Mohamed Marey

Classification of Space-Time Block Codes Based on Second-Order Cyclostationarity with Transmission Impairments

Signal classification is important in various commercial and military applications. Multiple antenna systems complicate the signal classification problem since there is now the issue of estimating the number and configuration of transmit antennas. The novel blind classification algorithm proposed in this paper exploits the cyclostationarity property of space-time block codes (STBCs) for the classification of multiple antenna systems in the presence of possible transmission impairments. Analytical expressions for the second-order cyclic statistics used as the basis of the algorithm are derived, and the computational cost of the proposed algorithm is considered. This algorithm avoids the need for a priori knowledge of the channel coefficients, modulation, carrier phase, and timing offsets. Moreover, it does not need accurate information about the transmission data rate and carrier frequency offset. Monte Carlo simulation results demonstrate a good classification performance with low sensitivity to phase noise and channel effects, including frequency-selective fading and Doppler shift.

Fourth-Order Statistics for Blind Classification of Spatial Multiplexing and Alamouti Space-Time Block Code Signals

Blind signal classification, a major task of intelligent receivers, has important civilian and military applications. This problem becomes more challenging in multi-antenna scenarios due to the diverse transmission schemes that can be employed, e.g., spatial multiplexing (SM) and space-time block codes (STBCs). This paper presents a class of novel algorithms for blind classification of SM and Alamouti STBC (AL-STBC) transmissions. Unlike the prior art, we show that signal classification can be performed using a single receive antenna by taking advantage of the space-time redundancy. The first proposed algorithm relies on the fourth-order moment as a discriminating feature and employs the likelihood ratio test for achieving maximum average probability of correct classification. This requires knowledge of the channel coefficients, modulation type, and noise power. To avoid this drawback, three algorithms have been further developed. Their common idea is that the discrete Fourier transform of the fourth-order lag product exhibits peaks at certain frequencies for the AL-STBC signals, but not for the SM signals, and thus, provides the basis of a useful discriminating feature for signal classification. The effectiveness of these algorithms has been demonstrated in extensive simulation experiments, where a Nakagami-m fading channel and the presence of timing and frequency offsets are assumed.

Blind Modulation Classification Algorithm for Single and Multiple-Antenna Systems Over Frequency-Selective Channels

This letter proposes a blind modulation classification (MC) algorithm applicable to single and multiple-antenna systems operating over frequency-selective channels. We show that the correlation functions of the received signals for certain modulation formats exhibit peaks at a particular set of time lags, a result which can be exploited as a discriminating feature. We also develop a new hypothesis test in order to detect the correlation-induced peaks. The proposed algorithm is general in the sense that it accommodates any number of transmit- and receive-antennas, without prior information about channel statistics. The classification performance of the proposed algorithm is assessed through Monte Carlo simulations.

EM-Based Joint Channel Estimation and IQ Imbalances for OFDM Systems

The imbalances between the In-phase (I) and Quadrature-phase (Q) branches represent a significant source of impairment in the orthogonal frequency division multiplexing (OFDM) systems. Recently, it has been shown that the unwanted IQ imbalances can be actually exploited to achieve a diversity gain. In this contribution, by taking into account the diversity gain resulting from the IQ imbalances, we develop a novel algorithm to jointly estimate the channel impulse response and IQ imbalances occurring at both the transmitter and receiver. Starting from the Maximum Likelihood (ML) principle, we derive an estimation algorithm based on the expectation maximization (EM) algorithm, which exploits information from the pilot symbols and detected data symbols in a systematic fashion. To reduce the complexity of the estimation algorithm, a sub-optimal scheme is also introduced. The results indicate that the proposed algorithms achieve a significant improvement in the bit error rate (BER) performance after three iterations as compared to conventional data-aided algorithms.

Blind STBC Identification for Multiple-Antenna OFDM Systems

This paper addresses the problem of space-time block code (STBC) identification for multiple-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems operating over frequency-selective channels for the first time in literature. Previous investigations published on the topic of STBC identification were restricted to single-carrier systems operating over frequency-flat channels. OFDM systems make this topic more challenging to handle since the identifiers work in frequency-selective channels with little or no knowledge of the beginning of the OFDM blocks, OFDM parameters, and frequency-selective channel coefficients. We show that, by taking advantage of the space-time redundancy, STBC identification can be performed by exploiting the cross-correlation of the signals received from different antennas as a discriminating feature. Using this feature, we develop a binary hypothesis test for decision making. The proposed method does not require information about the channel coefficients, modulation format, noise power, or timing of the OFDM and STBC blocks. Moreover, it does not need accurate knowledge of either clock-timing information or OFDM parameters, including the number of sub-carriers and cyclic prefix length. Extensive simulation experiments have verified the effectiveness of the proposed method.

Joint Estimation of Transmitter and Receiver IQ Imbalance With ML Detection for Alamouti OFDM Systems

The application of multiple transmit antennas in orthogonal frequency-division multiplexing (OFDM) systems provides better spectral efficiency with high performance gain. However, these systems are very sensitive to channel estimation errors and analog front-end impairments, such as in-phase/quadrature-phase (IQ) imbalance. To ensure a reliable transmission, the channel impulse response (CIR) and IQ imbalance have to be accurately estimated. Unlike previous work, we show that the unwanted IQ imbalance can be exploited to achieve a diversity gain. This paper develops a maximum-likelihood (ML) detector by exploiting the diversity gain resulting from the IQ imbalance for Alamouti space-time block code OFDM systems. Moreover, we propose a novel iterative algorithm to jointly estimate the CIR and IQ imbalance occurring at both the transmitter and the receiver. We initially employ a pilot sequence for estimation and detection. Then, we exploit the soft information provided by the detector via an expectation-maximization (EM) algorithm to improve the estimation efficiency iteratively. To reduce the computational complexity of the estimation process, a suboptimal algorithm is also developed. Simulation results indicate that the bit-error-rate (BER) performance of the proposed detector in conjunction with the proposed estimation algorithms is very close to the BER of the perfectly known parameters case with a diversity gain resulting from the IQ imbalance.

Classification of STBC Systems Over Frequency-Selective Channels

Space-time block code (STBC) classification algorithms have recently received growing attention in academia and industry. In addition to their use in the context of military operations, these algorithms found civilian applications in reconfigurable systems, such as software-defined and cognitive radios. The previously reported single-carrier-based STBC classification algorithms are limited to frequency-flat fading channels; however, the wireless channels are typically frequency selective. This paper exploits the dispersive nature of the frequency-selective fading channels to classify Alamouti (AL) and spatial multiplexing (SM) STBCs over such channels. We show that the cross-correlation function of two different received signals for AL exhibits peaks at a particular set of time lags, whereas that for SM does not. Furthermore, we develop a maximum-likelihood classification algorithm. This requires channel knowledge, which may be unavailable in some scenarios such as radio environment awareness in cognitive radios. To avoid this requirement, we also propose a new classification algorithm based on the false alarm rate. Monte Carlo simulations are conducted to demonstrate the performance of the proposed algorithms.

An efficient algorithm for space-time block code classification

This paper proposes a novel and efficient algorithm for space-time block code (STBC) classification, when a single antenna is employed at the receiver. The algorithm exploits the discriminating features provided by the discrete Fourier transform (DFT) of the fourth-order lag products (FOLPs) of the received signal. It does not require estimation of the channel, signal-to-noise ratio (SNR), and modulation of the transmitted signal. Computer simulations are conducted to evaluate the performance of the proposed algorithm. The results show the validity of the algorithm, its robustness to carrier frequency offset, and low sensitivity to timing offset.

Turbo Estimation and Equalization for Asynchronous Uplink MC-CDMA

In this contribution, we propose a new code-aided synchronization and channel estimation algorithm for uplink MC-CDMA. The space alternating generalized expectation- maximization (SAGE) algorithm is used to estimate the channel impulse responses, propagation delays and carrier frequency offsets of the different users. The estimator, multi-user detector, equalizer, demapper and channel decoder exchange soft information in an iterative way. The performance of the proposed algorithm is evaluated through Monte Carlo simulations. Impressive performance gains are visible as compared to a conventional data-aided estimation scheme.

The Effect of Narrowband Interference on Frequency Ambiguity Resolution for OFDM

In orthogonal frequency division multiplexed (OFDM) systems affected by carrier frequency offsets, frequency ambiguity resolution, i.e. the estimation of the part of the frequency offset corresponding to an integer times the carrier spacing, is a crucial issue. The proper action of frequency ambiguity resolution algorithms can be strongly affected by the presence of disturbances, like narrowband interference (NBI). In this paper, the susceptibility of the blind and data aided ML frequency ambiguity estimators to NBI signals is investigated in an analytical way. The analytical results are verified by means of simulations. Although the estimators turn out to be essentially independent of the bandwidth of the interferers and the number of interferers, the performance of the estimators is very sensitive to the positions of the interferers.