Light-Emitting Diodes. (Monographs in Electrical and Electronic Engineering.) By A. A. Bergh and P. J. Dean. Pp. viii+591. (Clarendon: Oxford; Oxford University: London, 1976.) £22.

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Light-emitting diodes

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and learning problems from streaming data through localized interactions among agents. The results derived in this work are useful in comparing network topologies against each other, and in comparing networked solutions against centralized or batch implementations. There are many good reasons for the peaked interest in distributed implementations, especially in this day and age when the word "network" has become commonplace whether one is referring to social networks, power networks, transportation networks, biological networks, or other types of networks. Some of these reasons have to do with the benefits of cooperation in terms of improved performance and improved resilience to failure. Other reasons deal with privacy and secrecy considerations where agents may not be comfortable sharing their data with remote fusion centers. In other situations, the data may already be available in dispersed locations, as happens with cloud computing. One may also be interested in learning through data mining from big data sets. Motivated by these considerations, this work examines the limits of performance of distributed solutions and discusses procedures that help bring forth their potential more fully. The presentation adopts a useful statistical framework and derives performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks. At the same time, the work illustrates how distributed processing over graphs gives rise to some revealing phenomena due to the coupling effect among the agents. These phenomena are discussed in the context of adaptive networks, along with examples from a variety of areas including distributed sensing, intrusion detection, distributed estimation, online adaptation, network system theory, and machine learning.

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Adaptation, Learning, and Optimization Over Networks

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 to interact locally in response to streaming data and to continually learn and adapt to track drifts in the data and models. Under reasonable technical conditions on the data, the adaptive networks are shown to be mean square stable in the slow adaptation regime, and their mean square error performance and convergence rate are characterized in terms of the network topology and data statistical moments. Classical results for single-agent adaptation and learning are recovered as special cases. The performance results presented in this work are useful in comparing network topologies against each other, and in comparing adaptive networks against centralized or batch implementations. The presentation is complemented with various examples linking together results from various domains.

Adaptive Networks

We report a visible-light wireless point-to-point communication link operating at 513 Mbit/s gross transmission rate (net Mbit/s). The bit-error ratio of the uncoded data was smaller than for an illumination level of lx. The link was based on a commercial thin-film high-power phosphorescent white LED, an avalanche photo diode, and off-line signal processing of discrete multitone signals. Quadrature-amplitude modulation, bit- and power-loading, as well as symmetrical clipping were successfully employed in pushing the gross transmission rate beyond 500 Mbit/s. Adaptation of the clipping level increased the data rate only by 2%, while simulations predicted an enhancement of 20%. Obstacles towards higher data rates as well as potential remedies are discussed. We predicted that data rates of over 1 Gbit/s can be achieved with the same setup and under the same experimental conditions if these obstacles are overcome.

513 Mbit/s Visible Light Communications Link Based on DMT-Modulation of a White LED

Visible Light Communications : Theory and Applications

Adaptive algorithms for sparse system identification

Greedy algorithms form an essential tool for compressed sensing. However, their inherent batch mode discourages their use in time-varying environments due to significant complexity and storage requirements. In this paper two existing powerful greedy schemes developed in the literature are converted into an adaptive algorithm which is applied to estimation of a class of nonlinear communication systems. Performance is assessed via computer simulations on a variety of linear and nonlinear channels; all confirm significant improvements over conventional methods.

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An Adaptive Greedy Algorithm With Application to Nonlinear Communications

Light-emitting diodes (LEDs) constitute a low-cost alternative for optical data transmission of up to ~ 1 Gb/s. What differentiates such applications from, e.g., backhaul optical networks, is the fact that apart from their data throughput, LEDs are generally not as well characterized by the manufacturer as, for example, optical fiber amplifiers. While for simple modulation formats, this lack of knowledge is not a severe impediment; in any other situation, one may face rather complex behaviors of commercial LEDs. In this paper, the main electro-optical characteristics of LEDs are discussed, and it is shown that some popular simple nonlinear models available in the literature are inadequate in describing their dynamics. As a way out of this malady, we present a reverse-engineering approach that is based on Volterra expansions of the electro-optical characteristic function of LEDs, enabling the introduction of a realistic empirical model for commercial devices.

Empirical Volterra-Series Modeling of Commercial Light-Emitting Diodes

Input-output identification of nonlinear channels using PSK, QAM and OFDM inputs

In this correspondence, closed-form expressions for the blind identification of linear-quadratic Volterra systems are established. The system is excited by a complex valued random sequence and the output cumulants (of order up to 4) are employed. It is assumed that the memory of the linear part is greater than or equal to the memory of the quadratic part. Cumulant-based formulas are developed demonstrating that the system is uniquely identifiable. An SVD based variant with improved performance is also derived. Simulations and comparisons with existing techniques are presented.

Gerasimos Mileounis

Papers

Adaptive algorithms for sparse system identification

An Adaptive Greedy Algorithm With Application to Nonlinear Communications

Empirical Volterra-Series Modeling of Commercial Light-Emitting Diodes

Input-output identification of nonlinear channels using PSK, QAM and OFDM inputs

Blind Identification of Second Order Volterra Systems With Complex Random Inputs Using Higher Order Cumulants