Ramón Parra-Michel

Estimation of Fast Time-Varying Channels in OFDM Systems Using Two-Dimensional Prolate

Modern communication systems are based on orthogonal frequency division multiplexing (OFDM). They are designed for dealing with frequency selective channels considering invariance within the time-span of an OFDM symbol. However, this assumption is no longer valid when the transceivers operate in higher mobility scenarios or higher carrier frequencies. This condition provokes inter-carrier interference (ICI) that greatly degrades system performance. State-of-the-art approaches that satisfactorily mitigate this problem have a complexity of O(N3), which makes them infeasible with current technology. In this paper, a novel channel estimation algorithm to cope with this problem is presented. It is based on a subspace approach using two-dimensional Prolate functions, achieving a complexity of only O(N2). It depends only on the maximum delay spread and maximum Doppler spread while being robust in the sense that it is independent of the particular channel scattering function. Performance analysis of the proposed algorithm is presented. Simulation results under the WiMAX standard show that this algorithm improves previous results, achieving a bit error rate (BER) close to the one obtained with perfect channel state information (CSI) in very-fast transceiver mobility, as high as 874 Km/h over a 2.4 Ghz carrier frequency.

Enhanced Channel Estimation Using Superimposed Training Based on Universal Basis Expansion

In this correspondence, an approach to enhance the quality of superimposed training (ST) based channel estimation procedures is proposed. The approach is based on postprocessing the estimated channel. This postprocessing is performed with the projection of the estimated channel onto a set of orthogonal functions known as the Universal Basis (UB), that were defined in [A. G. Orozco-Lugo, R. Parra-Michel, D. McLernon, and V. Kontorovitch, ldquoEnhancing the Performance of the CR Blind Channel Estimation Algorithm Using the Karhunen-Loeve Expansion,rdquo Proceedings of the ICASSP, May 2002, pp. III-2653-III-2656 ]. The projection leads to improved channel estimation when compared to raw ST methods. We demonstrate the enhanced performance of the proposed technique by means of both theoretical formulas and simulation results, focusing on data dependent ST.

Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization and Superimposed Training

In this correspondence, a method is presented for estimating double-selective channels using superimposed training (ST). The estimator is based on a subspace projection of the time-varying channel onto a set of two dimensional orthogonal functions. These functions are formed via the outer product of the discrete prolate spheroidal basis vectors and the universal basis vectors. This approach allows the channel to be expanded in both the time-delay and time dimensions with the fewest parameters when incomplete channel statistics are given. This correspondence also provides a theoretical performance analysis of the estimation algorithm and its corroboration via simulations. It is shown that this new method provides an enhancement in channel estimation when compared with state-of-the-art approaches.

Linear Dispersion Codes Generation from Hybrid STBC-VBLAST Architectures

Hybrid MIMO systems are defined as a combination of architectures designed to achieve both multiplexing gain (such as VBLAST), and diversity gain, (such as STBC). In these systems the detection can be performed with an ordered successive interference cancellation (OSIC) based on SQRD algorithm, in which the layers with diversity are decoded first. In this paper we introduce a new way to represent hybrid systems, in which the detection process is carried out in a unified manner for both spatial and diversity transmitted symbols, using an OSIC algorithm, but symbol by symbol, just as single VBLAST systems performs. We show that within this approach, linear dispersion (LD) coding has been actually generated, that allows the use of LD theory to determine the performance achieved by the hybrid system. Therefore a bridge between hybrid systems and LD codes has been established.

Frequency-Selective Joint Tx/Rx I/Q Imbalance Estimation Using Golay Complementary Sequences

The increasing use of smaller technologies in the manufacture of analog front-ends (AFEs) for communication systems has increased the impact their non-ideal components produce. This results in a significant degradation of the system performance that must be identified and addressed. In particular, the I/Q imbalance is commonly estimated and compensated via digital signal processing techniques using training sequences. In order to preclude the rise of other non-idealities, such as the non-linearity of the power amplifier (PA), these training sequences should be chosen to have a low peak-to-average power ratio (PAPR). In addition, it is desirable that these techniques have reduced computational complexity for minimizing estimation times and area resources. This paper presents a novel I/Q imbalance estimation algorithm that is computationally simple, it only requires adders and shifters, while exhibiting a PAPR ≤ 2. It can include the transmitter and receiver I/Q imbalances as well as the multipath phenomena. It is based on a newly used property of Golay complementary sequences (GCS). The statistical efficiency and low complexity of the proposed algorithm are proved, while its flexibility is illustrated under several extreme test cases.

High payload data-hiding in audio signals based on a modified OFDM approach

This paper presents a high payload data-hiding scheme for audio signals following the OFDM concept. It is based on changing the phase component of the audio signal via a reduced-arc M-order Phase Shift Keying (MPSK) modulator on selected frequency samples of the audio signal. This approach allows the data-hiding payload and perceptual distortion to be controlled by the number of altered frequency components of the audio signal and the severity of this alteration. The data-hiding payload can be estimated a priori independently of the audio signal features. In addition, due to the relation of the proposed scheme with the traditional use of MPSK modulation, the BER of the encoded data is analytically derived, therefore, it is known in advance. This allows the transmission of reliable hidden data, in contrast to previous works. Results obtained with representative test cases show that the proposed algorithm provides an increase of one order of magnitude in data payload compared with state-of-the-art works.

Simulation of Wide Band Channels with non-separable scattering functions

In this paper a new and constructive method useful to simulate Wide Band Channels with non-separable scattering function via the filtering approach is provided. This method is based on the approximation of the Channel by the eigenfunctions taken from the Karhunen-Loève expansion of the Channels. ensemble autocorrelation function at Δt=0. Channel realizations are performed via the filtering of an independent gaussian random process vector by some filter matrix, which is obtained from some manipulations of the correlation statistics of eigenfunctions. An example to clarify this proposal is also presented.

Estimation of Correlated MIMO Channels using Partial Channel State Information and DPSS

A novel approach is proposed for correlated multiple-input multiple-output (MIMO) channel estimation based on reduced-rank (RR) technique and partial channel state information (CSI). In contrast to previous proposals that used the channel correlation matrix (CCM) and its eigendecomposition, this paper shows that close linear minimum mean-square-error (LMMSE) performance can be achieved with the use of predefined bases derived from the knowledge of the maximum angular dispersion. A theoretical framework to synthesize a suitable set of bases is provided, from which discrete prolate spheroidal sequences (DPSSs) are identified as one of the appropriate predefined bases for spatial channel representation. The robustness of the proposed estimator allows changes in the propagation scenario to be managed according to the demands of realistic communications systems. The performance analysis of the channel estimator is shown and corroborated with simulation results.

Design and Implementation of a Configurable Interleaver/Deinterleaver for Turbo Codes in 3GPP Standard

During the last decade, Turbo codes have been taking an increasing importance in channel coding due to its good performance in error correction. One key component in Turbo codes is the interleaver/deinterleaver pair, often designed as reconfigurable coprocessors able to deal with requirements of large data length variability found in the newest communication standards. In this work we introduce a configurable interleaver architecture for the turbo decoder in 3rd Generation Partnership Project (3GPP) standard. It is implemented under the idea of “iterative modulo computation”. Additionally, the presented solution not only generates the interleaved addresses, but also deals with the flow of data streams through the interleaver. The architecture and FPGA implementation results are also presented.

Iterative Mean Removal Superimposed Training for SISO and MIMO Channel Estimation

This contribution describes a novel iterative radio channel estimation algorithm based on superimposed training (ST) estimation technique. The proposed algorithm draws an analogy with the data dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receivers end. We first demonstrate that this mean removal ST (MRST) applied to estimate a single-input single-output (SISO) wideband channel results in similar bit error rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, we jointly use the MRST and Alamouti coding to obtain an estimate of the multiple-input multiple-output (MIMO) narrowband radio channel. The impact of imperfect channel on the BER performance is evidenced by a comparison between the MRST method and the best iterative techniques found in the literature. The proposed algorithm shows a good tradeoff performance between complexity, channel estimation error, and noise immunity.