An OFDM Signal Identification Method for Wireless Communications Systems

Distinction of OFDM signals from single carrier signals is highly important for adaptive receiver algorithms and signal identification applications. OFDM signals exhibit Gaussian characteristics in time domain and fourth order cumulants of Gaussian distributed signals vanish in contrary to the cumulants of other signals. Thus fourth order cumulants can be utilized for OFDM signal identification. In this paper, first, formulations of the estimates of the fourth order cumulants for OFDM signals are provided. Then it is shown these estimates are affected significantly from the wireless channel impairments, frequency offset, phase offset and sampling mismatch. To overcome these problems, a general chi-square constant false alarm rate Gaussianity test which employs estimates of cumulants and their covariances is adapted to the specific case of wireless OFDM signals. Estimation of the covariance matrix of the fourth order cumulants are greatly simplified peculiar to the OFDM signals. A measurement setup is developed to analyze the performance of the identification method and for comparison purposes. A parametric measurement analysis is provided depending on modulation order, signal to noise ratio, number of symbols, and degree of freedom of the underlying test. The proposed method outperforms statistical tests which are based on fixed thresholds or empirical values, while a priori information requirement and complexity of the proposed method are lower than the coherent identification techniques

Non-linear echo cancellation - a Bayesian approach

Echo cancellation literature is reviewed, then a Bayesian model is introduced and it is shown how how it can be used to model and fit nonlinear channels. An algorithm for cancellation of echo over a nonlinear channel is developed and tested. It is shown that this nonlinear algorithm converges for both linear and nonlinear channels and is superior to linear echo cancellation for canceling an echo through a nonlinear echo-path channel

Time of Arrival and Angle of Arrival Estimation of LTE Signals for Positioning Applications

With the increase of services that need accurate location of the user, new techniques that cooperate with the Global Navigation Satellite System (GNSS) are necessary. Toward this objective, this thesis presents our research work about the estimation of the time of arrival (TOA) and of the angle of arrival (AOA) exploiting modern cellular signals. In particular, we focus on the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) standard, and in particular uplink and downlink reference signals are exploited to this purposes. The current release of the 3GPP LTE specification supports a UTDOA localization technique based on the Sounding Reference Signal (SRS). In real environments, however, user equipments (UE) are rarely set up to transmit this particular signal. The main original contribution of this thesis consists in a new TOA estimation method based on uplink transmission. In particular, we explore the possibility of performing radio localization exploiting the uplink Demodulation Reference Signal (DM-RS), which is always sent by UEs during data transmission. Real uplink transmissions are modeled in simulations and the performance of known algorithms like SAGE and IAA-APES are evaluated for TOA estimation. A new method to estimate the initial conditions of the SAGE algorithm is proposed and the estimation performance in uplink scenarios is evaluated. The analysis revealed that the proposed method outperforms the non-coherent initial conditions estimation proposed in the literature, when uplink transmission are used. Then, the benefits of our proposal are evaluated and the feasibility of TOA estimation exploiting the DM-RS is demonstrated by means of experiments using real DM-RS signals generated by an LTE module. A second original contribution is given by AOA estimation. In particular, the independence of AOA estimation with respect to uplink and downlink transmission is verified. According to this result, the performance of IAA-APES and SAGE in real-world AOA experiments is evaluated in the downlink scenarios. Based on the overall results, we conclude that the proposed radio localization method, exploiting the uplink Demodulation Reference Signal (DM-RS), can be extended also to joint TOA, AOA using SAGE, for hybrid localization techniques. We can also conclude that the proposed method can be easily extended to downlink transmission exploiting the cell specific reference signal (CRS)

Convexity in source separation: Models, geometry, and algorithms

Source separation or demixing is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, blind deconvolution, and even dictionary learning. Despite the recent progress in each of these applications, advances in high-throughput sensor technology place demixing algorithms under pressure to accommodate extremely high-dimensional signals, separate an ever larger number of sources, and cope with more sophisticated signal and mixing models. These difficulties are exacerbated by the need for real-time action in automated decision-making systems. Recent advances in convex optimization provide a simple framework for efficiently solving numerous difficult demixing problems. This article provides an overview of the emerging field, explains the theory that governs the underlying procedures, and surveys algorithms that solve them efficiently. We aim to equip practitioners with a toolkit for constructing their own demixing algorithms that work, as well as concrete intuition for why they work