A two-node binary chief executive officer (ceo) problem is investigated. Noise-corrupted versions of a binary sequence are forwarded by two nodes to a single destination node over orthogonal additive white Gaussian no...
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A two-node binary chief executive officer (ceo) problem is investigated. Noise-corrupted versions of a binary sequence are forwarded by two nodes to a single destination node over orthogonal additive white Gaussian noise (AWGN) channels. We first reduce the binary ceo problem to a binary multiterminal source coding problem, of which an outer bound for the rate-distortion region is derived. The distortion function is then established by evaluating the relationship between the binaryceo and multiterminal source coding problems. A lower bound approximation on the Hamming distortion (HD) is obtained by minimizing a distortion function subject to constraints obtained based on the source-channel separation theorem. Encoding/decoding algorithms using concatenated convolutional codes and a joint decoding scheme are used to verify the lower bound on the HD. It is found that the theoretical lower bounds on the HD and the computer simulation-based bit error rate performance curves have the same tendencies. The differences in the threshold signal-to-noise ratio between the theoretical lower bounds and those obtained by simulations are around 1.5 dB in AWGN channel. The theoretical lower bound on the HD in block Rayleigh fading channel is also evaluated by performing Monte Carlo simulation.
Estimation of a binary source using multiple observers, a variant of the so called Chief Executive Officer (ceo) problem, is considered. A low-complexity Distributed Joint Source Channel Coding (D-JSCC) based on the P...
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Estimation of a binary source using multiple observers, a variant of the so called Chief Executive Officer (ceo) problem, is considered. A low-complexity Distributed Joint Source Channel Coding (D-JSCC) based on the Parallel Concatenated Convolutional Codes (PCCC) is implemented in a cluster of sensors in a distributed fashion. Convergence of the iterative decoder is analyzed by utilizing EXtrinsic Information Transfer (EXIT) chart technique to determine the convergence region in terms of the sensors observation accuracy and channel SNR, where the iterative decoder outperforms the non-iterative one. This leads to design of a bi-modal decoder that adaptively switches between the iterative and non-iterative modes in order to avoid inefficient iterative information exchange without compromising the resulting Bit Error Rate (BER). This adaptive decoding algorithm saves the computational power and decoding time by a factor of about 10 by avoiding unnecessary iterations.
In this paper, we propose an efficient coding scheme for the binary Chief Executive Officer (ceo) problem under logarithmic loss criterion. Courtade and Weissman obtained the exact rate-distortion bound for a two-link...
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In this paper, we propose an efficient coding scheme for the binary Chief Executive Officer (ceo) problem under logarithmic loss criterion. Courtade and Weissman obtained the exact rate-distortion bound for a two-link binary ceo problem under this criterion. We find optimal parameters of the binary symmetric test-channel model for the encoder of each link by using the given bound. Furthermore, an efficient coding scheme based on compound low-density generator matrix (LDGM)-low-density parity-check (LDPC) codes is presented to achieve the theoretical rates. In the proposed encoding scheme, a binary quantizer using LDGM codes and a syndrome generator using LDPC codes are applied. The proposed decoder employs a sum-product algorithm and a soft estimator to produce an approximate a posteriori distribution of the source bits given the data received through both links. Our numerical examples verify a close performance of the proposed coding scheme to the theoretical bound in several cases.
The L-link binary Chief Executive Officer (ceo) problem under logarithmic loss is investigated in this paper. A quantization splitting technique is applied to convert the problem under consideration to a (2L - 1)-step...
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The L-link binary Chief Executive Officer (ceo) problem under logarithmic loss is investigated in this paper. A quantization splitting technique is applied to convert the problem under consideration to a (2L - 1)-step successive Wyner-Ziv (WZ) problem, for which a practical coding scheme is proposed. In the proposed scheme, Low-Density Generator-Matrix (LDGM) codes are used for binary quantization while Low-Density Parity-Check (LDPC) codes are used for syndrome generation;the decoder performs successive decoding based on the received syndromes and produces a soft reconstruction of the remote source. The simulation results indicate that the rate-distortion performance of the proposed scheme can approach the theoretical inner bound based on binary-symmetric test-channel models.
In this paper, we propose an efficient coding scheme for the binary Chief Executive Officer (ceo) problem under logarithmic loss criterion. Courtade and Weissman obtained the exact rate-distortion bound for a two-link...
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In this paper, we propose an efficient coding scheme for the binary Chief Executive Officer (ceo) problem under logarithmic loss criterion. Courtade and Weissman obtained the exact rate-distortion bound for a two-link binary ceo problem under this criterion. We find optimal parameters of the binary symmetric test-channel model for the encoder of each link by using the given bound. Furthermore, an efficient coding scheme based on compound low-density generator matrix (LDGM)-low-density parity-check (LDPC) codes is presented to achieve the theoretical rates. In the proposed encoding scheme, a binary quantizer using LDGM codes and a syndrome generator using LDPC codes are applied. The proposed decoder employs a sum-product algorithm and a soft estimator to produce an approximate a posteriori distribution of the source bits given the data received through both links. Our numerical examples verify a close performance of the proposed coding scheme to the theoretical bound in several cases.
This paper focuses on one-helper assisted binary data gathering networks, for example, such as in Internet of Things, where a destination makes estimates of binary data relying on a number of agents and one helper. Du...
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This paper focuses on one-helper assisted binary data gathering networks, for example, such as in Internet of Things, where a destination makes estimates of binary data relying on a number of agents and one helper. Due to the noise, corrupting errors already exist in the agent observations. To analyze the performance of this system, we formulate this system as a binary chief executive officer (ceo) problem with a helper. Initially, we use a successive decoding scheme to decompose the binary ceo problem with a helper into the multiterminal source coding and final decision problems. Then, we present an outer bound on the rate-distortion region for multiterminal source coding with binary sources and a helper. After solving a convex optimization problem formulated from the derived outer bound, we obtain the final distortion by substituting the minimized distortions of observation into the distortion propagating function, which is derived to bridge the relationship between the joint decoding results and final decision. Finally, we analyze the trade-off of rate-distortion through theoretical calculation and simulations. Both the theoretical and simulation results demonstrate that a helper can obviously reduce the signal-to-noise ratio threshold. We also have an in-depth discussion on the differences of system performance improvement between locating a helper and including an additional agent.
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