For orthogonal frequency division multiplexing (OFDM) communication systems, the orthogonality among subcarriers is lost in mobile applications due to frequency offset resulting from either transmitter-receiver local oscillator differences or Doppler shift caused by mobility. As a direct result, inter-carrier interference (ICI) is observed on each and every subcarrier, leading to significant performance degradation. There are a lot of OFDM carrier frequency offset (CFO) estimation schemes classified as data aided estimation and blind estimation. Due to the system power and high bandwidth efficiencies, blind estimators have received a lot of attention recently. Many blind CFO schemes were proposed for OFDM systems, some of which are based on power spectrum smoothing, kurtosis-type cost functions and minimum output variance. In this paper, we propose a novel blind CFO estimator based on Minimum Reconstruction Error (MRE). In contrast to other blind CFO estimators, the proposed technique can be used for any constellation schemes, does not require a large number of blocks to reach acceptable estimation error and provides reliable estimation performance with very low mean square error (MSE). Simulation results in AWGN and multi-path fading channels confirm that performance of the proposed highly accurate blind CFO estimator is superior when frequency offset or time variation occurs in the channel - the proposed technique outperforms most existing blind CFO estimation methods.

A computationally fast and optimally smooth method for generating a probability density of y given x that models given data points is described and illustrated. This method interpolates in that the mean function intersects the points and the variance function is zero at the points. It is fast and optimal in that it is produced by the smallest number of maximally-smooth Gaussian radial interpolators for which the extrapolated density has the mean and variance of the classic linear model.

Applicability of Spectrally Modulated, Spectrally Encoded (SMSE) waveform design has been expanded for future Cognitive Radio (CR)-based Software Defined Radio (SDR) applications. As previously demonstrated, the SMSE waveform design process can exploit statistical knowledge of PU spectral and temporal behavior to maximize SMSE system throughput (bits/second) while adhering to SMSE and Primary User (PU) spectral constraints. The capacity of SMSE systems is extended here using spectral partitioning with carrier-interferometry (CI) coding to increase SMSE waveform agility in the presence of a spectrally diverse transmission channel. By adaptively varying the modulation order and optimally allocating power within each spectral partition, inherent SMSE flexibility is more fully exploited and substantially increases system throughput while meeting Power Spectral Density (PSD) constraints. A coexistent scenario is provided in which the analytic optimization of the SMSE waveform is demonstrated while meeting spectral mask requirements. Results show that spectrally partitioned CI-SMSE waveforms have a significantly greater ability to adapt to varying spectral requirements.

The impact of channel estimation error is investigated for Spectrally Modulated, Spectrally Encoded (SMSE) waveform designs in a coexistent environment containing multiple 802.11 Primary User (PU) systems. As previously demonstrated, the SMSE waveform design process can exploit statistical knowledge of PU spectral and temporal behavior to maximize SMSE system throughput (bits/second). This can be done by enforcing SMSE and PU bit error rate constraints while limiting mutual coexistent interference limited to manageable levels. Since maximum system performance requires accurate channel state knowledge at the SMSE transmitter, the presence of channel estimation error decreases the ability to design spectrally agile signals that optimally exploit coexistent spectral regions. Relative to a spectrally-only adapted system, the spectrally-temporally adapted SMSE system provides significant performance improvement by leveraging knowledge of PU temporal statistics to design temporally agile signals while maintaining desired performance levels for each system. Superiority of spectrally-temporally adapted signals is demonstrated here in terms of increased SMSE throughput (bits/symbol) and greater tolerance to increased channel estimation error.

With the proliferation of wireless devices being used, the RF spectrum's capacity continues to dwindle. In recent years, a new technology called Cognitive Radio has been advocated to solve the impending spectral drought. The premise of Cognitive Radio is that it can modify its signal to either avoid currently occupied frequency bands or alter its transmission parameters so as to cohabit the frequency band without interfering with the primary user. However, if the widespread use of Cognitive Radios and Dynamic Access Networks becomes a reality, it would enable multiple users to occupy the same frequency band There have yet to be any works published regarding how to classify the signals of multiple users, a barrier which will have great implications in the future use of Cognitive Radio. In addition to future commercial applications for multiuser signal classification, there is currently a need for this technology in the military. Military communication devices are used in scenarios where the RF spectrum is filled with jamming and interference from enemies. A method to detect and classify what signals are being used to jam and interfere would solve a significant roadblock for the military. Cyclic spectral analysis has proven to be a key tool in Cognitive Radios, giving them the ability to determine the parameters of the present signal, thus being able to modify its own transmission accordingly. Using this analysis as a foundation, we revisit the signal classification problem and propose a novel multi-user signal classification scheme using spectral correlation.

Spectrally Modulated, Spectrally Encoded (SMSE) waveforms have demonstrated considerable practical utility and remain a viable alternative for Cognitive Radio (CR) -based Software Defined Radio (SDR) applications. As demonstrated in this paper, this utility is greatly enhanced when soft decision selection and dynamic assignment of SMSE design parameters is incorporated. This paper provides the analytical development for optimizing SMSE performance in a coexistent environment containing Primary User (PU) signals. Optimization is performed by exploiting statistical knowledge of PU spectral and temporal behavior, and independently selecting SMSE intra-symbol subcarrier power and modulation order using soft decision criteria. It is shown that SMSE system throughput can be maximized while adhering to SMSE and PU bit error rate (BER) constraints while limiting mutual coexistent interference to manageable levels. For proof-of-concept demonstration, simulation results are presented for SMSE coexistent scenarios containing DSSS and OFDM-based 802.11a PU signals. A sensitivity analysis is also provided to show performance changes resulting from variation in SMSE waveform update latency and update interval. Relative to a spectrally-only adapted waveform, the spectrally-temporally adapted SMSE waveform provides significant performance improvement. Maximum improvement is achieved using statistic-based prediction of PU channel temporal conditions and the appropriate SMSE waveform design update interval.

Cognitive Radio (CR), a hierarchical Dynamic Spectrum Access (DSA) model, has been considered as a strong candidate for future communication systems improving spectrum efficiency utilizing unused spectrum of opportunity. However, to ensure the effectiveness of dynamic spectrum access, accurate signal classification in fading channels at low signal to noise ratio is essential. In this paper, a hierarchical cyclostationary-based classifier is proposed to reliably identify the signal type of a wide range of unknown signals. The proposed system assumes no a priori knowledge of critical signal statistics such as carrier frequency, carrier phase, or symbol rate. The system is designed with a multistage approach to minimize the number of samples required to make a classification decision while simultaneously ensuring the greatest reliability in the current and previous stages. The system performance is demonstrated in a variety of multipath fading channels, where several multiantenna-based combining schemes are implemented to exploit spatial diversity.

MC-CDMA (Multi-carrier Code Division Multiple Access) remains a strong candidate for next generation wireless communication systems. Due to its capability of exploiting frequency diversity, MC-CDMA provides high BER performance in multi-path fading channels. In our previous work, we proposed CI/MC-CDMA (Carrier Interferometry MC-CDMA) and demonstrated better performance relative to traditional MC-CDMA using novel polyphase CI spreading codes. In this work, we evaluate the BER performance of MC-CDMA and CI/MC-CDMA systems in the presence of narrowband interference (NBI). Specifically, theoretical analysis of BER performance for both systems in a multi-path fading channel is derived. We show that the CI/MC-CDMA system provides better NBI suppression capability than MC-CDMA and offers better BER. Simulation results over frequency selective fading channels confirm the validity of the theoretical analysis.

In this paper, we propose a novel signal classification method using cyclic spectral analysis and neural networks for multipath fading channels. The proposed system provides excellent classification performance in realistic multipath fading channels at low SNR, while assuming no a priori knowledge of the signal statistics, including carrier frequency, phase offset, or symbol rate. Due to its insensitivity to these statistics and its robustness to multipath fading channels, the spectral coherence function (SOF) is employed in the proposed system to produce a highly reliable classifier. Additionally, by employing a multiple-antenna based system, even greater advantages are achieved by exploiting spatial diversity. Numerical results demonstrate the classifier performance under a variety of channel conditions.

A new method for modeling functions that intersect given points is developed and demonstrated.This method yields a generally non-Gaussian probability density of y given x that has properties which are often desired in practice.It is shown that this density can have a smoother mean function and a variance which is never larger than that of a classic Gaussian process density.

In this paper, we propose a novel signal classification method using cyclic spectral analysis and neural network for cognitive radio applications. In cogni- tive radio, it is desirable to have an accurate and reliable signal classification algorithm which can operate at low signal to noise ratio and without knowledge of the carrier frequency and bandwidth of the target signal. Cyclic spectral analysis has been proven to be a powerful tool for classifying signals. However, the amount of data introduced by spectral analysis is too large for any classifier to employ. Hence, a spectral analysis based feature extraction has to be performed to drastically reduce the data. Specifically, we propose to use both the α profile and the frequency profile of the Spectral Coherence Function (SOF) as the feature. Numerical results show significant performance improvement compared to those of using only the α profile feature.

In this paper, we investigate the performance of a cyclic cumulant (CC) based modulation classifier for digital communication signals in flat fading channels. The proposed system assumes no a priori information regarding the signal's carrier frequency, symbol rate, or phase. For this reason, the classifier employs cyclic cumulants (CC) of different orders which are shown to be insensitive to unknown signal statistics. To capitalize on the relative strengths of different orders of CC, a hierarchical design is implemented. To further increase performance in fading channels, several multi-antenna combining schemes are investigated and compared, and are shown to provide significant performance gains over the single antenna case.