Koffi Clément Yao

Blind Recognition of Linear Space–Time Block Codes: A Likelihood-Based Approach

Blind recognition of communication parameters is a research topic of high importance for both military and civilian communication systems. Numerous studies about carrier frequency estimation, modulation recognition as well as channel identification are available in literature. This paper deals with the blind recognition of the space-time block coding (STBC) scheme used in multiple-input-multiple-output (MIMO) communication systems. Assuming there is perfect synchronization at the receiver side, this paper proposes three maximum-likelihood (ML)-based approaches for STBC classification: the optimal classifier, the second-order statistic (SOS) classifier, and the code parameter (CP) classifier. While the optimal and the SOS approaches require ideal conditions, the CP classifier is well suited for the blind context where the communication parameters are unknown at the receiver side. Our simulations show that this blind classifier is more easily implemented and yields better performance than those available in literature.

Hierarchical Space-Time Block Code Recognition Using Correlation Matrices

The blind recognition of communication parameters is a key research issue for commercial and military communication systems. The results of numerous investigations about symbol timing estimation, modulation recognition as well as identification of the number of transmitters have been reported in the literature. But, to our knowledge, none of them have dealt with the recognition of the Space-Time Block Codes (STBC) used in multiple transmitter communications. In order to blindly recognize the STBC of a wireless communication, this paper proposes a method based on the space-time correlations of the received signals. Under perfect timing synchronization and under ideal conditions (full rank channel and a number of receivers greater or equal to the number of transmitters), it shows that the Frobenius norms of these statistics present non-null values whose positions only depend on the STBC at the transmitter side. A classifier for the space-time code recognition of 5 linear STBC (Spatial Multiplexing, Alamouti Coding, and 3 Orthogonal STBC using 3 antennas) is presented. Simulations show that the proposed method performs well even at low signal-to-noise ratios.

Blind Channel Estimation for STBC Systems Using Higher-Order Statistics

This paper describes a new blind channel estimation algorithm for Space-Time Block Coded (STBC) systems. The proposed method exploits the statistical independence of sources before space-time encoding. The channel matrix is estimated by minimizing a kurtosis-based cost function after Zero-Forcing equalization. In contrast to subspace or Second-Order Statistics (SOS) approaches, the proposed method is more general since it can be employed for the general class of linear STBCs including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBCs. Furthermore, unlike other approaches, the method does not require any modification of the transmitter and, consequently, is well-suited for non-cooperative context. Numerical examples corroborate the performance of the proposed algorithm.

Blind recognition of linear space time block codes

Blind recognition of communication parameters is a research topic of high importance for both military and civilian communication systems. Numerous studies about carrier frequency estimation, modulation recognition as well as channel identification are available in literature. This paper deals with the blind recognition of the space-time block coding (STBC) scheme used in multiple-input-multiple-output (MIMO) communication systems. Assuming there is perfect synchronization at the receiver side, this paper proposes three maximum-likelihood (ML)-based approaches for STBC classification: the optimal classifier, the second-order statistic (SOS) classifier, and the code parameter (CP) classifier. While the optimal and the SOS approaches require ideal conditions, the CP classifier is well suited for the blind context where the communication parameters are unknown at the receiver side. Our simulations show that this blind classifier is more easily implemented and yields better performance than those available in literature.

Underdetermined BSS of MISO OSTBC Signals

To improve the bit rate, the effectiveness of wireless transmission systems and limit the effects of fading transmission channel, an increased attention has recently been paid to MIMO systems. In fact, Alamoutis space-time block code is introduced in various wireless standards and systems. This manuscript deals with the problem of presence identification as well as blind separation of Orthogonal Space-Time Block Code (OSTBC) in the context of non data aided.

Underwater acoustic signal separation based on prior estimation of the channel impulse response

An underwater acoustic signals separation method based on prior information about the channel impulse response (IR) is presented. Using a sound speed profile measured in a real world environment, the IR of the ocean is estimated with normal mode theory and the ray tracing method. This propagation medium is characterized by a long duration finite IR composed alternatively of several intervals with null coefficients and peaks of non-zero amplitude that express the multi-path propagation. The channel IR characteristics are used as a prior knowledge to select intervals expected to contain non-zero coefficients to be adapted for separation filters estimation. Hence, the number of samples of filters to be adapted is highly reduced. The algorithm proposed by Yellin and Weinstein (1994) is used to successfully achieve the separation of two independent 16-QAM signals mixed in a realistic shallow water environment.

Spectrum Sensing for Full-Duplex Cognitive Radio Systems

Full-Duplex (FD) transceiver has been proposed to be used in Cognitive Radio (CR) in order to enhance the Secondary User (SU) Data-Rate. In FD CR systems, in order to diagnose the Primary User activity, SU can perform the Spectrum Sensing while operating. Making an accurate decision about the PU state is related to the minimization of the Residual Self Interference (RSI). RSI represents the error of the Self Interference Cancellation (SIC) and the receiver impairments mitigation such as the Non-Linear Distortion (NLD) of the receiver Low-Noise Amplifier (LNA). In this manuscript, we deal with the RSI problem by deriving, at the first stage, the relation between the ROC curves under FD and Half-Duplex (HD) (when SU stops the transmission while sensing the channel). Such relation shows the RSI suppression to be achieved in FD in order to establish an efficient Spectrum Sensing relatively to HD. In the second stage, we deal with the receiver impairments by proposing a new technique to mitigate the NLD of LNA. Our results show the efficiency of this method that can help the Spectrum Sensing to achieve a closed performance under FD to that under HD.

Object classification using neural networks in sonar imagery

An experimental comparison of pattern classification methods in the particular case of objects lying on the seafloor in sonar imagery is carried out. The object identification technique relies on the analysis of the object cast shadow. Different kinds of geometric features are extracted such as elongation and orientation of the shadow, Fourier descriptors, and new parameters derived from the shadow profile. The performance is evaluated using two sets of data coming form synthetic sonar images differently noised. The comparison shows better performance of multi-layer perceptron especially for poorly segmented images. Finally, the performance of the system is investigated on real images.

Spectrum Sensing for Half and Full-Duplex Cognitive Radio

Due to the increasing demand of wireless communication services and the limitation in the frequency resources, the Cognitive Radio (CR) has been initially proposed (Mitolal, IEEE Personal Commun 6:13–18, 1999 [1]) in order to solve the spectrum scarcity. CR distinguishes between two types of users, the Primary (PU) and the Secondary (SU) Users. PU has the legal right to use the spectrum bandwidth, while SU is an opportunistic user that can transmit on that bandwidth whenever it is vacant in order to avoid any interference with the signal of PU. Hence the detection of PU becomes a main priority for CR systems. The Spectrum Sensing is performed by CR to monitor PU activities. In actual CR systems (Yucek and Arslan, IEEE Commun Surv Tutorials 11:116–130, 2009 [2]), SU should stop transmitting while Spectrum Sensing is performed. The transmission of SU can be only resumed if PU is still absent. This procedure means that the CR can only operate in a Half-Duplex (HD) mode. Recently, many works have been proposed in order to attend the Full-Duplex (FD) mode. In other words, the Spectrum Sensing should be performed while SU is being active. Our work deals with the HD and FD of CR. First, we develop two Spectrum Sensing algorithms, based on the Cumulative Power Spectral Density (CPSD) of the received signal, dealing with the HD mode. These algorithms outperform the traditional Energy Detector (ED), the well known Cyclostationary Detector (CSD) based on the Generalized Likelihood Ratio Test (GLRT) and the Autocorrelation Detector (ACD). Furthermore, our algorithms are robust against the noise variance, so that the dependence on Noise Uncertainty (NU) presented in ED is avoided. In addition, the proposed algorithms are blind as they don’t require any prior information on the PU’s signal, contrary to Cyclostationary or Waveform detectors. Our algorithms make a decision on the PU presence by comparing the form of CPSD shape to curves depending on the CPSD of the noise. Two algorithms based on hard and soft cooperative schemes are introduced. In these algorithms, the spectrum is divided into two parts. The first part corresponds to negative frequencies, while the second part deals with the positive frequencies. Hence, two test statistics are evaluated, based on the CPSD of each of those two parts, and they are then combined according to the considered scheme. The False Alarm and Detection probabilities of the two proposed algorithms are evaluated analytically under Gaussian and Rayleigh fading channels. We examine our proposed detectors at a low Signal to Noise Ratio. The performance of our detectors is compared to that of ED, CSD and the ACD. Our detectors outperform ED, even at low oversampling rate, where CSD and ACD provide poor performance. Increasing the oversampling rate enhances the performance our algorithms as well as that of ACD and CSD. However, our algorithms remain better than ED, CSD and ACD for all tested values of oversampling rate. Furthermore, our detectors are less sensitive to NU than ED. Besides that, our detectors can be modified to become independent from noise variance and no longer affected by NU problem. The FD transceiver has been proposed to double the channel efficiency by transmitting and receiving in the same band at the same time. The main challenge in FD consists in minimizing the Residual Self Interference (RSI) which represents the error of the Self Interference Cancellation (SIC) and the receiver’s impairments mitigation such as the Non-Linear Distortion (NLD) of the receiver’s Low-Noise Amplifier (LNA). In CR, FD concerns the Secondary User (SU) who can transmit while sensing the channel. Since the SU should be aware of the Primary User (PU) activity, the RSI represents an important challenge for the SU. An ideal situation is achieved by SU when the RSI is totally eliminated, leading the SU to establish a Spectrum Sensing process equivalent to that of HD. In our work, we deal with this problem by analyzing, in the first stage, the impact of the RSI power on the detection process. For that objective, we derive a relation among the RSI power and the probabilities of detection and false alarm under HD and FD modes. To mitigate the NLD of LNA, we analyze the NLD impact on the channel estimation and the Spectrum Sensing Performance. After that, a novel method is proposed to suppress the NLD of LNA without affecting the channel estimation process. Furthermore, our proposed method outperforms significantly other methods proposed in the literature. In addition, using our method, the receiver requires only one training symbol period to perform the estimation of the channel and the NLD estimation. Our results show that an accurate mitigation of the NLD can be reached, which leads to an accurate channel estimation and to reduce the RSI power. Finally, Receiver Operating Characteristic (ROC) curves obtained in FD mode, after applying our method of NLD elimination, are very closed to those of HD mode.

Spatial and time diversities for canonical correlation significance test in spectrum sensing

In this paper, we present a new detector for cognitive radio system based on the Canonical Correlation Significance Test (CCST). Unlike existing CCST approaches, which can only be applied on Multi-Antenna System (MAS), our algorithm can be extended for both Single Antenna System (SAS) and MAS. For SAS, the proposed algorithm exploits the time diversity of cyclostationary signals in order to detect the Primary User (PU) signal. Our simulation results shows that our algorithm outperforms well-known cyclostationary algorithm [9]. For MAS, our algorithm uses both spatial and time diversities to apply the CCST. Numerical results are given to illustrate the performance of our algorithm and verify its efficiency for special noise cases (spatially correlated and spatially colored). The simulation results show the superiority of the performance of the proposed detector compared to the recently CCST proposed algorithm [1].