Mounir Ghogho

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST).

Optimized null-subcarrier selection for CFO estimation in OFDM over frequency-selective fading channels

We address the problem of frequency synchronization in OFDM-based communications systems in the context of frequency-selective fading channels. Frequency offsets are estimated by inserting null sub-carriers into a single OFDM block. The paper clarifies issues related to acquisition range and identifiability of carrier frequency offset (CFO), and performance of estimators. A deterministic maximum likelihood estimation approach is adopted. We derive necessary and sufficient conditions on the number of null-subcarriers and their placement in order to ensure identifiability. The Cramer-Rao bound (CRB) for the CFO is derived; for a given number of null sub-carriers, the optimal placement which minimizes the CRB is derived. We show that if the number of null sub-carriers is less than half the total number of sub-carriers, performance is optimal when the null sub-carriers are equispaced.

Secure Communication via Sending Artificial Noise by the Receiver: Outage Secrecy Capacity/Region Analysis

A novel approach for ensuring confidential wireless communication is proposed and analyzed from an information-theoretic standpoint. In this approach, the legitimate receiver generates artificial noise (AN) to impair the intruders channel. This method is robust because it does not need the feedback of channel state information (CSI) to the transmitter and does not assume that the number of Eves antennas should be smaller than that of Bob. Furthermore, we propose a new concept of outage secrecy region to evaluate the secrecy performance from a geometrical perspective. This should be useful if we need to know what zone should be protected (or militarized). Analysis and simulation results in practical environments show that the proposed method has a good performance.

Training Design for Multipath Channel and Frequency-Offset Estimation in MIMO Systems

This paper addresses the problem of training design for frequency-selective channel and carrier frequency-offset (CFO) estimation in single- and multiple-antenna systems under different energy-distribution constraints. The performance metric used here is the Cramer-Rao bound (CRB). The paper first addresses the CFO-free case and then generalizes the results to include CFO-corrupted scenarios. Training sequences are designed that render the CRB for the CFO independent of the channel zeros. The proposed training designs also facilitate simple implementation of the maximum-likelihood CFO and channel estimators. Simulation results illustrate the merits of the proposed training designs

Parameter estimation for random amplitude chirp signals

We consider the problem of estimating the parameters of a chirp signal observed in multiplicative noise, i.e., whose amplitude is randomly time-varying. Two methods for solving this problem are presented. First, an unstructured nonlinear least-squares approach (NLS) is proposed. It is shown that by minimizing the NLS criterion with respect to all samples of the time-varying amplitude, the problem reduces to a two-dimensional (2-D) maximization problem. A theoretical analysis of the NLS estimator is presented, and an expression for its asymptotic variance is derived. It is shown that the NLS estimator has a variance that is very close to the Cramer-Rao bound. The second approach combines the principles behind the high-order ambiguity function (HBF) and the NLS approach. It provides a computationally simpler but suboptimum estimator. A statistical analysis of the HAF-based estimator is also carried out, and closed-form expressions are derived for the asymptotic variance of the HAF estimators based on the data and on the squared data. Numerical examples attest to the validity of the theoretical analyzes and establish a comparison between the two proposed methods.

Blind frequency-offset estimator for OFDM systems transmitting constant-modulus symbols

We address the problem of carrier frequency offset (CFO) synchronization in OFDM communications systems in the context of frequency-selective fading channels. We consider the case where the transmitted symbols have constant modulus, i.e., PSK constellations. A novel blind CFO estimation algorithm is developed. The new algorithm is shown to greatly outperform a previously published blind technique that exploits the fact that practical OFDM systems are not fully loaded. Further, the proposed algorithm is consistent even when the system is fully loaded. Finally, the proposed CFO estimator is obtained via a one-dimensional search, the same as with the existing virtual subcarrier-based estimator, but achieves a substantial gain in performance (10-dB SNR or one order of magnitude in CFO MSE).

Joint Carrier Frequency Offset and Channel Estimation for OFDM Systems via the EM Algorithm in the Presence of Very High Mobility

In this paper, the problem of joint carrier frequency offset (CFO) and channel estimation for OFDM systems over the fast time-varying frequency-selective channel is explored within the framework of the expectation-maximization (EM) algorithm and parametric channel model. Assuming that the path delays are known, a novel iterative pilot-aided algorithm for joint estimation of the multipath Rayleigh channel complex gains (CG) and the carrier frequency offset (CFO) is introduced. Each CG time-variation, within one OFDM symbol, is approximated by a basis expansion model (BEM) representation. An autoregressive (AR) model is built to statistically characterize the variations of the BEM coefficients across the OFDM blocks. In addition to the algorithm, the derivation of the hybrid Cramer-Rao bound (HCRB) for CFO and CGs estimation in our context of very high mobility is provided. We show that the proposed EM has a lower computational complexity than the optimum maximum a posteriori estimator and yet incurs only an insignificant loss in performance.

Estimation of directions of arrival of multiple scattered sources

We consider the problem of estimating the directions of arrival (DOA) of multiple sources in the presence of local scattering. This problem is encountered in wireless communications due to the presence of scatterers in the vicinity of the mobile or when the signals propagate through a random inhomogeneous medium. Assuming a uniform linear array (ULA), we develop DOA estimation algorithms based on covariance matching applied to a reduced-size statistic obtained from the sample covariance matrix after redundancy averaging. Next, a computationally efficient estimator based on AR modelling of the coherence loss function is derived. A theoretical expression for the asymptotic covariance matrix of this estimator is derived. Finally, the corresponding Cramer-Rao bounds (CRBs) are derived. Despite its simplicity, the AR-based estimator is shown to possess performance that is nearly as good as that of the covariance matching method.

On the Joint Estimation of the RSS-Based Location and Path-loss Exponent

Due to its straightforward implementation, the received signal strength (RSS) has been an advantageous approach for low cost localization systems. Although the propagation model is difficult to characterize in uncertain environments, the majority of current studies assume to have exact knowledge of the path-loss exponent (PLE). This letter deals with RSS based localization in an unknown path-loss model. First, we derive an analytical expression for the mean square error on location estimates for incorrect PLE assumption and examine, via simulation, the effects of error in the PLE on the location accuracy. Second, we enhance a previously proposed RSS-PLE joint estimator (JE) by reducing its complexity. We also propose a maximum a posteriori (MAP) estimator by considering the PLE as an unknown random variable. Finally, we derive the Hybrid Cramer Rao Bound (HCRB) as a benchmark for the MAP estimator. Error analysis results predict large error due to incorrect PLE assumption which are in agreement with the simulation results. Further simulations show that the MAP estimator exhibits better performance at low signal to noise ratio (SNR) and that the relation between the HCRB and CRB depends on the network geometry.

A Joint TOA/AOA Constrained Minimization Method for Locating Wireless Devices in Non-Line-of-Sight Environment

Non-line-of-sight (NLOS) propagation degrades the performance of wireless location systems. Thus, developing algorithms that are robust to NLOS is of great importance. This paper introduces a new location technique that utilizes time of arrival (TOA) and angle of arrival (AOA) measurements. In the proposed method, we assume the signal from the mobile station reaches each base station via one dominant scatterer. By including the scatterers coordinates as unknowns in a TOA/AOA-based cost function and imposing some equality and inequality constrains, the location of the mobile station (MS) is shown to significantly improve. The performance of the proposed algorithm is assessed and compared with that of existing algorithms through extensive simulations.