1,721,056 research outputs found

    Distributed space-time coding for regenerative relay networks

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    Cooperation among mobile users (MUs) in a wireless network can be very useful to reduce the total radiated power necessary to insure the delivery of the information with the desired quality of service. A systematic framework for achieving such a gain consists in making the cooperating nodes act as the antennas of a virtual transmit array, operating according to a distributed space-time coding (DSTC) strategy. However, cooperation implies the allocation of dedicated resources, typically power and time slots, for the exchange of data between source and intermediate nodes (relays). It is then necessary to design the system properly to make possible a final net gain, taking into account all resources involved in the communication. In this paper, we consider regenerative relays and we analyze the effect of intermediate decision errors at the relay nodes. We derive the optimal maximum-likelihood (NIL) detector, at the final destination, in case of binary phase-shift keying (BPSK) transmission, and a suboptimal scalar detector, whose bit-error rate (BER) is expressed in (approximate) closed form. Since with DSTC the transmit antennas are not colocated, we show how to allocate the power among source and relay terminals in order to minimize the average BER at the final destination. Finally, we compare alternative cooperation and decoding strategies

    Bio-inspired sensor network design: Distributed decision through self-synchronization

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    The goal of this article is to show how a simple self-synchronization mechanism, borrowed from biological systems, can form the basic tool for achieving globally optimal distributed decisions in a wireless sensor network with no need for a fusion center. After describing the basic interaction mechanism among the network nodes, the conditions are illustrated guaranteeing the convergence of each node to a global (or local) consensus coinciding with the globally (locally) optimal decision statistics. The interaction mechanism takes into account the physical channel parameters, such as fading coefficients and propagation delays. Then the results are illustrated through examples of distributed estimation and multiple hypothesis testing. The energy consumption issues are addressed and discuss some possible implementations

    Distributed Consensus Over Wireless Sensor Networks Affected by Multipath Fading

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    The design of sensor networks capable of reaching a consensus on a globally optimal decision test, without the need for a fusion center, is a problem that has received considerable attention in the last years. Many consensus algorithms have been proposed, with convergence conditions depending on the graph describing the interaction among the nodes. In most works, the graph is undirected and there are no propagation delays. Only recently, the analysis has been extended to consensus algorithms incorporating propagation delays. In this correspondence, we propose a consensus algorithm able to converge to a globally optimal decision statistic, using a wideband wireless network, governed by a fairly simple MAC mechanism, where each link is a multipath, frequency-selective, channel. The main contribution of the correspondence is to derive necessary and sufficient conditions on the network topology and sufficient conditions on the channel transfer functions guaranteeing the exponential convergence of the consensus algorithm to a globally optimal decision value, for any bounded delay condition

    Decentralized Maximum Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems

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    In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximum-likelihood (ML) estimate through self-synchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a first-order dynamical system initialized with the local measurements. Nearby nodes interact with each other exchanging their state value, and the final estimate is associated to the state derivative of each dynamical system. We derive the conditions on the coupling mechanism guaranteeing that, if the network observes one common phenomenon, each node converges to the globally optimal ML estimate. We prove that the synchronized state is globally asymptotically stable if the coupling strength exceeds a given threshold. Acting on a single parameter, the coupling strength, we show how, in the case of nonlinear coupling, the network behavior can switch from a global consensus system to a spatial clustering system. Finally, we show the effect of the network topology on the scalability properties of the network, and we validate our theoretical findings with simulation results

    Distributed decision through self-synchronizing sensor networks in the presence of propagation delays and asymmetric channels

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    In this paper we propose and analyze a distributed algorithm for achieving globally optimal decisions, either estimation or detection, through a self-synchronization mechanism among linearly coupled integrators initialized with local measurements. We model the interaction among the nodes as a directed graph with weights (possibly) dependent on the radio channels, and we pose special attention to the effect of the propagation delay occurring in the exchange of data among sensors, as a function of the network geometry. We derive necessary and sufficient conditions for the proposed system to reach a consensus on globally optimal decision statistics. One of the major results proved in this work is that a consensus is reached with exponential convergence speed for any bounded delay condition if and only if the directed graph is quasi-strongly connected. We provide a closed form expression for the global consensus, showing that the effect of delays is, in general, the introduction of a bias in the final decision. Finally, we exploit our closed form expression to devise a double-step consensus mechanism able to provide an unbiased estimate with minimum extra complexity, without the need to know or estimate the channel parameters
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