Eric C. Like

Signal classification in fading channels using cyclic spectral analysis

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.

Reliable Modulation Classification at Low SNR Using Spectral Correlation

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.

Modulation Recognition in Multipath Fading Channels Using Cyclic Spectral 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.

Highly Accurate Blind Carrier Frequency Offset Estimator for Mobile OFDM Systems

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.

Multi-User Signal Classification via Spectral Correlation

With the proliferation of wireless devices being used, the RF spectrums 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 multi- user 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.

Adaptive Intra-Symbol SMSE Waveform Design Amidst Coexistent Primary Users

An analytic approach is presented for optimizing Spectrally Modulated, Spectrally Encoded (SMSE) waveforms using independent selection of intra-symbol (within a symbol) subcarrier power and modulation order. The SMSE framework is well-suited for cognition-based, software defined radio (SDR) applications. By exploiting statistical knowledge about the spectral and temporal behavior of interfering signals, the inherent SMSE framework flexibility is leveraged to substantially increase system throughput while limiting coexistent interference. Results for a coexistent scenario are provided in which the analytic optimization of the SMSE waveform is demonstrated in the presence of multiple Direct Sequence Spread Spectrum (DSSS) signals. The results reveal significant performance benefits that demonstrate the potential of the SMSE framework to dynamically adapt to changing environmental conditions-key functionality required for future SDR implementations.

Modulation Recognition In Fading Channels Using Higher Order Cyclic Cumulants

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 signals 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.

Intra-symbol SMSE waveform design amidst coexistent 802.11 OFDM signals

Spectrally Modulated, Spectrally Encoded (SMSE) waveform designs are optimized in a coexistent environment by independently selecting intra-symbol subcarrier power and modulation order. As demonstrated previously with Direct Sequence Spread Spectrum (DSSS) primary users (PU), SMSE waveform applicability is extended here using a coexistent environment containing multiple OFDM-based 802.11 PU signals. By exploiting statistical knowledge of PU spectral and temporal behavior, the SMSE system throughput (bits/second) can be maximized while adhering to SMSE and PU bit error rate (BER) constraints with mutual coexistent interference limited to manageable levels. Results for the SMSE-802.11 coexistent scenario demonstrate that significant performance improvement is available when the SMSE signal dynamically adapts to PU transmissions.

Coexistent intra-symbol SMSE waveform design: Variation in waveform update latency and update rate

The impact of variation in waveform update latency and update rate 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 for no latency with a fixed update rate, 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 PU bit error rate constraints with mutual coexistent interference limited to manageable levels. Building upon this previous work, a sensitivity analysis is conducted here through parametric variation in both waveform update latency and update rate. Relative to a spectrally-only adapted waveform, the spectrally-temporally adapted waveform provides significant performance improvement. Maximum improvement is achieved using statistic-based prediction of channel temporal conditions and appropriate updating of the SMSE waveform design.

Spectrally-Temporally Adapted SMSE Waveform Design Using Imperfect Channel Estimates

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.