EM-Type Algorithms for DOA Estimation in Unknown Nonuniform Noise

The expectation--maximization (EM) algorithm updates all of the parameter estimates simultaneously, which is not applicable to direction of arrival (DOA) estimation in unknown nonuniform noise. In this work, we present several efficient EM-type algorithms, which update the parameter estimates sequentially, for solving both the deterministic and stochastic maximum--likelihood (ML) direction finding problems in unknown nonuniform noise. Specifically, we design a generalized EM (GEM) algorithm and a space-alternating generalized EM (SAGE) algorithm for computing the deterministic ML estimator. Simulation results show that the SAGE algorithm outperforms the GEM algorithm. Moreover, we design two SAGE algorithms for computing the stochastic ML estimator, in which the first updates the DOA estimates simultaneously while the second updates the DOA estimates sequentially. Simulation results show that the second SAGE algorithm outperforms the first one.Comment: arXiv admin note: text overlap with arXiv:2208.0751

Toward RIS-Enhanced Integrated Terrestrial/Non-Terrestrial Connectivity in 6G

The next generation of wireless systems will take the concept of communications and networking to another level through the seamless integration of terrestrial, aerial, satellite, maritime and underwater communication systems. Reconfigurable intelligent surface (RIS) is an innovative technology which, with its singular features and functionalities, can expedite the realization of this everywhere connectivity. Motivated by the unparalleled properties of this innovatory technology, this article provides a comprehensive discussion on how RIS can contribute to the actualization and proper functioning of future integrated terrestrial/non-terrestrial (INTENT) networks. As a case study, we explore the integration of RIS into non-orthogonal multiple access (NOMA)-based satellite communication networks and demonstrate the performance enhancement achieved by the inclusion of RIS via numerical simulations. Promising directions for future research in this area are set forth at the end of this article.Comment: This work has been accepted for publication in IEEE Networ

Full Wave-equation Based Passive Seismic Imaging and Multispectral Seismic Geometric Attributes

Both passive (e.g., microseismic) and active seismic methods are used for the hydrocarbon exploration. For example, microseismic data analysis provides helpful information in not only mapping hydraulic fracture initiation, but also reservoir monitoring and even structural imaging. In 3D seismic interpretation, geometric attributes provide effective tools to map structure and stratigraphy. However, current microseismic events locating method faces challenge due to the poor resolution of passive-seismic imaging result and the dependency of the subsurface velocity model. Further, some stratigraphic features are buried in the conventional seismic geometric attributes. Focusing on these challenges, I develop new passive seismic imaging method and multispectral geometric attributes in this dissertation to provide more effective tools for hydrocarbon exploration. To improve the quality of passive seismic imaging, I have constructed an iterative approach to locate the passive source locations and estimate the background overburden velocity based on full wave-equation methods. Specifically, I use a high-resolution geometric-mean reverse-time migration (GmRTM) to provide source locations that are better focused compared to conventional time-reversal imaging method. I also use the passive-source full-waveform inversion (FWI) to optimize the overburden velocity model. Given this accurate velocity, I use passive-source reverse-time migration to provide a structural image. Numerical experiments on the Marmousi model dataset indicate that the proposed approach can handle complicated structures and noisy seismic recordings. Recent developments in multispectral coherence based on simple band-pass filters show improvements in fault and stratigraphic edge delineation. To further improve this technology, I evaluate several different spectral decomposition algorithms to determine which, if any provide superior coherence images, I find that exponentially-spaced spectral voices provide better coherence images than linearly-spaced spectral components for the same computation cost. I also find that multispectral coherence computed from generated using the high-resolution maximum entropy algorithm provides reduced noise and better resolution of thinner channels than the other spectral decomposition algorithms. Equally important, I analyze why multispectral coherence provides more continuous fault images where conventional coherence images often exhibit gaps in areas where a human interpreter would draw a single, unbroken fault. These coherence fault occur when the displacement across the fault aligns different stratigraphic reflectors, resulting in what appears to be a continuous reflector. This same alignment also confounds auto-pickers. Although two different broadband seismic reflectors may be aligned across faults, in general, the corresponding spectral voices are not, thereby reducing their cross-correlation, and for multispectral coherence, elements in their covariance matrices, across the fault. Considering that multispectral coherence provides a better delineation of the seismic discontinuities due to data quality, thin-bed tuning, or non-stratigraphic alignments, I further investigate multispectral dip attributes, which try to combine the benefits from different spectral voices. I illustrate the multispectral gradient structure tensor (GST) dip method, which helps improve the quality of dip attributes in the conventional broadband dips. Multispectral GST dip is performed by computing the eigenvectors and eigenvalues from the multispectral gradient structure tensor matrix. I use two 3D seismic surveys to indicate the improvement using the multispectral GST dip over conventional broadband dip attributes

Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.Comment: 32 Pages, 12 figures, accepted to IEEE Transactions on Wireless Communication

ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces

The prominent progress in generative models has significantly improved the reality of generated faces, bringing serious concerns to society. Since recent GAN-generated faces are in high realism, the forgery traces have become more imperceptible, increasing the forensics challenge. To combat GAN-generated faces, many countermeasures based on Convolutional Neural Networks (CNNs) have been spawned due to their strong learning ability. In this paper, we rethink this problem and explore a new approach based on forest models instead of CNNs. Specifically, we describe a simple and effective forest-based method set called {\em ForensicsForest Family} to detect GAN-generate faces. The proposed ForensicsForest family is composed of three variants, which are {\em ForensicsForest}, {\em Hybrid ForensicsForest} and {\em Divide-and-Conquer ForensicsForest} respectively. ForenscisForest is a newly proposed Multi-scale Hierarchical Cascade Forest, which takes semantic, frequency and biology features as input, hierarchically cascades different levels of features for authenticity prediction, and then employs a multi-scale ensemble scheme that can comprehensively consider different levels of information to improve the performance further. Based on ForensicsForest, we develop Hybrid ForensicsForest, an extended version that integrates the CNN layers into models, to further refine the effectiveness of augmented features. Moreover, to reduce the memory cost in training, we propose Divide-and-Conquer ForensicsForest, which can construct a forest model using only a portion of training samplings. In the training stage, we train several candidate forest models using the subsets of training samples. Then a ForensicsForest is assembled by picking the suitable components from these candidate forest models..