We consider the problem of communicating over a relay-assisted multiple-input multiple-output (MIMO) channel with additive noise, in which physically separated relays forward quantized information to a central decoder...
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We consider the problem of communicating over a relay-assisted multiple-input multiple-output (MIMO) channel with additive noise, in which physically separated relays forward quantized information to a central decoder where the transmitted message is to be decoded. We assume that channel state information is available in the transmitter and show that the design of a rational-forcing precoder-a precoder which is matched to the quantizers used in the relays-is beneficial for reducing the symbol error probability. It turns out that for such rationalforcing precoder based systems, there is natural tradeoff between the peak to average power ratio in the transmitter and the rate of communication between the relays and the central decoder. The precoder design problem is formulated mathematically, and several algorithms are developed for realizing this tradeoff. Optimality of the decoder communication rate is shown based on a result in distributed function computation. Numerical and simulation results show that a useful tradeoff can be obtained between the excess decoder communication rate and the peakaverage power ratio in the transmitter.
A key aspect of many resource allocation problems is the need for the resource controller to compute a function, such as the max or arg max, of the competing users' metrics. Information must be exchanged between t...
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A key aspect of many resource allocation problems is the need for the resource controller to compute a function, such as the max or arg max, of the competing users' metrics. Information must be exchanged between the competing users and the resource controller in order for this function to be computed. In many practical resource controllers, the competing users' metrics are communicated to the resource controller, which then computes the desired extremization function. However, in this paper, it is shown that information rate savings can be obtained by recognizing that the controller only needs to determine the result of this extremization function. If the extremization function is to be computed losslessly, the rate savings are shown in most cases to be at most 2 b independent of the number of competing users. Motivated by the small savings in the lossless case, simple achievable schemes for both the lossy and interactive variants of this problem are considered. It is shown that both of these approaches have the potential to realize large rate savings, especially in the case where the number of competing users is large. For the lossy variant, it is shown that the proposed simple achievable schemes are in fact close to the fundamental limit given by the rate distortion function.
We construct and analyze the communication cost of protocols (interactive and one-way) for classifying X = (X-1,X-2,...X-n) is an element of[0,1)(n) subset of R-n, in a network with n >= 2 nodes, with X-i known onl...
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ISBN:
(纸本)9781538692912
We construct and analyze the communication cost of protocols (interactive and one-way) for classifying X = (X-1,X-2,...X-n) is an element of[0,1)(n) subset of R-n, in a network with n >= 2 nodes, with X-i known only at node i. The classifier takes the form Sigma(n)(i=1) h(i)X(i) a, with weights h(i) is an element of{-1,+1}. The interactive protocol (a zero-error protocol) exchanges a variable number of messages depending on the input X and its sum rate is directly proportional to its mean stopping time. An exact analysis, as well as an approximation of the mean stopping time is presented and shows that it depends on gamma = alpha + (1/2 - beta), where alpha = a/n and beta = m/n, with m being the number of positive weights. In particular, the mean stopping time grows logarithmically in n when gamma = 0, and is bounded in n otherwise. Comparisons show that the sum rate of the interactive protocol is smaller than that of the one-way protocol when the error probability for the one-way protocol is small, with the reverse being true when the error probability is large. Comparisons of the interactive protocol are also made with lower bounds on the sum rate show the correct scaling behavior when gamma not equal 0.
We consider the problem of distributedcomputation of the nearest lattice point for a two-dimensional lattice. An interactive model of communication is considered. We address the problem of reconfiguring a specific re...
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ISBN:
(纸本)9781509040964
We consider the problem of distributedcomputation of the nearest lattice point for a two-dimensional lattice. An interactive model of communication is considered. We address the problem of reconfiguring a specific rectangular partition, a nearest plane, or Babai, partition, into the Voronoi partition. Expressions are derived for the error probability as a function of the total number of communicated bits. With an infinite number of allowed communication rounds, the average cost of achieving zero error probability is shown to be finite. For the interactive model, with a single round of communication, expressions are obtained for the error probability as a function of the bits exchanged. We observe that the error exponent depends on the lattice.
We consider in-network computation of an arbitrary function over an arbitrary communication network. A network with capacity constraints on the links is given. Some nodes in the network generate data, e. g., sensor no...
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ISBN:
(纸本)9781457705953
We consider in-network computation of an arbitrary function over an arbitrary communication network. A network with capacity constraints on the links is given. Some nodes in the network generate data, e. g., sensor nodes in a sensor network. An arbitrary function of this distributed data is to be obtained at a terminal node. The structure of the function is described by a given computation schema, which in turn is represented by a directed tree. We define a new notion of conservation of flow suitable in this setup and design computing and communicating schemes to obtain the function at the terminal at the maximum rate. For this, we formulate linear programs to determine network flows that maximize the computation rate. Our approach introduces the network flow techniques to the distributed function computation setup where such a scope was hitherto unsuspected due to the lack of traditional conservation of flow.
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