Gerasimos Mileounis

Adaptive algorithms for sparse system identification

In this paper, identification of sparse linear and nonlinear systems is considered via compressive sensing methods. Efficient algorithms are developed based on Kalman filtering and Expectation-Maximization. The proposed algorithms are applied to linear and nonlinear channels which are represented by sparse Volterra models and incorporate the effect of power amplifiers. Simulation studies confirm significant performance gains in comparison to conventional non-sparse methods.

An Adaptive Greedy Algorithm With Application to Nonlinear Communications

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.

Empirical Volterra-Series Modeling of Commercial Light-Emitting Diodes

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.

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

Nonparametric identification of baseband and passband complex Volterra systems excited by communication inputs (phase shift keying, PSK; quadrature amplitude modulation, QAM and OFDM) is considered. Closed form expressions are established using multivariate orthogonal polynomials and higher order statistics. First multivariate orthogonal polynomials are used for baseband and passband Volterra models driven by PSK and QAM inputs and closed form expressions are derived. For baseband Volterra models excited with i.i.d. complex Gaussian signals (OFDM), the general 2p+1 order Volterra system is solved using cross-cumulants in time and frequency domain. An order recursive algorithm is presented for the latter case, that does not require a priori knowledge of the systems order. Performance is illustrated by simulations.

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

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.

Adaptive algorithms for sparse nonlinear channel estimation

In this paper, we consider the estimation of sparse nonlinear communication channels. Transmission over the channels is represented by sparse Volterra models that incorporate the effect of Power Amplifiers. Channel estimation is performed by compressive sensing methods. Efficient algorithms are proposed based on Kalman filtering and Expectation Maximization. Simulation studies confirm that the proposed algorithms achieve significant performance gains in comparison to the conventional non-sparse methods.

A greedy sparsity-promoting LMS for distributed adaptive learning in diffusion networks

In this paper, a distributed adaptive algorithm for sparsity-aware learning in diffusion networks is developed. The algorithm follows the greedy roadmap for sparsity along with the adapt-combine co-operation strategy, based on the LMS rationale for adaptivity. A bound on the error norm between the obtained estimates and the target vector is computed, and the algorithm is shown to converge in the mean under some general assumptions. Finally, comparative experiments with a recently developed sparsity-promoting diffusion LMS demonstrate the enhanced performance of the proposed algorithm.

A sparsity driven approach to cumulant based identification and order determination

The area of blind system identification using Higher-Order-Statistics has gained considerable attention over the last two decades. This paper, motivated by the recent developments in sparse approximations, proposes new algorithms for the blind identification and order determination of sparse systems. The methodology used relies on greedy schemes. In particular, the first algorithm exploits a single step greedy structure, while the second improves performance using a threshold-based selection procedure. Finally, the proposed algorithms are tested on a wide range of randomly generated channels and different output signal lengths.

Blind identification of sparse channels and symbol detection via the EM algorithm

In this paper, we address the problem of blind identification of sparse channels. For this purpose, the Expectation- Maximization is modified to accommodate channel sparsity. The resulting algorithm is applicable for linear and nonlinear channels. Computer simulations on various channel set ups illustrate that the proposed algorithm achieves performance close to the genie-aided estimator.

Greedy Sparsity-Promoting Algorithms for Distributed Learning

This paper focuses on the development of novel greedy techniques for distributed learning under sparsity constraints. Greedy techniques have widely been used in centralized systems due to their low computational requirements and at the same time their relatively good performance in estimating sparse parameter vectors/signals. The paper reports two new algorithms in the context of sparsity-aware learning. In both cases, the goal is first to identify the support set of the unknown signal and then to estimate the nonzero values restricted to the active support set. First, an iterative greedy multistep procedure is developed, based on a neighborhood cooperation strategy, using batch processing on the observed data. Next, an extension of the algorithm to the online setting, based on the diffusion LMS rationale for adaptivity, is derived. Theoretical analysis of the algorithms is provided, where it is shown that the batch algorithm converges to the unknown vector if a Restricted Isometry Property (RIP) holds. Moreover, the online version converges in the mean to the solution vector under some general assumptions. Finally, the proposed schemes are tested against recently developed sparsity-promoting algorithms and their enhanced performance is verified via simulation examples.