-Pinsker setup, the channel transition distribution depends on a state that serves as encoder SI. In the Gaussian case, known as the dirty-paper channel (DPC) [3], the DPC output, , is given by (2)

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Section: Discussion: Delay and Complexitymentioning

confidence: 99%

Joint Wyner–Ziv/Dirty-Paper Coding by Modulo-Lattice Modulation

Kochman

Zamir

2009

IEEE Trans. Inform. Theory

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“…The study of such lattices is currently under research. Exact comparison of schemes in high dimension will involve studying the achieved joint source/channel excess distortion exponent (see [30] for a recent work about this exponent in the Gaussian setting).…”

Section: Discussion: Delay and Complexitymentioning

confidence: 99%

Joint Wyner-Ziv/Dirty Paper coding by modulo-lattice modulation

Kochman,

Zamir

2008

Preprint

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The combination of source coding with decoder side-information (Wyner-Ziv problem) and channel coding with encoder side-information (Gel'fand-Pinsker problem) can be optimally solved using the separation principle. In this work we show an alternative scheme for the quadratic-Gaussian case, which merges source and channel coding. This scheme achieves the optimal performance by a applying modulo-lattice modulation to the analog source. Thus it saves the complexity of quantization and channel decoding, and remains with the task of "shaping" only. Furthermore, for high signal-to-noise ratio (SNR), the scheme approaches the optimal performance using an SNR-independent encoder, thus it is robust to unknown SNR at the encoder.

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“…VI. A] and [23], a continuous-alphabet analog to the -typical class was studied for the MGS (referred to as Gaussian type classes in [1] and [23]). Given and , define the Gaussian -typical set by where is viewed as a column vector and denotes transposition.…”

Section: Memoryless Gaussian Systemsmentioning

confidence: 99%

“…Applying Lemma 3 to , and noting that (defined in Lemma 3) is increasing in , we can upper-bound the first term of (43) as (44) where is independent of . Similar to (23), by applying Lemmas 3 and 4 and (42), we can bound the second term of (43) as which vanishes double-exponentially. Just as in (28), by applying Lemmas 4 and 5, we can bound the third term of (43) By the weak law of large numbers, the probabilities in (57) converge to zero, and we can make the above bound arbitrarily small by choosing small enough and large enough.…”

Section: Probability Of Error Analysis: Letmentioning

confidence: 99%

Random-Coding Lower Bounds for the Error Exponent of Joint Quantization and Watermarking Systems

Zhong

Alajaji

Linder

2009

IEEE Trans. Inform. Theory

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Abstract-We establish random-coding lower bounds to the error exponent of discrete and Gaussian joint quantization and private watermarking systems. In the discrete system, both the covertext and the attack channel are memoryless and have finite alphabets. In the Gaussian system, the covertext is memoryless Gaussian and the attack channel has additive memoryless Gaussian noise. In both cases, our bounds on the error exponent are positive in the interior of the achievable quantization and watermarking rate region.Index Terms-Capacity region, error exponent, Gaussian-type class, information hiding, joint quantization and watermarking, private watermarking, random-coding lower bound.

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On the Excess Distortion Exponent for Memoryless Gaussian Source-Channel Pairs

Cited by 5 publications

References 11 publications

Joint Wyner–Ziv/Dirty-Paper Coding by Modulo-Lattice Modulation

Joint Wyner–Ziv/Dirty-Paper Coding by Modulo-Lattice Modulation

Joint Wyner-Ziv/Dirty Paper coding by modulo-lattice modulation

Random-Coding Lower Bounds for the Error Exponent of Joint Quantization and Watermarking Systems

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