In this paper, we consider a relay network which consists of two single-antenna transceivers and single-antenna relay nodes. Considering a two time slot two-way relaying scheme, each relay adjusts the phase and the am...
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In this paper, we consider a relay network which consists of two single-antenna transceivers and single-antenna relay nodes. Considering a two time slot two-way relaying scheme, each relay adjusts the phase and the amplitude of the mixture signal it receives fromthe two transceivers during the first time slot, by multiplying it with a complex beamforming coefficient. Then each relay transmits the so-obtained signal in the second time slot. Aiming at optimally calculating the beamforming coefficients as well as the transceiver transmit powers, we study two different approaches. In the first approach, we minimize the total transmit power (dissipated in the whole network) subject to two constraints on the transceivers' received signal-to-noise ratios (SNRs). We prove that such a power minimization technique has a unique solution. We also show that the optimal weight vector can be obtained through a simple iterative algorithm which enjoys a linear computational complexity per iteration. We also prove that for symmetric relaying schemes (where the two constraints on the transceiver SNRs are the same), half of the minimum total transmit power will be allocated to the two transceivers and the remaining half will be shared among the relaying nodes. In the second approach, we will study an SNR balancing technique. In this technique, the smaller of the two transceiver SNRs is maximized while the total transmit power is kept below a certain power budget. We show that this problem has also a unique solution which can be obtained through an iterative procedure with a linear computational complexity per iteration. We also prove that this approach leads to a power allocation scheme, where half of the maximum power budget is allocated to the two transceivers and the remaining half will be shared among all the relay nodes. For both approaches, we devise distributed schemes which require a minimal cooperation among the two transceivers and the relays. In fact, we show that both techn
distributed averaging describes a class of network algorithms for the decentralized computation of aggregate statistics. Initially, each node has a scalar data value, and the goal is to compute the average of these va...
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distributed averaging describes a class of network algorithms for the decentralized computation of aggregate statistics. Initially, each node has a scalar data value, and the goal is to compute the average of these values at every node (the so-called average consensus problem). Nodes iteratively exchange information with their neighbors and perform local updates until the value at every node converges to the initial network average. Much previous work has focused on algorithms where each node maintains and updates a single value;every time an update is performed, the previous value is forgotten. Convergence to the average consensus is achieved asymptotically. The convergence rate is fundamentally limited by network connectivity, and it can be prohibitively slow on topologies such as grids and random geometric graphs, even if the update rules are optimized. In this paper, we provide the first theoretical demonstration that adding a local prediction component to the update rule can significantly improve the convergence rate of distributed averaging algorithms. We focus on the case where the local predictor is a linear combination of the node's current and previous values (i. e., two memory taps), and our update rule computes a combination of the predictor and the usual weighted linear combination of values received from neighboring nodes. We derive the optimal mixing parameter for combining the predictor with the neighbors' values, and conduct a theoretical analysis of the improvement in convergence rate that can be achieved using this acceleration methodology. For a chain topology on nodes, this leads to a factor of improvement over standard consensus, and for a two-dimensional grid, our approach achieves a factor of root N improvement.
This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communi...
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This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper, we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.
A novel method for online tracking of the changes in the nonlinearity within both real-domain and complex-valued signals is introduced. This is achieved by a collaborative adaptive signalprocessing approach based on ...
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A novel method for online tracking of the changes in the nonlinearity within both real-domain and complex-valued signals is introduced. This is achieved by a collaborative adaptive signalprocessing approach based on a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. Implementations for tracking nonlinearity in general and then more specifically sparsity are illustrated on both benchmark and real world data. It is also shown that by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion.
In this paper, we present a semi-closed form solution to the SNR balancing problem, first considered in [1], in the context of network beamforming design for two-way relay networks. This solution relies on a simple bi...
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ISBN:
(纸本)9781424442966
In this paper, we present a semi-closed form solution to the SNR balancing problem, first considered in [1], in the context of network beamforming design for two-way relay networks. This solution relies on a simple bisection method to obtain the transmit power of one of the two transceivers. Given this transmit power, the relay beamforming weight vector is shown to have a closed-form solution. Simulation results show that the proposed solution has significantly lower computational complexity. We also present a suboptimal solution which does not use the aforementioned bisection algorithm while performs very closely to the optimal beamformer.
We study the sum-rate maximization approach when applied to design a decentralized beamformer for two-way relay networks. Considering a constraint on the total transmit power consumed in the whole network, we prove th...
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ISBN:
(纸本)9781424442966
We study the sum-rate maximization approach when applied to design a decentralized beamformer for two-way relay networks. Considering a constraint on the total transmit power consumed in the whole network, we prove that sum-rate maximization is equivalent to an SNR balancing approach where the smallest of the two receive SNRs is maximized subject to the same total power constraint.
In this paper, we present a semi-closed form solution to the SNR balancing problem, first considered in [1], in the context of network beamforming design for two-way relay networks. This solution relies on a simple bi...
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ISBN:
(纸本)9781424442959
In this paper, we present a semi-closed form solution to the SNR balancing problem, first considered in [1], in the context of network beamforming design for two-way relay networks. This solution relies on a simple bisection method to obtain the transmit power of one of the two transceivers. Given this transmit power, the relay beamforming weight vector is shown to have a closed-form solution. Simulation results show that the proposed solution has significantly lower computational complexity. We also present a suboptimal solution which does not use the aforementioned bisection algorithm while performs very closely to the optimal beamformer.
We study the sum-rate maximization approach when applied to design a decentralized beamformer for two-way relay networks. Considering a constraint on the total transmit power consumed in the whole network, we prove th...
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ISBN:
(纸本)9781424442959
We study the sum-rate maximization approach when applied to design a decentralized beamformer for two-way relay networks. Considering a constraint on the total transmit power consumed in the whole network, we prove that sum-rate maximization is equivalent to an SNR balancing approach where the smallest of the two receive SNRs is maximized subject to the same total power constraint.
This paper proposes an approach to accelerate local, linear iterative network algorithms asymptotically achieving distributed average consensus. We focus on the class of algorithms in which each node initializes its &...
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This paper proposes an approach to accelerate local, linear iterative network algorithms asymptotically achieving distributed average consensus. We focus on the class of algorithms in which each node initializes its "state value" to the local measurement and then at each iteration of the algorithm, updates this state value by adding a weighted sum or its, own and its neighbors' state values. Provided the weight matrix satisfies certain convergence conditions, the state values asymptotically converge to the average of the measurements, but the convergence is generally slow, Impeding the practical application of these algorithms. In order to improve the rate of convergence, we propose a novel method where each node employs a linear predictor to predict future node values. The local update then becomes a convex (weighted) sum of the original consensus update and the prediction, convergence is faster because redundant states are bypassed. The method is linear and poses a small computational burden. For a concrete theoretical analysis, we prove the existence of a convergent solution in the general case and then focus on one-step prediction based on the current state, and derive the optimal mixing parameter in the convex sum for this case. Evaluation of the optimal mixing parameter requires knowledge or the eigenvalues of the weight matrix, so we present a bound on the optimal parameter. Calculation or this bound requires only local information. We provide simulation results that demonstrate the validity and effectiveness of the proposed scheme. The results indicate that the incorporation of a multistep predictor call lead to convergence rates that are much faster than those achieved by an optimum weight matrix in the standard consensus framework.
We consider the distributed estimation-by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished befor...
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We consider the distributed estimation-by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional. In energy-limited wireless sensor networks, both local quantization and multihop transmission are essential to save transmission energy and thus prolong the network lifetime. The network lifetime optimization problem includes three components: i) optimizing source coding at each sensor node, ii) optimizing source throughput of each sensor node, and iii) optimizing multihop routing path. Fortunately, source coding optimization can be decoupled from source throughput and multihop routing path optimization, and is solved by introducing a concept of equivalent 1-bit MSE function. Based on the optimal source coding, the source throughput and multihop routing path optimization is formulated as a linear programming (LP) problem, which suggests a new notion of character-based routing. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.
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