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.

Blind signal identification is an important topic of research for both commercial and military communications. A novel identification algorithm for spatial multiplexing (SM) and Alamouti space-time block code (AL-STBC) signals is proposed in this paper, when the receiver is equipped with a single antenna. The proposed algorithm exploits a discriminating feature provided by the discrete Fourier transform (DFT) of the fourth-order lag product (FOLP) of the received signal. The proposed algorithm requires neither estimation of the channel, noise power, nor modulation of the transmitted signal. Computer simulations are conducted to evaluate the algorithm performance; these show the validity of the proposed algorithm with low sensitivity to timing offset.

This paper develops a space-time block code (STBC) identification algorithm for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) transmission over frequency-selective channels. We show that, by taking advantage of the space-time redundancy, signal identification can be performed by exploiting the cross-correlation of the received signals from two antennas as a discriminating feature. With this feature, we develop a binary hypothesis test for decision making. The proposed algorithm does not require information about the channel coefficients, modulation, noise power, or start times of the OFDM and STBC blocks. Moreover, it does not need accurate clock timing information. Simulation experiments have verified the accuracy and feasibility of the proposed algorithm.

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.

Likelihood-based algorithms for the classification of linear digital modulations are systematically investigated for a multiple receive antennas configuration. Existing modulation classification (MC) algorithms are first extended to the case of multiple receive antennas and then a critical problem is identified that the overall performance of the multiple antenna systems is dominated by the worst channel estimate of a particular antenna. To address the performance degradation issue, we propose a new MC algorithm by optimally combining the log likelihood functions (LLFs). Furthermore, to analyze the upper-bound performance of the existing and the proposed MC algorithms, the exact Cramer-Rao Lower Bound (CRLB) expressions of non-data-aided joint estimates of amplitude, phase, and noise variance are derived for general rectangular quadrature amplitude modulation (QAM). Numerical results demonstrate the accuracy of the CRLB expressions and verify that the results reported in the literature for quadrature phase-shift keying (QPSK) and 16-QAM are special cases of our derived expressions. Also, it is demonstrated that the probability of correct classification of the new algorithm approaches the theoretical bounds and a substantial performance improvement is achieved compared to the existing MC algorithm.

This paper investigates the second-order cyclostationarity of block transmitted-single carrier linearly digitally modulated (BT-SCLD) signals, and its applications to signal classification and blind (non-data aided) parameter estimation. Analytical closed-form expressions are derived for the cyclic autocorrelation function (CAF), cyclic spectrum (CS), complementary CAF (CCAF), complementary CS (CCS), and corresponding cycle frequencies (CFs). Furthermore, the conditions for avoiding aliasing in the cycle and spectral frequency domains are obtained. Based on these findings, we propose algorithms for classifying BTSCLD, orthogonal frequency division multiplexing (OFDM), and SCLD signals, and for the blind estimation of the BT-SCLD block transmission parameters. Simulation and laboratory experiments demonstrate the effectiveness of the proposed algorithms under low signal-to-noise ratios (SNRs), short sensing times, and various channel conditions. Furthermore, these algorithms have the advantage of not requiring the recovery of carrier, waveform, and symbol timing information, or the estimation of signal and noise powers.

Cognitive radio (CR) systems require knowledge of the signal environment if they are to coexist with the primary (incumbent) users of the radio spectrum. Consequently, signal identification is a major task of a CR. This paper proposes a novel identification algorithm for spatial multiplexing (SM) and Alamouti space-time block code (AL STBC) transmissions, when the CR employs a single receive antenna. This algorithm relies on the fourth-order moment as a discriminating signal feature and uses a maximum likelihood (ML) criterion for decision making. Its performance is investigated through theoretical analysis and simulation experiments.

Signal classification is a major task of a cognitive radio. This paper proposes a novel cyclostationarity-based algorithm for the blind classification of space time block codes (STBCs) and derives analytical expressions for the second-order cyclic statistics used as the basis of the algorithm. Monte Carlo simulation results demonstrate a good classification performance with low sensitivity to phase noise and the channel Doppler shift. The proposed approach avoids the need for a priori knowledge of the channel coefficients, carrier phase, and timing offsets. Moreover, it does not need accurate information about the transmission data rate and carrier frequency offset.

Spectrum sensing and awareness constitute key functionalities of a cognitive radio (CR), and encompass signal detection, classification, and blind parameter estimation. This paper proposes a novel algorithm for the tone frequency spacing estimation and joint classification of M-ary frequency shift keying (M-FSK) signals in a fading environment. The proposed algorithm relies on the number and position of the first-order cycle frequencies (CFs), and requires neither recovery of the carrier and symbol timing, nor the estimation of channel parameters or signal and noise powers. Receive spatial diversity is exploited to enhance the classification and estimation performance of the algorithm. The simulation results for the algorithm performance confirm its effectiveness.

Circular (angular) random variables, such as the angle of arrival(AOA) in direction finding systems and the phase error in digital phase-locked loops and digital phase interferometers, are best modeled by the von Mises distribution, not the ubiquitous Gaussian distribution. However, the Gaussian distribution has been commonly preferred in the performance analysis of AOA geolocation systems, mostly due to its mathematical tractability. This paper demonstrates the use of the von Mises distribution to model AOA measurement errors in direction finding systems and derives simple expressions for the Fisher information matrix (FIM) and the Cramer-Rao lower bound for the average miss distance. These results show that the use of the von Mises model in the performance analysis of AOA geolocation systems is entirely practicable.

The FFT summation detector, a detection scheme extensively used in applications such as civilian spectrum monitoring and military radio surveillance, is also applicable to cognitive radio spectrum sensing applications. The practical implementation of the FFT summation detector depends on the reliable numerical computation of the normalized detection threshold, Tn,. Although this problem has been the subject of considerable study, the computation of Tnstill presents difficulties. Existing approaches depend on numerical procedures such as the Newton-Raphson and the golden section search algorithms, which often fail to converge when the number of input data blocks, L, or the number of FFT bins, N, used for channel power estimation is large. To circumvent such numerical difficulties, easily computable lower and upper bounds for Tom, are of great interest as they are essential for verifying the accuracy of approximations to Tnwhen the true value of Tnis not available. Although several lower bounds for Tnhave been proposed, few good upper bounds for Tnhave been derived. This paper proposes a new upper bound for Tn, which is easily computed for larger values of the probability of false alarm, Pfa, using the Imhof integral formula.

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.

A simple geometric interpretation for received signal strength (RSS) difference based geolocation can be illustrated by considering a plane containing a single pair of receivers and a transmitter. If the path loss follows a simple inverse power law, the RSS difference (in decibels) between the two receivers can be shown to define a circle on which the transmitter must lie. With additional receivers, the position of the transmitter can be solved by finding the common intersection of the circles corresponding to the different pairs of receivers. In practice, the solution of this problem is complicated by the errors contributed by environmental noise, measurement errors and the deviation of the actual path losses from the model. The optimal nonlinear least squares solution can be obtained by performing a search on a planar grid. However, the computational cost becomes an issue when the number of receivers is large. This paper presents an efficient least squares solution whose performance approaches that of the optimal nonlinear least squares solution.

The Cramer-Rao lower bound (CRLB) is derived for the miss distance of an emitter geolocation system using received signal strength (RSS) measurements from spatially dispersed sensors. This result can be written as the product of several factors, one of which completely characterizes the effect of the emitter-sensor geometry on geolocation accuracy. This particular factor, which depends only on the relative positions of the emitter and sensors, is referred to as the geometric dilution of precision (G) in this paper. The properties of G can be analyzed using a combination of computer simulations and statistical analysis. These results provide useful insights into the problem of optimal sensor placement with respect to the area of interest for RSS based geolocation.

Cognitive radio (CR) represents a possible solution to the paradoxical problem of simultaneous scarcity and underutilization of the electromagnetic spectrum. Spectrum awareness, a key task of such a radio, encompasses the recognition of the received signal type and parameters. This paper investigates the cyclostationarity approach for the recognition of cyclically prefixed single carrier linearly digitally modulated (CP-SCLD) signals versus SCLD and orthogonal frequency division multiplexing (OFDM) signals under practical conditions, including time-dispersive channels, additive Gaussian noise, and phase, frequency and timing offsets. Analytical closed-form expressions are derived for the cyclic autocorrelation function (CAF) and the set of cycle frequencies (CFs) of CP-SCLD signals. These results are the basis of the proposed signal recognition algorithm. This algorithm has the advantage of avoiding requirements for the recovery of carrier, waveform, and symbol timing information, and the estimation of signal and noise powers.

This paper derives a fuzzy logic-based approach to the averaging of angle of arrival (AOA) information. It is shown that the maximum likelihood (ML) approach to AOA averaging can be considered as a special case of the fuzzy logic approach. Computer simulations show that the ML and fuzzy logic approaches produce very similar results if the membership functions used in the fuzzy logic approach are appropriately constructed.

Previous studies on the cyclostationarity aspect of orthogonal frequency division multiplexing (OFDM) and single carrier linearly digitally modulated (SCLD) signals assumed simplified signal and channel models or considered only second-order cyclostationarity. This paper presents new results concerning the cyclostationarity of these signals under more general conditions, including time dispersive channels, additive Gaussian noise, and carrier phase, frequency, and timing offsets. Analytical closed-form expressions are derived for time- and frequency-domain parameters of the cyclostationarity of OFDM and SCLD signals. In addition, a condition to eliminate aliasing in the cycle and spectral frequency domains is derived. Based on these results, an algorithm is developed for recognizing OFDM versus SCLD signals. This algorithm obviates the need for commonly required signal preprocessing tasks, such as signal and noise power estimation and the recovery of symbol timing and carrier information.

The second-order cyclostationarity of cyclically prefixed single carrier linear digital (CP-tSCLD) modulated signals is investigated with emphasis on its applicability to signal recognition. Analytical closed-form expressions for the second-order (one-conjugate) cyclic cumulants (CCs) and the set of cycle frequencies (CFs) for CP-SCLD modulated signals are derived. Based on these results, an algorithm is proposed for the recognition of CP-tSCLD against SCLD and orthogonal frequency division multiplexing (OFDM) signals. This algorithm obviates the need for signal pre-processing tasks, such as symbol timing, carrier and waveform recovery and estimation of signal and noise powers.

The sensing of the radio frequency (RF) environment is a fundamental concept of cognitive radio (CR). It follows that the detection and classification of very low signal-to-noise ratio signals with relaxed a priori information on signal parameters is a problem of considerable relevance to CR. This paper proposes an algorithm based on first-order cyclostationarity for joint detection and classification of frequency-shift-keying (FSK) signals. This algorithm does not require timing and frequency recovery, and estimation of signal and noise powers. The theoretical analysis of the algorithm performance is validated by simulation results.

This paper studies the nth-order cyclostationarity of orthogonal frequency division multiplexing (OFDM) and single carrier linearly digitally modulated (SCLD) signals affected by a time dispersive channel, additive Gaussian noise, carrier phase, and frequency and timing offsets. The analytical closed-form expressions for the nth-order cyclic cumulants (CCs) and cycle frequencies (CFs) of OFDM and SCLD signals are derived. Furthermore, a second-order CC-based algorithm is developed to recognize OFDM against SCLD signals under the aforementioned conditions. This algorithm obviates the need for signal preprocessing tasks, such as symbol timing estimation, carrier and waveform recovery, and signal and noise power estimation. Simulation experiments confirm the theoretical analysis.

The paper studies the cyclostationarity of an orthogonal frequency division multiplexing (OFDM) with a view to recognizing OFDM against single carrier linear digital (SCLD) modulations. The analytical expressions for the nth-order cyclic cumulants (CCs) and cycle frequencies of an OFDM signal embedded in additive white Gaussian noise and subject to phase, frequency and timing offsets are derived An algorithm based on a second-order CC is proposed to recognize OFDM against SCLD modulations. The recognition algorithm of the authors obviates the need for preprocessing tasks, such as symbol timing estimation, carrier and waveform recovery, and signal and noise power estimation. The results of simulation experiments confirm the theoretical analysis.

In this paper, first-order cyclostationarity of M-ary frequency shift keying (M-FSK) signals affected by additive Gaussian noise, phase, frequency offset and timing errors is investigated, and applied for modulation order recognition. A novel recognition algorithm is proposed, which employs the number of first-order cycle frequencies (CFs) of the received signal as a discriminating feature. The algorithm has the advantage that it requires neither symbol timing and carrier recovery, nor estimation of signal and noise powers as preprocessing tasks. Simulations are carried out to confirm theoretical developments.

The reliable computation of detection threshold T given a desired probability of false alarm Pfaa is an important issue in the design of the FFT filter bank-based summation CFAR (constant false alarm rate) detector. The computation of detection threshold T is based on numerical procedures such as the Newton-Ralphson algorithm and a priori knowledge of lower and upper bounds for T for a given Pfa. Current approaches used in the initialization stage of the computation of threshold T are largely ad hoc as there are no theoretical upper and lower bounds for T reported in the literature. In this article, several theoretical upper and lower bounds for T for overlapped and non-overlapped signal data are derived. These results enable a proper design of the FFT filter bank-based summation CFAR detector

An algorithm based on a decision-tree approach for the automatic modulation recognition of communications signals is presented. This algorithm can discriminate between continuous wave (CW), amplitude modulation (AM), double sideband suppressed carrier (DSB-SC), frequency modulation (FM), frequency shift keying (FSK), binary phase shift keying (BPSK) and quaternary phase shift keying (QPSK) modulations. Requirements for a priori knowledge of the signals are minimized by the inclusion of an efficient carrier frequency estimator and low sensitivity to variations in the sampling epochs. Computer simulations indicate good performance on an AWGN channel, even at signal-to-noise ratios as low as 5 dB. This compares favorably with the performance obtained with most algorithms based on pattern recognition techniques.

A new approach to the identification of a constant amplitude signal with frequency/phase modulation is investigated. The authors model the incoming signal phase as a linear combination of a set of orthogonal vectors and use the significant coefficients as features for identification. Because of the data compression ability of orthogonal transform, a few coefficients are sufficient for signal representation, thereby reducing the processing time and system complexity. The choices of transforms and feature size are discussed. The performance of the new identifier is studied through simulations.

Many of the practical problems encountered in time delay estimation for the source location application have received little attention in the literature. These include phase mismatches between spatially separated receivers, the need for estimates of the quality of the time delay estimates, and the development of computationally efficient algorithms capable of fine time resolution. This paper describes methods and techniques that address these needs and presents experimental and simulation results that confirm their reliability.

Research on the estimation of time of arrival (TOA) differences has emphasized the reduction of variance for measurements corresponding to individual pairs of signals, each estimate being regarded as an independent variable. In reality, the geometry of a source location system results in linear constraints which must be satisfied by the TOA differences. These constraints, known from a priori information, can be formally incorporated into the estimation procedure to achieve lower variances than can be obtained with independent maximum likelihood estimation. An algorithm for constrained estimation has been developed in the frequency domain extending the algorithm proposed by Chan et al. [1] for independent estimation. The concept of constrained estimation is then shown applicable in the time domain provided that variance estimates of the independent TOA differences can be explicitly derived. Experimental results with stationary and nonstationary signals demonstrated reduction of the RMS location error in the order of 20 to 56 percent for signal-to-noise ratios ranging from 30 to 10 dB, respectively.

Charge coupled device (CCD) memory technology offers potential economic advantages over semiconductor random-access memory technology. However, the limitations incurred by the serial nature of CCD's have previously restricted their application to computer mainframe memories. The 64 kbyte CCD memory system described in this paper demonstrates the feasibility of CCD memory technology for moderate size memory systems applicable to microcomputer systems. Design objectives included low cost, adequate performance, reliable operation, small size, and low power consumption as well as simple interfacing to standard microprocessors. A simple two-level organization employing a random access memory (RAM) to buffer the serial CCD memory was used to improve the memory system performance and to simplify the interfacing of microcomputers. It is anticipated that the memory system can be easily modified to use 64 kbit and larger CCD memory devices as these become available. Further more, the memory system control logic could be integrated on a single large-scale integration (LSI) chip, thereby facilitating the fabrication of relatively large and economical memory systems with a low component count.