Qinwen Deng

High mobility environment leads to severe Doppler effects and poses serious challenges to the conventional physical layer based on the widely popular orthogonal frequency division multiplexing (OFDM). The recent emergence of orthogonal time frequency space (OTFS) modulation, along with its many related variants, presents a promising solution to overcome such channel Doppler effects. This paper aims to clearly establish the relationships among the various manifestations of OTFS. Among these related modulations, we identify their connections, common features, and distinctions. Building on existing works, this work provides a general overview of various OTFS-related detection schemes and performance comparisons. We first provide an overview of OFDM and filter bank multi-carrier (FBMC) by demonstrating OTFS as a precoded FBMC through the introduction of inverse symplectic finite Fourier transform (ISFFT). We explore the relationship between OTFS and related modulation schemes with similar characteristics. We provide an effective channel model for high-mobility channels and offer a unified detection representation. We provide numerical comparisons of power spectrum density (PSD) and bit error rate (BER) to underscore the benefit of these modulation schemes in high-mobility scenarios. We also evaluate various detection schemes, revealing insights into their efficacies. We discuss opportunities and challenges for OTFS in high mobility, setting the stage for future research and development in this field.

Graph spectral analysis has emerged as an important tool to extract underlying structures among data samples. Central to graph signal processing (GSP) and graph neural networks (GNN), graph spectrum is often derived via eigen-decomposition (ED) of graph representation (adjacency/ Laplacian) matrix. Many real-world applications feature dynamic graphs whose representation matrix size varies over time. Such evolving graph usually shares part of the same structures with the previous graphs. We consider efficient ways to estimate the K dominant eigenvectors of the graph representation matrix. We focus on an iterative ED algorithm for low-rank symmetric matrices to update the top K eigen-pairs of the representation matrix for a graph with increasing node size. To accommodate the growing graph size, we propose two Incremental ED algorithms for Low Rank symmetric matrices (ILRED) based on an iterative eigen-updating strategy. We also provide analysis on the resulting error performance, computational complexity and memory usage to showcase the efficiency of ILRED. The experimental results in both synthetic and real-world datasets with the context of spectral clustering and graph filtering validate the power of the proposed ILRED algorithms.

Human body motion segmentation plays a major role in many applications, ranging from computer vision to robotics. Among a variety of algorithms, graph-based approaches have demonstrated exciting potential in motion analysis owing to their power to capture the underlying correlations among joints. However, most existing works focus on simpler single-layer geometric structures, whereas multi-layer spatial-temporal graph structure can provide more informative results. To provide an interpretable analysis on multilayer spatial-temporal structures, we revisit the emerging field of multilayer graph signal processing (M-GSP), and propose novel approaches based on M-GSP to human motion segmentation. Specifically, we model the spatial-temporal relationships via multilayer graphs (MLG) and introduce M-GSP spectrum analysis for feature extraction. We present two different M-GSP based algorithms for unsupervised segmentation in the MLG spectrum and vertex domains, respectively. Our experimental results demonstrate the robustness and effectiveness of our proposed methods.

Graph spectral analysis has emerged as an important tool to extract underlying structures among data samples. Central to graph signal processing (GSP) and graph neural networks (GNN), graph spectrum is often derived via eigen-decomposition (ED) of graph representation (adjacency/Laplacian) matrix. Many real-world applications feature dynamic graphs whose representation matrix size varies over time. Such evolving graph usually shares part of the same structures with the previous graphs. We consider efficient ways to estimate the K dominant eigen-vectors of the graph representation matrix. We focus on an iterative ED algorithm for low-rank symmetric matrices to update the top K eigen-pairs of the representation matrix for a graph with increasing node size. To accommodate the growing graph size, we propose two Incremental ED algorithm for Low Rank symmetric matrices (ILRED) algorithms based on an iterative eigen-updating strategy. We also provide analysis on the resulting error performance, computational complexity and memory usage to showcase the efficiency of ILRED. The experimental results in both synthetic and real-world datasets with the context of spectral clustering and graph filtering validate the power of the proposed ILRED algorithms.

This letter studies the mechanism of uplink multiple access and jamming suppression in an OTFS system. Specifically, we propose a novel resource hopping mechanism for orthogonal time frequency space (OTFS) systems with delay or Doppler partitioned sparse code multiple access (SCMA) to mitigate the effect of jamming in controlled multiuser uplink. We analyze the non-uniform impact of classic jamming signals such as narrowband interference (NBI) and periodic impulse noise (PIN) in delay-Doppler (DD) domain on OTFS systems. Leveraging turbo equalization, our proposed hopping method demonstrates consistent BER performance improvement under jamming over conventional OTFS-SCMA systems compared to static resource allocation schemes.

Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many real-life data sets and systems, including those in Internet of Things (IoT), are characterized by more complex interactions among distinct entities, which may represent multilevel interactions that are harder to be captured with a single-layer graph, and can be better characterized by multilayers graph connections. Such multilayer or multilevel data structures can be more naturally modeled by high-dimensional multilayer graphs (MLGs). To generalize traditional graph signal processing (GSP) over MLGs for analyzing multilevel signal features and their interactions, this work proposes a tensor-based framework of MLG signal processing (M-GSP). Specifically, we introduce core concepts of M-GSP and study properties of MLG spectral space, followed by fundamentals of MLG-based filter design. To illustrate novel aspects of M-GSP, we further explore its link with traditional signal processing and GSP. We provide example applications to demonstrate the efficacy and benefits of applying MLGs and M-GSP in practical scenarios.

This paper investigates an uplink coordinated multi-point (CoMP) coverage scenario, in which multiple mobile users are grouped for sparse code multiple access (SCMA), and served by the remote radio head (RRH) in front of them and the RRH behind them simultaneously. We apply orthogonal time frequency space (OTFS) modulation for each user to exploit the degrees of freedom arising from both the delay and Doppler domains. As the signals received by the RRHs in front of and behind the users experience respectively positive and negative Doppler frequency shifts, our proposed OTFS-based SCMA (OBSCMA) with CoMP system can effectively harvest extra Doppler and spatial diversity for better performance. Based on maximum likelihood (ML) detector, we analyze the single-user average bit error rate (ABER) bound as the benchmark of the ABER performance for our proposed OBSCMA with CoMP system. We also develop a customized Gaussian approximation with expectation propagation (GAEP) algorithm for multi-user detection and propose efficient algorithm structures for centralized and decentralized detectors. Our proposed OBSCMA with CoMP system leads to stronger performance than the existing solutions. The proposed centralized and decentralized detectors exhibit effective reception and robustness under channel state information uncertainty.

This paper investigates an uplink coordinated multi-point (CoMP) coverage scenario, in which multiple mobile users are grouped for sparse code multiple access (SCMA), and served by the remote radio head (RRH) in front of them and the RRH behind them simultaneously. We apply orthogonal time frequency space (OTFS) modulation for each user to exploit the degrees of freedom arising from both the delay and Doppler domains. As the signals received by the RRHs in front of and behind the users experience respectively positive and negative Doppler frequency shifts, our proposed OTFS-based SCMA (OBSCMA) with CoMP system can effectively harvest extra Doppler and spatial diversity for better performance. Based on maximum likelihood (ML) detector, we analyze the single-user average bit error rate (ABER) bound as the benchmark of the ABER performance for our proposed OBSCMA with CoMP system. We also develop a customized Gaussian approximation with expectation propagation (GAEP) algorithm for multi-user detection and propose efficient algorithm structures for centralized and decentralized detectors. Our proposed OBSCMA with CoMP system leads to stronger performance than the existing solutions. The proposed centralized and decentralized detectors exhibit effective reception and robustness under channel state information uncertainty.

Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many real-life datasets and systems, {including those in Internet of Things (IoT)}, are characterized by more complex interactions among distinct entities, which may represent multi-level interactions that are harder to be captured with a single-layer graph, and can be better characterized by multilayers graph connections. Such multilayer or multi-level data structure can be more naturally modeled by high-dimensional multilayer graphs (MLG)}. To generalize traditional graph signal processing (GSP) over multilayer graphs for analyzing multi-level signal features and their interactions, this work proposes a tensor-based framework of multilayer graph signal processing (M-GSP). Specifically, we introduce core concepts of M-GSP and study properties of MLG spectrum space, followed by fundamentals of MLG-based filter design. To illustrate novel aspects of M-GSP, we further explore its link with traditional signal processing and GSP. We provide example applications to demonstrate the efficacy and benefits of applying multilayer graphs and M-GSP in practical scenarios.

Efficient processing and feature extraction of large-scale point clouds are important in related computer vision and cyber-physical systems. This work investigates point cloud resampling based on hypergraph signal processing (HGSP) to better explore the underlying relationship among different points in the point cloud and to extract contour-enhanced features. Specifically, we design hypergraph spectral filters to capture multilateral interactions among the signal nodes of point clouds and to better preserve their surface outlines. Without the need and the computation to first construct the underlying hypergraph, our low complexity approach directly estimates hypergraph spectrum of point clouds by leveraging hypergraph stationary processes from the observed 3D coordinates. Evaluating the proposed resampling methods with several metrics, our test results validate the high efficacy of hypergraph characterization of point clouds and demonstrate the robustness of hypergraph-based resampling under noisy observations.