Santiago Zazo

DFT-based channel estimation in 2D-pilot-symbol-aided OFDM wireless systems

An efficient channel estimation algorithm for OFDM in wireless applications has been proposed. Two-dimensional pilot-symbol assisted modulation (2D-PSAM) is employed in coherent OFDM for channel estimation and it is based on inserting known symbols spreaded throughout the 2D time-frequency grid. These scattered symbols are employed to perform an estimation of the channels frequency response. At a first stage, if certain requirements are fulfilled, channel estimation in frequency dimension can be carried out with an efficient DFT-based algorithm, which provides an accurate estimation at time positions where pilot symbols are included. In the second step, linear interpolation is performed in the time direction. This scheme has been proven to be very successful in multicarrier HF communications systems.

A Fitting Algorithm for Random Modeling the PLC Channel

The characteristics of the power-line communication (PLC) channel are difficult to model due to the heterogeneity of the networks and the lack of common wiring practices. To obtain the full variability of the PLC channel, random channel generators are of great importance for the design and testing of communication algorithms. In this respect, we propose a random channel generator that is based on the top-down approach. Basically, we describe the multipath propagation and the coupling effects with an analytical model. We introduce the variability into a restricted set of parameters and, finally, we fit the model to a set of measured channels. The proposed model enables a closed-form description of both the mean path-loss profile and the statistical correlation function of the channel frequency response. As an example of application, we apply the procedure to a set of in-home measured channels in the band 2-100 MHz whose statistics are available in the literature. The measured channels are divided into nine classes according to their channel capacity. We provide the parameters for the random generation of channels for all nine classes, and we show that the results are consistent with the experimental ones. Finally, we merge the classes to capture the entire heterogeneity of in-home PLC channels. In detail, we introduce the class occurrence probability, and we present a random channel generator that targets the ensemble of all nine classes. The statistics of the composite set of channels are also studied, and they are compared to the results of experimental measurement campaigns in the literature.

Consensus-based distributed principal component analysis in wireless sensor networks

Principal component analysis is a powerful technique for data analysis and compression, with a wide range of potential applications in wireless sensor networks. However, its centralized implementation, with a fusion center collecting all the samples, is inefficient in terms of energy consumption, scalability, and fault tolerance. Previous distributed approaches reduce the communication cost, but not the lack of flexibility, as they require multi-hop communications if the network is not fully connected. We present two fully distributed consensus-based algorithms that are guaranteed to converge to the global results, using only local communications among neighbors, regardless of the data distribution or the sparsity of the network: CB-DPCA is based on finding the eigenvectors of local covariance matrices, while CB-EM-DPCA is a distributed version of the expectation maximization algorithm. Both offer a flexible trade-off between the tightness of the achieved approximation and the associated communication cost.

Efficient pilot patterns for channel estimation in OFDM systems over HF channels

In this paper, the distribution of pilot symbols in the time-frequency lattice of a multicarrier system for HF communications has been analysed. The pilot density of an OFDM modem based on the HFDL standard can be reduced, so the data rate can be about 3600 bit/s. An hexagonal pilot pattern has been proposed and compared with rectangular geometries. Hexagonal distribution of the pilots is optimum in terms of sampling efficiency and it provides better performance in terms of BER.

Belief consensus algorithms for fast distributed target tracking in wireless sensor networks

In distributed target tracking for wireless sensor networks, agreement on the target state can be achieved by the construction and maintenance of a communication path, in order to exchange information regarding local likelihood functions. Such an approach lacks robustness to failures and is not easily applicable to ad-hoc networks. To address this, several methods have been proposed that allow agreement on the global likelihood through fully distributed belief consensus (BC) algorithms, operating on local likelihoods in distributed particle filtering (DPF). However, a unified comparison of the convergence speed and communication cost has not been performed. In this paper, we provide such a comparison and propose a novel BC algorithm based on belief propagation (BP). According to our study, DPF based on metropolis belief consensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensus is the fastest in tree graphs. Moreover, we found that BC-based DPF methods have lower communication overhead than data flooding when the network is sufficiently sparse.

Cooperative localization in mobile networks using nonparametric variants of belief propagation

Of the many state-of-the-art methods for cooperative localization in wireless sensor networks (WSNs), only very few adapt well to mobile networks. The main problems of the well-known algorithms, based on nonparametric belief propagation (NBP), are the high communication cost and inefficient sampling techniques. Moreover, they either do not use smoothing or just apply it offline. Therefore, in this article, we propose more flexible and efficient variants of NBP for cooperative localization in mobile networks. In particular, we provide: (i) an optional 1-lag smoothing done almost in real-time, (ii) a novel low-cost communication protocol based on package approximation and censoring, (iii) higher robustness of the standard mixture importance sampling (MIS) technique, and (iv) a higher amount of information in the importance densities by using the population Monte Carlo (PMC) approach, or an auxiliary variable. Through extensive simulations, we confirmed that all the proposed techniques outperform the standard NBP method.

Reducing Communication Overhead for Cooperative Localization Using Nonparametric Belief Propagation

A number of methods for cooperative localization has been proposed, but most of them provide only location estimate, without associated uncertainty. On the other hand, nonparametric belief propagation (NBP), which provides approximated posterior distributions of the location estimates, is expensive mostly because of the transmission of the particles. In this paper, we propose a novel approach to reduce communication overhead for cooperative positioning using NBP. It is based on: i) communication of the beliefs (instead of the messages), ii) approximation of the belief with Gaussian mixture of very few components, and iii) censoring. According to our simulations results, these modifications reduce significantly communication overhead while providing the estimates almost as accurate as the transmission of the particles.

Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Energy efficiency is a major design issue in the context of Wireless Sensor Networks (WSN). If the acquired data is to be sent to a far-away base station, collaborative beamforming performed by the sensors may help to distribute the communication load among the nodes and to reduce fast battery depletion. However, collaborative beamforming techniques are far from optimality and in many cases we might be wasting more power than required. We consider the issue of energy efficiency in beamforming applications. Using a convex optimization framework, we propose the design of a virtual beamformer that maximizes the network lifetime while satisfying a pre-specified Quality of Service (QoS) requirement. We derive both centralized and distributed algorithms for the solution of the problem using convex optimization and consensus algorithms. In order to account for other sources of battery depletion different from that of communications beamforming, we consider an additional random energy term in the consumption model. The formulation then switches to a probabilistic design that generalizes the deterministic case. Conditions under which the general problem is convex are also provided.

Cattle-Powered Node Experience in a Heterogeneous Network for Localization of Herds

A heterogeneous network, mainly based on nodes that use harvested energy to self-energize, is presented, and its use is demonstrated. The network, mostly kinetically powered, has been used for the localization of herds in grazing areas under extreme climate conditions. The network consists of secondary and primary nodes. The former, powered by a kinetic generator, take advantage of animal movements to broadcast a unique identifier. The latter are battery-powered and gather secondary-node-transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. Because a limited human interaction is desirable, the aim of this network is to reduce the battery count of the system.

Indoor positioning using nonparametric belief propagation based on spanning trees

Nonparametric belief propagation (NBP) is one of the best-known methods for cooperative localization in sensor networks. It is capable of providing information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based on spanning trees (NBP-ST) created by breadth first search (BFS) method. In addition, we propose a reliable indoor model based on obtained measurements in our lab. According to our simulation results, NBP-ST performs better than NBP in terms of accuracy and communication cost in the networks with high connectivity (i.e., highly loopy networks). Furthermore, the computational and communication costs are nearly constant with respect to the transmission radius. However, the drawbacks of proposed method are a little bit higher computational cost and poor performance in low-connected networks.