François-Xavier Socheleau

Non data-aided SNR estimation of OFDM signals

This letter deals with the problem of non data aided (NDA) signal to noise ratio (SNR) estimation of OFDM signals transmitted through unknown multipath fading channel. Most of existing OFDM SNR estimators are based on the knowledge of pilot sequences which is not applicable in some contexts such as cognitive radio for instance. We show that it is possible to take advantage of the periodic redundancy induced by the cyclic prefix to get an accurate NDA SNR estimator. Numerical simulations highlight the benefit of the proposed method compared with the state of the art.

Robust Estimation of Noise Standard Deviation in Presence of Signals With Unknown Distributions and Occurrences

In many applications, d-dimensional observations result from the random presence or absence of random signals in independent and additive white Gaussian noise. An estimate of the noise standard deviation can then be very useful to detect or to estimate these signals, especially when standard likelihood theory cannot be applied because of too little prior knowledge about the signal probability distributions. The present paper introduces a new scale estimator of the noise standard deviation when the noisy signals have unknown probability distributions and unknown probabilities of presence less than or equal to one half. The latter assumption can be regarded as to a weak assumption of sparsity. Applied to the detection of noncooperative radio-communications, this new estimator outperforms the standard MAD and its alternatives as well as the trimmed and winsorized robust scale estimators. The Matlab code corresponding to the proposed estimator is available at http://perso.telecom-bretagne.eu/pastor.

OFDM System Identification for Cognitive Radio Based on Pilot-Induced Cyclostationarity

In the context of cognitive radio, this paper addresses the challenge of OFDM system identification. We show that it is possible to take advantage of pilot tone structures to perform identification. Pilot subcarrier usage is of great interest since it enables to discriminate OFDM systems that have the same modulation parameters (subcarrier spacing, cyclic prefix duration, etc.). The proposed method relies on the periodic redundancy often induced between pilot symbols. We demonstrate that most OFDM systems (Wifi, WiMAX, DVB-T, etc.) can be identified thanks to this redundancy by conducting a hypothesis test based on second order statistics. Detailed numerical examples demonstrate the efficiency of the proposed identification criterion in various kinds of environments.

Cognitive OFDM system detection using pilot tones second and third-order cyclostationarity

The emerging trend to provide users with ubiquitous seamless wireless access leads to the development of multi-mode terminals able to smartly switch between heterogeneous wireless networks. This switching process known as vertical handoff requires the terminal to first detect the surrounding networks it is compatible with. In the context where these networks are cognitive, this can be challenging since the carrier frequency of their access point may change over the time. One solution to overcome this challenge is to embed network specific signatures in the PHY layer. We here focus on cognitive OFDM systems and advocate to embed signatures onto pilot tones since (i) it makes possible to discriminate systems with the same modulation parameters (ii) it creates easy to intercept signatures implying short detection latency (iii) it avoids adding any side information dedicated to detection that would reduce systems capacity. We propose two complementary signature/detection schemes based on second and third-order statistics, respectively. The first scheme relies on redundancy between pilot symbols and the second is based on the use of maximum-length sequences. Detailed numerical examples demonstrate the efficiency of the two detection criteria in realistic environments.

Stochastic Replay of Non-WSSUS Underwater Acoustic Communication Channels Recorded at Sea

To fully exploit sea experiments under controlled and reproducible laboratory conditions, a channel model driven by real data is derived. This model relies on the assumption that a channel recorded at sea is a single observation of an underlying random process. From this single observation, the channel statistical properties are estimated to then feed a stochastic simulator that generates multiple realizations of the underlying process. Based on the analysis of data collected in the Atlantic Ocean and the Mediterranean Sea, we fully relax the usual wide-sense stationary uncorrelated scattering (WSSUS) assumption. We show thanks to the empirical mode decomposition that a trend stationary model suits the analyzed underwater acoustic communication channels very well. Scatterers with different path delays are also assumed to be potentially correlated so that the true second-order statistics of the channel are taken into account by our model. Test cases illustrate the benefits of channel stochastic replay to communication system design and validation. The Matlab code corresponding to the proposed simulator is available at http://perso.telecom- bretagne.eu/fxsocheleau/software .

Concise Derivation of Scattering Function from Channel Entropy Maximization

In order to provide a concise time-varying SISO channel model, the principle of maximum entropy is applied to scattering function derivation. The resulting model is driven by few parameters that are expressed as moments such as the channel average power or the Doppler spread. Physical interpretations of the model outputs are discussed. In particular, it is shown that common Doppler spectra such as the flat or the Jakes spectrum fit well into the maximum entropy framework. The Matlab code corresponding to the proposed model is available at http://perso.telecom-bretagne.eu/fxsocheleau/software.

Stochastic replay of SIMO underwater acoustic communication channels

Stochastic replay is a channel simulation method that generates random time-varying impulse responses (TVIRs) from data collected at sea. Most existing replay-based simulators only consider single-input single-output transmissions. In this paper, a single-input multiple-output (SIMO) stochastic replay-based simulator is presented. It is shown to keep temporal, inter-tap as well as spatial correlations consistent with those of the TVIR measured at sea. Numerical examples applied to data collected in Brest harbor, France, are discussed.

Automated detection of Antarctic blue whale calls

This paper addresses the problem of automated detection of Z-calls emitted by Antarctic blue whales (B. m. intermedia). The proposed solution is based on a subspace detector of sigmoidal-frequency signals with unknown time-varying amplitude. This detection strategy takes into account frequency variations of blue whale calls as well as the presence of other transient sounds that can interfere with Z-calls (such as airguns or other whale calls). The proposed method has been tested on more than 105 h of acoustic data containing about 2200 Z-calls (as found by an experienced human operator). This method is shown to have a correct-detection rate of up to more than 15% better than the extensible bioacoustic tool package, a spectrogram-based correlation detector commonly used to study blue whales. Because the proposed method relies on subspace detection, it does not suffer from some drawbacks of correlation-based detectors. In particular, it does not require the choice of an a priori fixed and subjective template. The analytic expression of the detection performance is also derived, which provides crucial information for higher level analyses such as animal density estimation from acoustic data. Finally, the detection threshold automatically adapts to the soundscape in order not to violate a user-specified false alarm rate.

A maximum entropy framework for statistical modeling of underwater acoustic communication channels

Based on a method of inductive inference known as the principle of maximum entropy, a time-varying underwater acoustic channel model is derived. The resulting model is proved to be consistent so that it only relies on the available knowledge of the environment to model. While requiring only a few parameters (e.g. channel average power and Doppler spread), it is shown through fading statistics and bit error rates measurements that accurate channel impulse responses can be obtained for communication applications. The Matlab code of the proposed model is available at http://perso.telecom-bretagne. eu/fxsocheleau/software.

Blind noise variance estimation for OFDMA signals

We present two new noise variance estimation methods for OFDMA signals transmitted through an unknown multipath fading channel. We focus on blind estimation as it does not require any pilot sequences and is therefore applicable to contexts, such as cognitive radio for instance, where little prior signal knowledge is available. The two estimators are respectively based on the time-frequency sparsity of OFDMA signals and on the redundancy induced by the cyclic prefix. Numerical simulations compare the performance of the two algorithms and highlight their complementarity.