Evangelos Vlachos

Low-Complexity OSIC Equalization for OFDM-Based Vehicular Communications

Vehicular communication systems are usually equipped with orthogonal frequency division multiplexing (OFDM) transceivers that operate on rapidly changing radio propagation environments, which results in high Doppler and delay spreads. More specifically, in these environments, the experienced channels are doubly selective and introduce severe intercarrier interference (ICI) at the receiver. An effective ICI mitigation technique is desired as a constituent part of an ordered successive interference cancellation (OSIC) architecture, which turns out to be computationally efficient, since it may require the solution of linear systems with multiple right-hand sides. To decrease the complexity, several techniques suggest mitigating the ICI by considering only a small number of adjacent subcarriers. However, this approximation introduces an error floor, which may result in unacceptable bit error rates (BER) at high signal-to-noise ratio regimes. In this paper, we propose a new OSIC equalization technique based on an iterative Galerkin projection-based algorithm that reduces the computational cost without sacrificing the performance gains of the OSIC architecture. Furthermore, we suggest a new serial/parallel cancellation architecture that extends the OSIC and has the potential to completely cancel the experienced ICI introduced in high-mobility scenarios. Extensive Monte Carlo experiments have been carried out to validate the accuracy of our framework, revealing intriguing tradeoffs between achieved BER and complexity, and highlighting the importance of designing low-complexity OSIC schemes for OFDM systems operating over double selective channels.

Regularized MMSE ICI equalization for OFDM systems over doubly selective channels

In this work, we consider a wireless OFDM system operating over doubly selective channels, where the Doppler effect destroys the orthogonality between subcarriers and hence, results into severe intercarrier interference (ICI). To mitigate this effect, computational demanding equalization schemes that require the inversion of the channel matrix, should be applied. In order to achieve linear complexity in the number of the subcarriers, a banded approximation of the channel matrix is usually adopted, whereas the performance of the equalizer is significantly degraded. To recover this performance loss, we propose a regularized estimation framework for MMSE ICI equalization in the frequency domain, where the complexity remains linear with respect to the number of the subcarriers. Simulation results verify the effectiveness of the proposed regularization.

Stochastic Gradient Pursuit for Adaptive Equalization of Sparse Multipath Channels

In this paper, a new heuristic algorithm for the sparse adaptive equalization problem, termed as stochastic gradient pursuit, is proposed. A decision-feedback equalization structure is used in order to effectively mitigate the effect of long multipath channels. Diverging from the commonly used approach of sparse channel identification, we exploit the sparsity of the inverse problem under the compressive sensing perspective. Also, an extension to the case where the sparsity order parameter is unknown, is developed. Simulation results verify that the proposed schemes exhibit faster convergence and improved tracking capabilities compared to conventional and other sparse aware equalization schemes, offering at the same time a reduced computational complexity.

Compressed Sensing for Efficient Encoding of Dense 3D Meshes Using Model-Based Bayesian Learning

With the growing demand for easy and reliable generation of 3D models representing real-world or synthetic objects, new schemes for acquisition, storage, and transmission of 3D meshes are required. In principle, 3D meshes consist of vertex positions and vertex connectivity. Vertex position encoders are much more resource demanding than connectivity encoders, stressing the need for novel geometry compression schemes. The design of an accurate and efficient geometry compression system can be achieved by increasing the compression ratio without affecting the visual quality of the object and minimizing the computational complexity. In this paper, we present novel compression/reconstruction schemes that enable aggressive compression ratios, without significantly reducing the visual quality. The encoding is performed by simply executing additions/subtractions. The benefits of the proposed method become more apparent as the density of the meshes increases, while it provides a flexible framework to trade efficiency for reconstruction quality. We derive a novel Bayesian learning algorithm that models the most significant graph Fourier transform coefficients of each submesh, as a multivariate Gaussian distribution. Then we evaluate iteratively the distribution parameters using the expectation-maximization approach. To improve the performance of the proposed approach in highly under determined problems, we exploit the local smoothness of the partitioned surfaces. Extensive evaluation studies, carried out using a large collection of different 3D models, show that the proposed schemes, as compared to the state-of-the-art approaches, achieve competitive compression ratios, offering at the same time significantly lower encoding complexity.

Supervised energy disaggregation using dictionary — based modelling of appliance states

In this paper, a supervised energy disaggregation method is proposed. The appliances that are monitored, are modelled by multi-state finite state machines. Each state of an appliance is described by exactly one vector of power consumptions from a carefully designed set of such vectors (called atoms), that comprise a dictionary. The latter is constructed during a training phase, where it is assumed that individual power consumption signals are available. A clustering algorithm is applied on overlapping patches extracted from the training signals to select a fixed number of representatives, i.e., the atoms of the dictionary. Moreover, in the training phase, an appropriate state transition probabilities matrix is constructed. During the operation phase, where the actual disaggregation task is performed, a trellis, with a reduced number of transitions, is used for the acquisition of the disaggregated power consumption signals per appliance. Numerical results, using the REDD dataset, are provided, in order to demonstrate the effectiveness of the proposed method.

Galerkin projections-based ICI cancellation in OFDM systems with doubly selective channels

Doubly selective channels can cause severe performance degradation in orthogonal frequency division multiplexing (OFDM) systems, introducing inter-carrier interference (ICI) at the receiver. In such cases, equalization schemes which require matrix inversion are prohibitively complex for large OFDM symbol lengths. In this paper, we propose two low-complexity iterative successive interference cancellation schemes, applying Krylov subspace optimization methods.We first derive a reduce-drank preconditioned conjugate gradient (PCG) algorithm in order to estimate the equalization matrix with a reduced number of iterations. We then develop an improved PCG algorithm with the same complexity order, using the Galerkin projections theory. As verified via simulations, the proposed schemes may offer near optimal performance with reduced computational complexity.

Compressed Sensing Techniques for Decision Feedback Equalization of Sparse Wireless Channels

In this paper new efficient decision feedback equalization (DFE) schemes for channels with long and sparse impulse responses are proposed. It has been shown that under reasonable assumptions concerning the channel impulse response (CIR) coefficients, the feedforward (FF) and feedback (FB) filters may be also approximated by sparse filters. Either the sparsity of the CIR, or the sparsity of the DFE filters may be exploited to derive efficient implementations of the DFE. To this end, compressed sampling (CS) approaches, already successful in system identification settings, can significantly improve the performance of the non sparsity aware DFE. Building on basis pursuit and matching pursuit techniques new DFE schemes are proposed that exhibit considerable computational savings, increased performance properties and short training sequence requirements. To investigate the performance of the proposed schemes the restricted isometry property in the common DFE setup is also investigated.

Blind distributed beamforming via matrix completion

In this paper, we consider the problem of distributed beamforming for maximization of the receiver signal-to-noise-ratio (SNR) subject to a total transmit power constraint. We investigate the case where the optimal beamforming weights are expressed based on the second-order statistics of the involved channels, while the communication among the relays is interference-limited. In this context, we propose a relay-cooperative scheme for interference minimization, where only a limited number of correlation quantities are sent to the fusion center (FC). We propose a technique which overcomes the problem of the incomplete covariance matrices via matrix completion. Through simulation results, we show that, after a number of iterations, the proposed technique converges to the true covariance matrices and thus the optimal beamformer may be computed.

Greedy algorithms for sparse adaptive decision feedback equalization

In this paper we propose two new adaptive decision feedback equalization (DFE) schemes for channels with long and sparse impulse responses. It has been shown that for a class of channels, and under reasonable assumptions concerning the DFE filter sizes, the feedforward (FF) and feedback (FB) filters possess also a sparse form. The sparsity form of both the channel impulse response (CIR) and the equalizer filters is properly exploited and two novel adaptive greedy schemes are derived. The first scheme is a channel estimation based one. In this scheme, the non-negligible taps of the involved CIR are first estimated via a new greedy algorithm, and then the FF and FB filters are adaptively computed by exploiting a useful relation between these filters and the CIR. The channel estimation part of this new technique is based on the steepest descent (SD) method and offers considerably improved performance as compared to other adaptive greedy algorithms that have been proposed. The second scheme is a direct adaptive sparse equalizer based on a SD-based greedy algorithm. Compared to non sparsity aware DFE, both of our schemes exhibit faster convergence, improved tracking capabilities and reduced complexity.

Efficient graph-based matrix completion on incomplete animated models

Recently, there has been increasing interest for easy and reliable generation of 3D animated models facilitating several real-time applications. In most of these applications, the reconstruction of soft body animations is based on time-varying point clouds which are irregularly sampled and highly incomplete. To overcome these imperfections, we introduce a novel reconstruction technique, using graph-based matrix completion approaches. The presented method exploits spatio-temporal coherences by implicitly forcing the proximity of the adjacent 3D points in time and space. The proposed constraints are modeled by using the weighted Laplacian graphs and are constructed from the available points. Extensive evaluation studies, carried out using a collection of different highly-incomplete dynamic models, verify that the proposed technique achieves plausible reconstruction output despite the constraints posed by arbitrarily complex and motion scenarios.