<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We consider the problem of distributed estimation, where a set nodes is required to collectively estimate some parameter interest from noisy measurements. The useful in several contexts including wireless and sensor networks, scalability, robustness, low power consumption are desirable features. Diffusion cooperation schemes have been shown provide good performance, robustness node link failure,...

We formulate and study distributed estimation algorithms based on diffusion protocols to implement cooperation among individual adaptive nodes. The nodes are equipped with local learning abilities. They derive estimates for the parameter of interest share information their neighbors only, giving rise peer-to-peer protocols. resulting algorithm is distributed, cooperative able respond in real time changes environment. It improves performance terms transient steady-state mean-square error, as...

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users opportunistically access under-utilized frequency bands. Spectrum sensing, a key enabling functionality in networks, needs reliably detect signals from licensed primary avoid harmful interference. However, due effects of channel fading/shadowing, individual may not be...

In multiuser MIMO downlink communications, it is necessary to design precoding schemes that are able suppress co-channel interference. This paper proposes designing precoders by maximizing the so-called signal-to-leakage-and-noise ratio (SLNR) for all users simultaneously. The presentation considers communications with both single- and multi-stream cases, as well systems employ Alamouti coding. effect of channel estimation errors on system performance also studied. Compared zero-forcing...

We study the problem of distributed Kalman filtering and smoothing, where a set nodes is required to estimate state linear dynamic system from in collaborative manner. Our focus on diffusion strategies, communicate with their direct neighbors only, information diffused across network through sequence iterations data-aggregation. problems filtering, fixed-lag smoothing fixed-point propose algorithms solve each one these problems. analyze mean mean-square performance proposed algorithms,...

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and opportunistically use under-utilized frequency bands without causing harmful interference legacy (primary) networks. In this paper, a novel wideband spectrum technique referred as <emphasis emphasistype="boldital">multiband joint detection</emphasis> introduced, which jointly detects the...

An adaptive distributed strategy is developed based on incremental techniques. The proposed scheme addresses the problem of linear estimation in a cooperative fashion, which nodes equipped with local computing abilities derive estimates and share them their predefined neighbors. resulting algorithm distributed, cooperative, able to respond real time changes environment. Each node allowed communicate its immediate neighbor order exploit spatial dimension while limiting communications burden...

We propose an adaptive diffusion mechanism to optimize a global cost function in distributed manner over network of nodes. The is assumed consist collection individual components. Diffusion adaptation allows the nodes cooperate and diffuse information real-time; it also helps alleviate effects stochastic gradient noise measurement through continuous learning process. analyze mean-square-error performance algorithm some detail, including its transient steady-state behavior. apply two...

We study the problem of distributed estimation over adaptive networks where a collection nodes are required to estimate in collaborative manner some parameter interest from their measurements. The centralized solution uses fusion center, thus, requiring large amount energy for communication. Incremental strategies that obtain global have been proposed, but they require definition cycle through network. propose diffusion recursive least-squares algorithm need communicate only with closest...

This letter proposes two new variable step-size algorithms for normalized least mean square and affine projection. The proposed schemes lead to faster convergence rate lower misadjustment error.

This paper surveys recent advances related to adaptation, learning, and optimization over networks. Various distributed strategies are discussed that enable a collection of networked agents interact locally in response streaming data continually learn adapt track drifts the models. Under reasonable technical conditions on data, adaptive networks shown be mean square stable slow adaptation regime, their error performance convergence rate characterized terms network topology statistical...

Nature provides splendid examples of real-time learning and adaptation behavior that emerges from highly localized interactions among agents limited capabilities. For example, schools fish are remarkably apt at configuring their topologies almost instantly in the face danger [1]: when a predator arrives, entire school opens up to let through then coalesces again into moving body continue its schooling behavior. Likewise, bee swarms, only small fraction (about 5%) informed, these informed...

This paper addresses the problem of channel tracking and equalization for multi-input multi-output (MIMO) time-varying frequency-selective channels. These channels model effects inter-symbol interference (ISI), co-channel (CCI), noise. A low-order autoregressive approximates MIMO variation facilitates via a Kalman filter. Hard decisions to aid come from finite-length minimum-mean-squared-error decision-feedback equalizer (MMSE-DFE), which performs task. Since optimum DFE wide range produces...

Adaptive networks consist of a collection nodes with adaptation and learning abilities. The interact each other on local level diffuse information across the network to solve estimation inference tasks in distributed manner. In this work, we compare mean-square performance two main strategies for over networks: consensus diffusion strategies. analysis paper confirms that under constant step-sizes, allow more thoroughly through property has favorable effect evolution network: are shown...

In this survey paper, we describe how strands of work that are important in two different fields, matrix theory and complex function theory, have come together some on fast computational algorithms for matrices with what call displacement structure. particular, a triangularization procedure can be developed such matrices, generalizing striking way an algorithm presented by Schur (1917) [J. Reine Angew. Math., 147 (1917), pp. 205–232] paper checking when power series is bounded the unit disc....

Adaptive filtering algorithms fall into four main groups: recursive least squares (RLS) and the corresponding fast versions; QR- inverse QR-least algorithms; lattice (LSL) QR decomposition-based (QRD-LSL) gradient-based such as least-mean square (LMS) algorithm. Our purpose in this article is to present yet another approach, for sake of achieving two important goals. The first one show how several different variants least-squares algorithm can be directly related widely studied Kalman...

Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we study the mean-square performance of a convex combination two transversal filters. The individual filters are independently adapted using their own error signals, while is by means stochastic gradient algorithm in order minimize overall structure. General expressions derived that show method universal with respect component filters, i.e., steady-state, it performs at least as well best...

Cognitive radio (CR) has recently emerged as a promising technology to revolutionize spectrum utilization in wireless communications. In CR network, secondary users continuously sense the spectral environment and adapt transmission parameters opportunistically use available spectrum. A fundamental problem for CRs is sensing; need reliably detect weak primary signals of possibly different types over targeted wide frequency band order identify holes opportunistic Conceptually practically,...