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• W1986092017 abstract "A model, called the linear transform network (LTN), is proposed to analyze the compression and estimation of correlated signals transmitted over directed acyclic graphs (DAGs). An LTN is a DAG network with multiple source and receiver nodes. Source nodes transmit subspace projections of random correlated signals by applying reduced-dimension linear transforms. The subspace projections are linearly processed by multiple relays and routed to intended receivers. Each receiver applies a linear estimator to approximate a subset of the sources with minimum mean squared error (MSE) distortion. The model is extended to include noisy networks with power constraints on transmitters. A key task is to compute all local compression matrices and linear estimators in the network to minimize end-to-end distortion. The non-convex problem is solved iteratively within an optimization framework using constrained quadratic programs (QPs). The proposed algorithm recovers as special cases the regular and distributed Karhunen-Loeve transforms (KLTs). Cut-set lower bounds on the distortion region of multi-source, multi-receiver networks are given for linear coding based on convex relaxations. Cut-set lower bounds are also given for any coding strategy based on information theory. The distortion region and compression-estimation tradeoffs are illustrated for different communication demands (e.g. multiple unicast), and graph structures." @default.
• W1986092017 created "2016-06-24" @default.
• W1986092017 creator A5025728381 @default.
• W1986092017 creator A5063528341 @default.
• W1986092017 date "2012-06-01" @default.
• W1986092017 modified "2023-10-15" @default.
• W1986092017 title "Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks" @default.
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• W1986092017 doi "https://doi.org/10.1109/tsp.2012.2188716" @default.
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