Entity

Time filter

Source Type


Marques A.G.,King Juan Carlos University | Segarra S.,University of Pennsylvania | Leus G.,Technical University of Delft | Ribeiro A.,University of Pennsylvania
IEEE Transactions on Signal Processing | Year: 2016

A new scheme to sample signals defined on the nodes of a graph is proposed. The underlying assumption is that such signals admit a sparse representation in a frequency domain related to the structure of the graph, which is captured by the so-called graph-shift operator. Instead of using the value of the signal observed at a subset of nodes to recover the signal in the entire graph, the sampling scheme proposed here uses as input observations taken at a single node. The observations correspond to sequential applications of the graph-shift operator, which are linear combinations of the information gathered by the neighbors of the node. When the graph corresponds to a directed cycle (which is the support of time-varying signals), our method is equivalent to the classical sampling in the time domain. When the graph is more general, we show that the Vandermonde structure of the sampling matrix, critical when sampling time-varying signals, is preserved. Sampling and interpolation are analyzed first in the absence of noise, and then noise is considered. We then study the recovery of the sampled signal when the specific set of frequencies that is active is not known. Moreover, we present a more general sampling scheme, under which, either our aggregation approach or the alternative approach of sampling a graph signal by observing the value of the signal at a subset of nodes can be both viewed as particular cases. Numerical experiments illustrating the results in both synthetic and real-world graphs close the paper. © 2015 IEEE. Source


Segarra S.,University of Pennsylvania | Marques A.G.,King Juan Carlos University | Ribeiro A.,University of Pennsylvania
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2016

We introduce node-variant graph filters, which allow the simultaneous implementation of multiple (regular) graph filters at different nodes, and study their design to implement arbitrary linear transformations between graph signals. Node-variant graph filters can be implemented distributedly, making them suitable for networked settings. We determine spectral conditions under which a specific linear transformation can be implemented perfectly and, for the cases where perfect implementation is infeasible, the design of optimal approximations for different error metrics is analyzed. We demonstrate the practical relevance of the developed framework by studying the application of node-variant graph filters for analog network coding. © 2016 IEEE. Source


Gatsis N.,University of Texas at San Antonio | Marques A.G.,King Juan Carlos University
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2014

A system comprising a utility company serving a set of electricity end-users is considered. The utility company can purchase energy from the wholesale market. It is also connected to a renewable energy production facility, from which it can harvest energy at no cost, and also to a battery for energy storage. Ahead of a scheduling horizon, the utility purchases energy based on forecasted demand and renewable energy production. During online operation, if the renewable energy is not adequate, real-time decisions with respect to user load shedding, energy procurement, and battery charging or discharging need to be made. The problem is cast in a stochastic approximation framework, and is solved online via a dual stochastic subgradient method with low per-slot complexity. © 2014 IEEE. Source


Marques A.G.,King Juan Carlos University | Lopez-Ramos L.M.,King Juan Carlos University | Giannakis G.B.,University of Minnesota | Ramos J.,King Juan Carlos University
IEEE Journal on Selected Areas in Communications | Year: 2012

Efficient design of cognitive radios (CRs) calls for secondary users implementing adaptive resource allocation schemes that exploit knowledge of the channel state information (CSI), while at the same time limiting interference to the primary system. This paper introduces stochastic resource allocation algorithms for both interweave (also known as overlay) and underlay cognitive radio paradigms. The algorithms are designed to maximize the weighted sum-rate of orthogonally transmitting secondary users under average-power and probabilistic interference constraints. The latter are formulated either as short-or as long-term constraints, and guarantee that the probability of secondary transmissions interfering with primary receivers stays below a certain pre-specified level. When the resultant optimization problem is non-convex, it exhibits zero-duality gap and thus, due to a favorable structure in the dual domain, it can be solved efficiently. The optimal schemes leverage CSI of the primary and secondary networks, as well as the Lagrange multipliers associated with the constraints. Analysis and simulated tests confirm the merits of the novel algorithms in: i) accommodating time-varying settings through stochastic approximation iterations; and ii) coping with imperfect CSI. © 2012 IEEE. Source


Marques A.G.,King Juan Carlos University | Lopez-Ramos L.M.,King Juan Carlos University | Giannakis G.B.,University of Minnesota | Ramos J.,King Juan Carlos University | Caamano A.J.,King Juan Carlos University
IEEE Transactions on Vehicular Technology | Year: 2012

Algorithms that jointly allocate resources across different layers are envisioned to boost the performance of wireless systems. Recent results have revealed that two of the most important parameters that critically affect the resulting cross-layer designs are channel- and queue-state information (QSI). Motivated by these results, this paper relies on stochastic convex optimization to develop optimal algorithms that use instantaneous fading and queue length information to allocate resources at the transport (flow-control), link, and physical layers. Focus is placed on a cellular system, where an access point exchanges information with different users over flat-fading orthogonal channels. Both uplink and downlink setups are considered. The allocation strategies are obtained as the solution of a constrained utility maximization problem that involves average performance metrics. It turns out that the optimal allocation at a given instant depends on the instantaneous channel-state information (CSI) and Lagrange multipliers, which are associated with the quality-of-service (QoS) requirements and the operating conditions of the system. The multipliers are estimated online using stochastic approximation tools and are linked with the window-averaged length of the queues. Capitalizing on those links, queue stability and average queuing delay of the developed algorithms are characterized, and a simple mechanism is devised to effect delay priorities among users. © 2012 IEEE. Source

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