Jorge Plata-Chaves

Distributed Incremental-Based LMS for Node-Specific Adaptive Parameter Estimation

We introduce an adaptive distributed technique that is suitable for parameter estimation in a network where nodes have different but overlapping interests. At each node, the parameters to be estimated can be of local interest, global interest to the whole network and common interest to a subset of nodes. To estimate each set of local, common and global parameters, a least mean squares (LMS) algorithm is implemented under an incremental mode of cooperation. Coupled with the estimation of the different sets of parameters, the implementation of each LMS algorithm is only undertaken by the nodes of the network interested in a specific set of local, common or global parameters. Besides obtaining the conditions under which the proposed strategy converges in the mean to the solution of a centralized unit that processes all the observations, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node across the network. Finally, the theoretical results are validated through generic computer simulations as well as simulation results in the context of cooperative spectrum sensing in cognitive radio networks.

Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where the nodes are interested in estimating parameters that can be of local interest, common interest to a subset of nodes and global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different, yet coupled Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.

Distributed diffusion-based LMS for node-specific parameter estimation over adaptive networks

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local or global parameters. Although all the different LMS algorithms are coupled, the diffusion-based implementation of each LMS algorithm is exclusively undertaken by the nodes of the network interested in a specific set of local or global parameters. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.

Distributed incremental-based LMS for node-specific parameter estimation over adaptive networks

We introduce an adaptive distributed technique that is suitable for node-specific parameter estimation in an adaptive network where each node is interested in a set of parameters of local interest as well as a set of network global parameters. The estimation of each set of parameters of local interest is undertaken by a local Least Mean Squares (LMS) algorithm at each node. At the same time and coupled with the previous local estimation processes, an incremental mode of cooperation is implemented at all nodes in order to perform an LMS algorithm which estimates the parameters of global interest. In the steady state, the new distributed technique converges to the MMSE solution of a centralized processor that is able to process all the observations. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.

Blind Interference Alignment for Cellular Networks

We propose a blind interference alignment scheme for partially connected cellular networks. The scheme cancels both intracell and intercell interference by relying on receivers with one reconfigurable antenna and by allowing users at the cell edge to be served by all the base stations in their proximity. An outer bound for the degrees of freedom is derived for general partially connected networks with single-antenna receivers when knowledge of the channel state information at the transmitter is not available. It is demonstrated that for symmetric scenarios, this outer bound is achieved by the proposed scheme. On the other hand, for asymmetric scenarios, the achievable degrees of freedom are not always equal to the outer bound. However, the penalty is typically small, and the proposed scheme outperforms other blind interference alignment schemes. Moreover, significant reduction of the supersymbol length is achieved compared with a standard blind interference alignment strategy designed for fully connected networks.

Unsupervised diffusion-based LMS for node-specific parameter estimation over wireless sensor networks

We study a distributed node-specific parameter estimation problem where each node in a wireless sensor network is interested in the simultaneous estimation of different vectors of parameters that can be of local interest, of common interest to a subset of nodes, or of global interest to the whole network. We assume a setting where the nodes do not know which other nodes share the same estimation interests. First, we conduct a theoretical analysis on the asymptotic bias that results in case the nodes blindly process all the local estimates of all their neighbors to solve their own node-specific parameter estimation problem. Next, we propose an unsupervised diffusion-based LMS algorithm that allows each node to obtain unbiased estimates of its node-specific vector of parameters by continuously identifying which of the neighboring local estimates correspond to each of its own estimation tasks. Finally, simulation experiments illustrate the efficiency of the proposed strategy.

On the choice of blind interference alignment strategy for cellular systems with data sharing

A cooperative blind interference alignment (BIA) strategy is considered for the downlink of cellular systems. The aim is to reduce intercell interference in order to protect users, especially at the cell edge. The strategy consists of appropriately splitting the available bandwidth and is shown to be well-suited to scenarios where the number of cell-edge users is considerable. For a system comprising two cells each with a base station of Nt antennas, it is shown that, compared to a previous augmented code approach where transmission to all users occurs in the same frequency band, the proposed strategy leads to better rates over a wide range of signal-to-noise ratios when the number of cell-edge users in both cells exceeds 2Nt -1.

Heterogeneous and Multitask Wireless Sensor Networks—Algorithms, Applications, and Challenges

Unlike traditional homogeneous single-task wireless sensor networks (WSNs), heterogeneous and multitask WSNs allow the cooperation among multiple heterogeneous devices dedicated to solving different signal processing tasks. Despite their heterogenous nature and the fact that each device may solve a different task, the devices could still benefit from a collaboration between them to achieve a superior performance. However, the design of such heterogeneous WSNs is very challenging and requires the design of scalable algorithms that maximize the performance of the devices without transmitting their raw sensor signals in an uncontrolled fashion. Towards this goal, novel techniques are needed both on the signal processing level and on the network-communication level. In this paper, we give an overview of applications in the field of heterogeneous and multitask WSNs with special focus on the signal processing aspects. Moreover, we provide a general overview of the existing algorithms for distributed node-specific estimation. Finally, we discuss the main challenges that have to be tackled for the design of heterogeneous multitask WSNs.

Incremental multiple error filtered-X LMS for node-specific active noise control over wireless acoustic sensor networks

We propose an adaptive distributed algorithm to solve a node-specific Active Noise Control (ANC) problem. In this novel ANC problem, the nodes estimate different but overlapping ANC filters in order to generate secondary signals that cancel a primary noise source as it impinges on their microphones. Different sets of nodes follow a cyclic mode of cooperation in order to implement several coupled Multiple Error Filtered-X Least Mean Squares algorithms, each responsible for the estimation of part of the different node-specific ANC filters. The proposed algorithm outperforms the non-cooperative strategies and achieves the same steady-state noise reduction as a centralized solution that processes all the signals in the network. Finally, computer simulations are provided to illustrate the effectiveness of the proposed algorithm.

Distributed signal estimation in a wireless sensor network with partially-overlapping node-specific interests or source observability

We study a distributed node-specific signal estimation problem where the node-specific desired signals and/or the sensor observations can have partially-overlapping latent signal subspaces. First, we provide the minimum number of linear combinations of observed sensor signals that each node can broadcast to still let all other nodes achieve the network-wide Linear Minimum Mean-Square Error (LMMSE) estimate of their node-specific desired signals. Later, for a fully-connected wireless sensor network, we derive a distributed algorithm that, under some settings, allows each node to achieve the LMMSE estimate of its node-specific desired signals by broadcasting the smallest number of signals. Unlike the existing algorithms, the proposed algorithm deals with the problem of partially-overlapping node-specific interests and incomplete observability of all latent sources at the nodes. Finally, the effectiveness of the proposed technique is shown through numerical simulations.