Examinando por Autor "Garcia Marques, Antonio"

Mostrando 1 - 20 de 27

A Bayesian Decision Model for Intelligent Routing in Sensor Networks
(3rd International Symposium on Wireless Communication Systems, 2006. ISWCS '06, 2006-09) Arroyo-Valles, Rocio; Garcia Marques, Antonio; Vinagre Díaz, Juan José; Cid Sueiro, Jesús
In this paper we propose an efficient energy-aware routing algorithm based on learning patterns. Energy and message importance are considered in a Bayesian model in order to establish intelligent decision rules that make the network economize in crucial resources.
A Learning Algorithm for Energy-Efficient Routing of Prioritized Messages in Wireless Sensor Networks
(2007-10-01T08:52:49Z) Arroyo-Valles, Rocio; Garcia Marques, Antonio; Cid Sueiro, Jesús
Energy is a valuable resource in wireless sensor networks since it constitutes a limiting factor for the network lifetime. In order to make an efficient use of its own energy resources, each node in the network should be aware of the energy resources at other nodes, which can be relevant to the success of their routing decisions. The proposal of this paper is twofold:(i)to design a routing algorithm based on learning patterns using geographic information and (ii) to focus on the cut down in energy consumption. We show that by exploiting local information from the signals detected at each node, sensor nodes can learn to route messages in order to improve the communication performance of the overall network and minimize the need of coordination or signalling protocols among nodes. Moreover, if messages are prioritized by some importance parameter, the overall importance of the successfully transmitted messages can be drastically improved. Experimental results highlight that our algorithm achieves a good performance in terms of successful delivery rate and maximizes the importance of the received messages.
Cuantificación del estado del canal para la minimización de la potencia en sistemas con transmisores adaptativos
(2007-01) Garcia Marques, Antonio
Energy Efficient MISO Systems Using Adaptive Modulation and Coding
(2006-11-01T09:13:12Z) Garcia Marques, Antonio; Wang, Xin; Giannakis, Georgios B.
By viewing the coherent wireless sensor network (WSN) setup as a distributed space-time multi-input singleoutput (MISO) system, we minimize average transmit-power when sensors communicate with a fusion center (FC) using adaptive modulation and coding over a wireless fading channel. To this end, we derive optimal distributed beamforming and resource allocation strategies when the full (F-) channel state information at the transmitters (CSIT) is available, or, each sensor has F-CSIT of its own link with the FC but only quantized CSIT of other sensors through finite-rate feedback. Numerical results are presented to evaluate the power savings of the novel strategies.
Energy-efficient Selective Forwarding for Sensor Networks
(2008-06-01T09:24:16Z) Arroyo-Valles, Rocio; Garcia Marques, Antonio; Cid Sueiro, Jesús
In this paper a new energy-efficient scheme for data transmission in wireless sensor networks is proposed. It is based on the idea of selective forwarding: sensor nodes only transmit the most relevant messages, discarding the least important ones. To do so, messages are assumed to be graded with an importance value, and a forwarding threshold, which depends on the sensor consumption patterns, the available energy resources and the information obtained from the neighborhood, is applied to these values. In this approach, the sensor decision also depends on the expected behavior of neighboring nodes, so as to maximize not only the transmission efficiency, but also the performance of the whole communication up to the estination node. Simulation results show that the proposed scheme increases the network lifetime, and maximizes the global importance of the messages received by the sink node. Index Terms¿selective forwarding, energy-efficiency, message importance, sensor networks.
Energy-Efficient TDMA with Quantized Channel State Information
(2006-06-01T09:35:20Z) Garcia Marques, Antonio; Wang, Xin; Giannakis, Georgios B.
We deal with energy efficient time-division multiple access (TDMA)over fading channels with finite-rate feedback in the power-limited regime. Through finite-rate feedback from the access point, users acquire quantized channel state information. The goal is to map channel quantization states to adaptive modulation and coding (AMC) modes and allocate optimally time slots to users so that transmitpower is minimized. To this end, we develop two joint quantization and resource allocation approaches. In the first one, we rely on the quantization regions associated to each AMC mode and the time allocation policy inherited from the perfect CSI case to optimize the fixed transmit-power across quantization states. In the second approach,we pursue separable optimization and resort to coordinate descent algorithms to solve the following two sub-problems: (a)given a time allocation, we optimize the quantization regions and transmit-powers; and (b) with improved quantization regions, we optimize the time allocation policy. Numerical results are present to evaluate the energy savings and compare the novel approaches.
Enhanced graph-learning schemes driven by similar distributions of motifs
(Institute of Electrical and Electronics Engineers, 2023-08-10) Rey, Samuel; T. Mitchell, Roddenberry; Segarra, Santiago; Garcia Marques, Antonio
This paper looks at the task of network topology inference, where the goal is to learn an unknown graph from nodal observations. One of the novelties of the approach put forth is the consideration of prior information about the density of motifs of the unknown graph to enhance the inference of classical Gaussian graphical models. Directly dealing with the density of motifs constitutes a challenging combinatorial task. However, we note that if two graphs have similar motif densities, one can show that the expected value of a polynomial applied to their empirical spectral distributions will be similar. Guided by this, we first assume that we observe a reference graph with a density of motifs similar to that of the sought graph, and then, we exploit this relation by incorporating a similarity constraint and a regularization term in the graph learning optimization problem. The (non-)convexity of the optimization problem is discussed, and a computationally efficient alternating majorization-minimization algorithm is designed. We assess the performance of the proposed method through exhaustive numerical experiments, where different constraints are considered and compared against popular alternatives on both synthetic and real-world datasets
Error-Aware Scheduling and its Effects on Efficiency and Fairness
(2009-07-29T13:19:03Z) Serrano, Pablo; Larrabeiti, David; Urueña, Manuel; Garcia Marques, Antonio
Error-Aware Scheduling and its Effects on Efficiency and Fairness
(2009-07-29T13:00:30Z) Serrano, Pablo; Larrabeiti, David; Urueña, Manuel; Garcia Marques, Antonio
This paper describes a mechanism to adapt an existing wireline scheduling algorithm for a WLAN Access Point, by taking into account the error ratio affecting each flow. This enhancement is based on the idea of weighting flows according to their error ratio. Users connected over error-prone channels get their bandwidth share increased, up to a point where the overall efficiency breaks down,and the mechanism is reverted. The cost of this mechanism in terms of fairness is also addressed.
Joint Network Topology Inference in the Presence of Hidden Nodes
(Institute of Electrical and Electronics Engineers, 2024-04-23) Navarro, Madeline; Rey, Samuel; Buciulea, Andrei; Garcia Marques, Antonio; Segarra, Santiago
We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations. While most joint inference methods assume that observations are available at all nodes, we consider the realistic and more difficult scenario where a subset of nodes are hidden and cannot be measured. Under the assumptions that the partially observed nodal signals are graph stationary and the networks have similar connectivity patterns, we derive structural characteristics of the connectivity between hidden and observed nodes. This allows us to formulate an optimization problem for estimating networks while accounting for the influence of hidden nodes. We identify conditions under which a convex relaxation yields the sparsest solution, and we formalize the performance of our proposed optimization problem with respect to the effect of the hidden nodes. Finally, synthetic and real-world simulations provide evaluations of our method in comparison with other baselines
Learning graphs from smooth and graph-stationary signals with hidden variables
(Institute of Electrical and Electronics Engineers, 2022-03-22) Buciulea, Andrei; Rey, Samuel; Garcia Marques, Antonio
Network-topology inference from (vertex) signal observations is a prominent problem across data-science and engineering disciplines. Most existing schemes assume that observations from all nodes are available, but in many practical environments, only a subset of nodes is accessible. A natural (and sometimes effective) approach is to disregard the role of unobserved nodes, but this ignores latent network effects, deteriorating the quality of the estimated graph. Differently, this paper investigates the problem of inferring the topology of a network from nodal observations while taking into account the presence of hidden (latent) variables . Our schemes assume the number of observed nodes is considerably larger than the number of hidden variables and build on recent graph signal processing models to relate the signals and the underlying graph. Specifically, we go beyond classical correlation and partial correlation approaches and assume that the signals are smooth and/or stationary in the sought graph. The assumptions are codified into different constrained optimization problems, with the presence of hidden variables being explicitly taken into account. Since the resulting problems are ill-conditioned and non-convex, the block matrix structure of the proposed formulations is leveraged and suitable convex-regularized relaxations are presented. Numerical experiments over synthetic and real-world datasets showcase the performance of the developed methods and compare them with existing alternatives
Minimizing Power in Wireless OFDMA with Limited Rate Feedback
(2007-03-11T09:52:37Z) Garcia Marques, Antonio; Giannakis, Georgios B.; Digham, Fadel F.; Ramos, Javier
Emerging applications involving low-cost wireless sensor networks motivate well optimization of multi-user orthogonal frequency-division multiple access (OFDMA) in the power-limited regime. In this context, the present paper relies on limited-rate feedback (LRF) sent from the access point to terminals to minimize the total average transmit-power under individual average rate and error probability constraints. The characterization of optimal bit, power and subcarrier allocation policies based on LRF, as well as optimal channel quantization are provided. Numerical examples corroborate the analytical claims and reveal that significant power savings result even with few fed back bits.
Minimizing Transmit-Power for Coherent Communications in Wireless Sensor Networks using Quantized Channel State Information
(2007-04-16T10:00:18Z) Garcia Marques, Antonio; Wang, Xin; Giannakis, Georgios B.
We consider minimizing average transmit-power with finite-rate feedback for coherent communications in a wireless sensor network (WSN), where sensors communicate with a fusion center(FC)using adaptive modulation and coding over a wireless fading channel. By viewing the coherent WSN setup as a distributed space-time multi-input single-output (MISO) system, we develop beamforming and resource allocation strategies and design optimal quantizers when the sensors only have available quantized (Q-) channel state information at the transmitters (CSIT) through a finite-rate feedback channel. Numerical results reveal that our novel design based on Q-CSIT yields significant power savings even for a small number of feedback bits.
Optimal Selective Transmission under Energy Constraints in Sensor Networks
(2009-06-24T08:10:13Z) Arroyo-Valles, Rocio; Garcia Marques, Antonio; Cid Sueiro, Jesús
An optimum selective transmission scheme for energy-limited sensor networks where sensors send or forward messages of different importance (priority) is developed.Considering the energy costs, the available battery, the message importances and their statistical distribution, sensors decide whether to transmit or discard a message so that the importance sum of the effectively transmitted messages is maximized. It turns out that the optimal decision is made comparing the message importance with a time-variant threshold. Moreover, the gain of the selective transmission scheme, compared to a non-selective one, critically depends on the energy expenses, among other factors. Albeit suboptimal, practical schemes that operate under less demanding conditions than those for the optimal one are developed. Effort is placed into three directions: (i) the analysis of the optimal transmission policy for several stationary importance distributions; (ii) the design of a transmission policy with invariant threshold that entails asymptotic optimality; and (iii) the design of an adaptive algorithm that estimates the importance distribution from the actual received (or sensed) messages. Numerical results corroborating our theoretical claims and quantifying the gains of implementing the selective scheme close this paper.
Optimal stochastic dual resource allocation for cognitive radios based on quantized CSI
(2008-04-01T10:11:32Z) Garcia Marques, Antonio; Wang, Xin; Giannakis, Georgios B.
The present paper deals with dynamic resource management based on quantized channel state information(CSI)for multicarrier cognitive radio networks comprising primary and secondary wireless users. For each subcarrier, users rely on adaptive modulation, coding and power modes that they select in accordance with the limited-rate feedback they receive from the access point. The access point uses CSI to maximize the sum of generic concave utilities of the individual average rates in the network while respecting rate and power constraints on the primary and secondary users. Using a stochastic dual approach, optimum dual prices are found to optimally allocate resources across users per channel realization without requiring knowledge of the channel distribution.
Optimizing Energy-Efficient of TDMA with Finite Rate Feedback
(2007-04-16) Garcia Marques, Antonio; Wang, Xin; Giannakis, Georgios B.
We deal with energy efficient time-division multiple access (TDMA) over fading channels with finite-rate feedback (FRF) for use in the power-limited regime. Through FRF from the access point, users acquire quantized channel state information. The goal is to map channel quantization states to adaptive modulation and coding modes and allocate optimally time slots to users so that the total average transmit-power is minimized. To this end, we develop a joint quantization and resource allocation approach, which decouples the complicated problem at hand into three minimization sub-problems and relies on a coordinate descent approach to iteratively effect energy efficiency. Numerical results are presented to evaluate the energy savings.
Optimum Scheduling for Orthogonal Multiple Access over Fading Channels using Quantized Channel State Information
(2008-07-01T10:38:31Z) Garcia Marques, Antonio; Giannakis, Georgios B.; Ramos, Javier
The efficiency of multi-access communications over wireless fading links benefits from channel-adaptive allocation of the available bandwidth and power resources. Different from most existing approaches that allocate resources based on perfect channel state information (P-CSI), this work optimizes channel scheduling and resource allocation over orthogonal fading channels when user terminals and the scheduler rely on quantized channel state information (Q-CSI). The novel unifying approach optimizes an average transmit-performance criterion subject to average quality of service requirements. The resultant optimal policy per fading realization either allocates the entire channel to a single (winner) user, or, to a small group of winner users whose percentage of shared resources is found by solving a linear program. Both alternatives become possible by smoothing the allocation scheme. The smooth policy is asymptotically optimal and incurs reduced computational complexity.
Patrones Eficientes de Pilotos en Sistemas OFDM para Canales Inalámbricos Selectivos en Tiempo y Frecuencia
(2005-07-01T07:45:41Z) Garcia Marques, Antonio; Morgado, Eduardo; Cano, Alfonso; Caamaño, Antonio J.; Ramos, Javier
In coherent systems, when the channel is not known at the receiver, pilot-assisted techniques are needed to estimate the channel. Using OFDM, this paper overcomes the problem of designing such optimum pilot patterns that efficiently estimate doubly selective (in time and frequency)fading channels. We show that,decoupling time- and frequency- selectivity, the challenging process of estimating such channels can be seen as a two-dimensional sampling problem. We further propose efficient sampling patterns depending on the spreading (multipath and Doppler) function of the channel.
Power Control for Cooperative Dynamic Spectrum Access Networks with Diverse QoS Constraints
(2009-07-29T13:11:04Z) Gatsis, Nikolaos; Garcia Marques, Antonio; Giannakis, Georgios B.
Dynamic spectrum access (DSA) is an integral part of cognitive radio technology aiming at efficient management of the available power and bandwidth resources. The present paper deals with cooperative DSA networks, where collaborating terminals adhere to diverse (maximum and minimum) quality-of-service (QoS) constraints in order to not only effect hierarchies between primary and secondary users but also prevent abusive utilization of the available spectrum. Peer-to-peer networks with co-channel interference are considered in both single- and multi-channel settings. Utilities that are functions of the signal-tointerference- plus-noise-ratio (SINR) are employed as QoS metrics. By adjusting their transmit power, users can mitigate the generated interference and also meet the QoS requirements. A novel formulation accounting for heterogeneous QoS requirements is obtained after introducing a suitable relaxation and recasting a constrained sum-utility maximization as a convex optimization problem. The optimality of the relaxation is established under general conditions. Based on this relaxation, an algorithm for optimal power control that is amenable to distributed implementation is developed, and its convergence is established. Numerical tests verify the analytical claims and demonstrate performance gains relative to existing schemes.
Power-Efficient OFDM via Quantized Channel State Information
(2006-06-01T10:47:55Z) Garcia Marques, Antonio; Digham, Fadel F.; Giannakis, Georgios B.
In response to the growing demand for low-cost low-power wireless sensor networks and related applications,we develop bit and power loading algorithms that minimize transmit-power for orthogonal frequency division multiplexing (OFDM) under rate and error probability constraints.Our novel algorithms exploit one of three types of channel state information at the transmitter (CSIT): deterministic (per channel realization)for slow fading links, statistical (channel mean) for fast fading links, and quantized (Q-) CSIT whereby a limited number of bits are fed back from the transmitter to the receiver. By adopting average transmit-power as a distortion metric, a channel quantizer is also designed to obtain a suitable form of Q-CSI. Numerical examples corroborate the analytical claims and reveal that significant power savings result even with a few bits of QCSIT.