This paper deals with a secret key agreement problem from correlated random numbers. It is proved that there is a pair of linear matrices that yields a secret key agreement in the situation wherein a sender, a legitim...
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This paper deals with a secret key agreement problem from correlated random numbers. It is proved that there is a pair of linear matrices that yields a secret key agreement in the situation wherein a sender, a legitimate receiver, and an eavesdropper have access to correlated random numbers. A relation between the coding problem of correlated sources and a secret key agreement problem from correlated random numbers are also discussed.
Motivated by potential applications in wireless sensor networks, we consider the problem of communicating a large number of correlated analog sources over a Gaussian multiple-access channel using non-orthogonal code-d...
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ISBN:
(纸本)9781479903566
Motivated by potential applications in wireless sensor networks, we consider the problem of communicating a large number of correlated analog sources over a Gaussian multiple-access channel using non-orthogonal code-division multiple-access (CDMA). We present a joint source-channel decoder which exploits the inter-source correlation for interference reduction in the CDMA channel. This decoder uses a linear minimum mean square error (MMSE) multi-user detector (MUD) in tandem with a MMSE joint source decoder for multiple sources to achieve a computational complexity that scales with the number of sources. However, iterative exchange of extrinsic information between the MUD and the joint source decoder leads to improved interference cancellation. Experimental results obtained with decoding observations from Gaussian random fields show that the proposed iterative decoder can achieve a considerable performance gain compared to a non-iterative decoder. The results also show that the iterative decoder is robust against the performance degradation due to correlated interference in a non-orthogonal CDMA channel.
We introduce a framework to study fundamental limits of sequential coding of Markov sources under an error propagation constraint. An encoder sequentially compresses a sequence of vector-sources that are spatially i.i...
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ISBN:
(纸本)9780769546568
We introduce a framework to study fundamental limits of sequential coding of Markov sources under an error propagation constraint. An encoder sequentially compresses a sequence of vector-sources that are spatially i.i.d. but temporally correlated according to a Markov process. The channel erases up to B packets in a single burst, but reveals all other packets to the destination. The destination is required to reproduce all the source-vectors instantaneously and in a lossless manner, except those sequences that occur in a window of length B+W following the start of the erasure burst. We define a rate-recovery function R(B, W), the minimum compression rate that can be achieved in this framework, and develop upper and lower bounds for first-order Markov sources. For the special class of linear diagonally correlated deterministic sources, we propose a new coding technique - prospicient coding - that achieves the rate-recovery function. Finally, a lossy extension to the rate-recovery function is also studied for a class of Gaussian sources where the source is temporally and spatially i.i.d. and the decoder aims to recover a collection of past K sources with a quadratic distortion measure. The optimal rate-recovery function is compared with the sub-optimal techniques including forward error correction coding (FEC) and Wyner-Ziv coding, and performance gains are quantified.
This paper considers linear matrices for a coding problem for multiple access networks. It is proved that we can construct codes by using sparse matrices, which are also called low density parity check (LDPC) matrices.
ISBN:
(纸本)0780377990
This paper considers linear matrices for a coding problem for multiple access networks. It is proved that we can construct codes by using sparse matrices, which are also called low density parity check (LDPC) matrices.
This work studies error exponent limits in hypothesis testing (HT) in a distributed scenario with partial communication constraints. We derive general conditions on the Type I error restriction under which the error e...
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ISBN:
(纸本)9781728127231
This work studies error exponent limits in hypothesis testing (HT) in a distributed scenario with partial communication constraints. We derive general conditions on the Type I error restriction under which the error exponent of the optimal Type II error has a closed-form characterization for the task of testing against independence. We show that the error exponent is preserved for a family of decreasing Type I error restrictions. Complementing this analysis, new expressions are derived to bound the optimal Type II error probability for a finite number of observations. These bounds shed light about the velocity at which error exponent limits are attained with the number of samples.
This paper considers the problem of characterizing the optimal tradeoff between the total transmit versus receive rate in the Gray-Wyner network. This tradeoff plays a crucial role in many important practical applicat...
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ISBN:
(纸本)9781457704376
This paper considers the problem of characterizing the optimal tradeoff between the total transmit versus receive rate in the Gray-Wyner network. This tradeoff plays a crucial role in many important practical applications including establishing fundamental limits in databases for correlated sources and in minimum cost routing for networks. We develop the insight into this tradeoff by defining two quantities C(X;Y;R') and K(X;Y;R ''), which quantify the shared rate as a function of the total transmit and receive rates respectively. Closely tied up with this tradeoff is the notion of common information of two dependent random variables. The two most influential definitions are due to Wyner [2] and Gacs-Korner [1]. Though it is well know that these definitions can be characterized as two extreme points in the Gray-Wyner region, no contour with operational significance is known which connects them. We will show that the tradeoff between transmit and receive rates leads to a contour of points on the boundary of Gray-Wyner region which passes through the operating points of Wyner and Gacs-Korner. We use these properties to derive alternate characterizations for the two definitions of common information under a broader unified framework.
Motivated by potential applications in wireless sensor networks, we consider the problem of communicating a large number of correlated analog sources over a Gaussian multiple-access channel using non-orthogonal code-d...
详细信息
ISBN:
(纸本)9781479903573
Motivated by potential applications in wireless sensor networks, we consider the problem of communicating a large number of correlated analog sources over a Gaussian multiple-access channel using non-orthogonal code-division multiple-access (CDMA). We present a joint source-channel decoder which exploits the inter-source correlation for interference reduction in the CDMA channel. This decoder uses a linear minimum mean square error (MMSE) multi-user detector (MUD) in tandem with a MMSE joint source decoder for multiple sources to achieve a computational complexity that scales with the number of sources. However, iterative exchange of extrinsic information between the MUD and the joint source decoder leads to improved interference cancellation. Experimental results obtained with decoding observations from Gaussian random fields show that the proposed iterative decoder can achieve a considerable performance gain compared to a non-iterative decoder. The results also show that the iterative decoder is robust against the performance degradation due to correlated interference in a non-orthogonal CDMA channel.
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