Blind Estimation of OFDM System Parameters for Automatic Signal Identification

Orthogonal frequency division multiplexing (OFDM) has gained worldwide popular­ ity in broadband wireless communications recently due to its high spectral efficiency and robust performance in multipath fading channels. A growing trend of smart receivers which can support and adapt to multiple OFDM based standards auto­ matically brings the necessity of identifying different standards by estimating OFDM system parameters without a priori information. Consequently, blind estimation and identification of OFDM system parameters has received considerable research atten­ tions. Many techniques have been developed for blind estimation of various OFDM parameters, whereas estimation of the sampling frequency is often ignored. Further­ more, the estimated sampling frequency of an OFDM signal has to be very accurate for data recovery due to the high sensitivity of OFDM signals to sampling clock offset. To address the aforementioned problems, we propose a two-step cyclostation- arity based algorithm with low computational complexity to precisely estimate the sampling frequency of a received oversampled OFDM signal. With this estimated sampling frequency and oversampling ratio, other OFDM system parameters, i.e., the number of subcarriers, symbol duration and cyclic prefix (CP) length can be es­ timated based on the cyclic property from CP sequentially. In addition, modulation scheme used in the OFDM can be classified based on the higher-order statistics (HOS) of the frequency domain OFDM signal. All the proposed algorithms are verified by a lab testing system including a vec­ tor signal generator, a spectrum analyzer and a high speed digitizer. The evaluation results confirm the high precision and efficacy of the proposed algorithm in realistic scenarios

機械学習を用いたコグニティブ無線におけるOFDM信号認識に関する研究

電気通信大学201

Spectrum sensing for cognitive radio and radar systems

The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model

Exploiting the pilot pattern orthogonality of ofdma signals for the estimation of base stations number of antennas

International audienceIn a recent work, we proposed a GLR test dedicated to the identification of OFDM systems. In the present paper, we show that the proposed technique can be extended for the estimation of the number of antennas used by a base station. This extension is made possible thanks to the orthogonality property that exhibit the pilot pattern associated to the different antennas. Thanks to a multihypothesis testing we show that the number of transmitting antennas is estimated using only one antenna at the receiver and without any knowledge of the pilot sequence

Enhanced Spectrum Sensing for Cognitive Cellular Systems

This dissertation aims at improving spectrum sensing algorithms in order to effectively apply them to cellular systems. In wireless communications, cellular systems occupy a significant part of the spectrum. The spectrum usage for cellular systems are rapidly expanding due to the increasing demand for wireless services in our society. This results in radio frequency spectrum scarcity. Cellular systems can effectively handle this issue through cognitive mechanisms for spectrum utilization. Spectrum sensing plays the first stage of cognitive cycles for the adaptation to radio environments. This dissertation focuses on maximizing the reliability of spectrum sensing to satisfy regulation requirements with respect to high spectrum sensing performance and an acceptable error rate. To overcome these challenges, characteristics of noise and manmade signals are exploited for spectrum sensing. Moreover, this dissertation considers system constraints, the compatibility with the current and the trends of future generations. Newly proposed and existing algorithms were evaluated in simulations in the context of cellular systems. Based on a prototype of cognitive cellular systems (CCSs), the proposed algorithms were assessed in realistic scenarios. These algorithms can be applied to CCSs for the awareness of desired signals in licensed and unlicensed bands. For orthogonal frequency-division multiplexing (OFDM) signals, this dissertation exploits the characteristics of pilot patterns and preambles for new algorithms. The new algorithms outperform the existing ones, which also utilize pilot patterns. Additionally, the new algorithms can work with short observation durations, which is not possible with the existing algorithms. The Digital Video Broadcasting - Terrestrial (DVB-T) standard is taken as an example application for the algorithms. The algorithms can also be developed for filter bank multicarrier (FBMC) signals, which are a potential candidate for multiplexing techniques in the next cellular generations. The experimental results give insights for the reliability of the algorithms, taking system constraints v into account. Another new sensing algorithm, based on a preamble, is proposed for the DVBT2 standard, which is the second generation of of DVB system. DVB-T2 systems have been deployed in worldwide regions. This algorithm can detect DVB-T2 signals in a very short observation interval, which is helpful for the in-band sensing mode, to protect primary users (in nearly real-time) from the secondary transmission. An enhanced spectrum sensing algorithm based on cyclostationary signatures is proposed to detect desired signals in very low signal-to-noise ratios (SNRs). This algorithm can be developed to detect the single-carrier frequency division multiple access (SC-FDMA) signal, which is adopted for the uplink of long-term evolution (LTE) systems. This detector substantially outperforms the existing detection algorithms with the marginal complexity of some scalar multiplications. The test statistics are explicitly formulated in mathematical formulas, which were not presented in the previous work. The formulas and simulation results provide a useful strategy for cyclostationarity-based detection with different modulation types. For multiband spectrum sensing, an effective scheme is proposed not only to detect but also to classify LTE signals in multiple channels in a wide frequency range. To the best of our knowledge, no scheme had previously been described to perform the sensing tasks. The scheme is reliable and flexible for implementation, and there is almost no performance degradation caused by the scheme compared to single-channel spectrum sensing. The multiband sensing scheme was experimentally assessed in scenarios where the existing infrastructures are interrupted to provide mobile communications. The proposed algorithms and scheme facilitate cognitive capabilities to be applied to real cellular communications. This enables the significantly improved spectrum utilization of CCSs

Blind Demodulation of Pass Band OFDMA Signals and Jamming Battle Damage Assessment Utilizing Link Adaptation

This research focuses on blind demodulation of a pass band OFDMA signal so that jamming effectiveness can be assessed; referred to in this research as BDA. The research extends, modifies and collates work within literature to perform a new method of blindly demodulating of a passband OFDMA signal, which exhibits properties of the 802.16 Wireless MAN OFDMA standard, and presents a novel method for performing BDA via observation of SC LA. Blind demodulation is achieved by estimating the carrier frequency, sampling rate, pulse shaping filter roll off factor, synchronization parameters and CFO. The blind demodulator\u27s performance in AWGN and a perfect channel is evaluated where it improves using a greater number OFDMA DL symbols and increased CP length. Performance in a channel with a single multi-path interferer is also evaluated where the blind demodulator\u27s performance is degraded. BDA is achieved via observing SC LA modulation behavior of the blindly demodulated signal between successive OFDMA DL sub frames in two scenarios. The first is where modulation signaling can be used to observe change of SC modulation. The second assumes modulation signaling is not available and the SC\u27s modulation must be classified. Classification of SC modulation is performed using sixth-order cumulants where performance increases with the number of OFDMA symbols. The SC modulation classi er is susceptible to the CFO caused by blind demodulation. In a perfect channel it is shown that SC modulation can be classified using a variety of OFDMA DL sub frame lengths in symbols. The SC modulation classifier experienced degraded performance in a multi-path channel and it is recommended that it is extended to perform channel equalization in future work

Second-order cyclostationarity-based detection and classification of LTE SC-FDMA signals for cognitive radio

Cognitive radio (CR) was developed for utilizing the spectrum bands efficiently. Spectrum sensing and awareness represent main tasks of a CR, providing the possibility of exploiting the unused bands. In this thesis, we investigate the detection and classification of Long Term Evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) signals, which are used in uplink LTE, with applications to cognitive radio. We explore the second-order cyclostationarity of the LTE SC-FDMA signals, and apply results obtained for the cyclic autocorrelation function to signal detection and classification (in other words, to spectrum sensing and awareness). The proposed detection and classification algorithms provide a very good performance under various channel conditions, with a short observation time and at low signal-to-noise ratios, with reduced complexity. The validity of the proposed algorithms is verified using signals generated and acquired by laboratory instrumentation, and the experimental results show a good match with computer simulation results