arXiv subjects

Zhi Ding

Publications and source records attributed to Zhi Ding.

At least 73 records · Page 4Linked to original sources

arXiv2021

An Efficient Hypergraph Approach to Robust Point Cloud Resampling

Efficient processing and feature extraction of largescale 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 cloud points and to extract contour-enhanced features. Specifically, we design hypergraph spectral filters to capture multi-lateral 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.

cs.CV↗

arXiv2021

OTFS Signaling for Uplink NOMA of Heterogeneous Mobility Users

We investigate a coded uplink non-orthogonal multiple access (NOMA) configuration in which groups of co-channel users are modulated in accordance with orthogonal time frequency space (OTFS). We take advantage of OTFS characteristics to achieve NOMA spectrum sharing in the delay-Doppler domain between stationary and mobile users. We develop an efficient iterative turbo receiver based on the principle of successive interference cancellation (SIC) to overcome the co-channel interference (CCI). We propose two turbo detector algorithms: orthogonal approximate message passing with linear minimum mean squared error (OAMP-LMMSE) and Gaussian approximate message passing with expectation propagation (GAMP-EP). The interactive OAMP-LMMSE detector and GAMP-EP detector are respectively assigned for the reception of the stationary and mobile users. We analyze the convergence performance of our proposed iterative SIC turbo receiver by utilizing a customized extrinsic information transfer (EXIT) chart and simplify the corresponding detector algorithms to further reduce receiver complexity. Our proposed iterative SIC turbo receiver demonstrates performance improvement over existing receivers and robustness against imperfect SIC process and channel state information uncertainty.

cs.IT↗

arXiv2021

Receiver Design for OTFS with Fractionally Spaced Sampling Approach

The recent emergence of orthogonal time frequency space (OTFS) modulation as a novel PHY-layer mechanism is more suitable in high-mobility wireless communication scenarios than traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have analyzed OTFS performance using theoretical and ideal baseband pulseshapes, a challenging and open problem is the development of effective receivers for practical OTFS systems that must rely on non-ideal pulseshapes for transmission. This work focuses on the design of practical receivers for OTFS. We consider a fractionally spaced sampling (FSS) receiver in which the sampling rate is an integer multiple of the symbol rate. For rectangular pulses used in OTFS transmission, we derive a general channel input-output relationship of OTFS in delay-Doppler domain without the common reliance on impractical assumptions such as ideal bi-orthogonal pulses and on-the-grid delay/Doppler shifts. We propose two equalization algorithms: iterative combining message passing (ICMP) and turbo message passing (TMP) for symbol detection by exploiting delay-Doppler channel sparsity and the frequency diversity gain via FSS. We analyze the convergence performance of TMP receiver and propose simplified message passing (MP) receivers to further reduce complexity. Our FSS receivers demonstrate stronger performance than traditional receivers and robustness to the imperfect channel state information knowledge.

cs.IT↗

arXiv2021

Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays

The millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) have received great attention due to their simple hardware implementation and excellent performance. In this work, we investigate the joint design of beam selection and digital precoding matrices for mmWave MU-MIMO systems with DLA to maximize the sum-rate subject to the transmit power constraint and the constraints of the selection matrix structure. The investigated non-convex problem with discrete variables and coupled constraints is challenging to solve and an efficient framework of joint neural network (NN) design is proposed to tackle it. Specifically, the proposed framework consists of a deep reinforcement learning (DRL)-based NN and a deep-unfolding NN, which are employed to optimize the beam selection and digital precoding matrices, respectively. As for the DRL-based NN, we formulate the beam selection problem as a Markov decision process and a double deep Q-network algorithm is developed to solve it. The base station is considered to be an agent, where the state, action, and reward function are carefully designed. Regarding the design of the digital precoding matrix, we develop an iterative weighted minimum mean-square error algorithm induced deep-unfolding NN, which unfolds this algorithm into a layerwise structure with introduced trainable parameters. Simulation results verify that this jointly trained NN remarkably outperforms the existing iterative algorithms with reduced complexity and stronger robustness.

cs.IT↗

arXiv2020

From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation

Graph convolutional networks (GCN) have been recently utilized to extract the underlying structures of datasets with some labeled data and high-dimensional features. Existing GCNs mostly rely on a first-order Chebyshev approximation of graph wavelet-kernels. Such a generic propagation model does not always suit the various datasets and their features. This work revisits the fundamentals of graph wavelet and explores the utility of signal propagation in the vertex domain to approximate the spectral wavelet-kernels. We first derive the conditions for representing the graph wavelet-kernels via vertex propagation. We next propose alternative propagation models for GCN layers based on Taylor expansions. We further analyze the choices of detailed graph representations for TGCNs. Experiments on citation networks, multimedia datasets and synthetic graphs demonstrate the advantage of Taylor-based GCN (TGCN) in the node classification problems over the traditional GCN methods.

eess.SP↗

arXiv2020

A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback

Forward channel state information (CSI) often plays a vital role in scheduling and capacity-approaching transmission optimization for massive multiple-input multiple-output (MIMO) communication systems. In frequency division duplex (FDD) massive MIMO systems, forwardlink CSI reconstruction at the transmitter relies critically on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent studies on the use of recurrent neural networks (RNNs) have demonstrated strong promises, though the cost of computation and memory remains high, for massive MIMO deployment. In this work, we exploit channel coherence in time to substantially improve the feedback efficiency. Using a Markovian model, we develop a deep convolutional neural network (CNN)-based framework MarkovNet to differentially encode forward CSI in time to effectively improve reconstruction accuracy. Furthermore, we explore important physical insights, including spherical normalization of input data and convolutional layers for feedback compression. We demonstrate substantial performance improvement and complexity reduction over the RNN-based work by our proposed MarkovNet to recover forward CSI estimates accurately. We explore additional practical consideration in feedback quantization, and show that MarkovNet outperforms RNN-based CSI estimation networks at a fraction of the computational cost.

eess.SP↗

arXiv2020

Point Cloud Segmentation based on Hypergraph Spectral Clustering

Hypergraph spectral analysis has emerged as an effective tool processing complex data structures in data analysis. The surface of a three-dimensional (3D) point cloud and the multilateral relationship among their points can be naturally captured by the high-dimensional hyperedges. This work investigates the power of hypergraph spectral analysis in unsupervised segmentation of 3D point clouds. We estimate and order the hypergraph spectrum from observed point cloud coordinates. By trimming the redundancy from the estimated hypergraph spectral space based on spectral component strengths, we develop a clustering-based segmentation method. We apply the proposed method to various point clouds, and analyze their respective spectral properties. Our experimental results demonstrate the effectiveness and efficiency of the proposed segmentation method.

eess.SP↗

arXiv2020

Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems

Optimization theory assisted algorithms have received great attention for precoding design in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In this work, in order to address this issue, we first propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Then, we implement the proposed deepunfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. Specifically, the iterative WMMSE algorithm is unfolded into a layer-wise structure, where a number of trainable parameters are introduced to replace the highcomplexity operations in the forward propagation. To train the network, a generalized chain rule of the IAIDNN is proposed to depict the recurrence relation of gradients between two adjacent layers in the back propagation. Moreover, we discuss the computational complexity and generalization ability of the proposed scheme. Simulation results show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.

cs.IT↗

arXiv2020

Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications

Signal processing over graphs has recently attracted significant attentions for dealing with structured data. Normal graphs, however, only model pairwise relationships between nodes and are not effective in representing and capturing some high-order relationships of data samples, which are common in many applications such as Internet of Things (IoT). In this work, we propose a new framework of hypergraph signal processing (HGSP) based on tensor representation to generalize the traditional graph signal processing (GSP) to tackle high-order interactions. We introduce the core concepts of HGSP and define the hypergraph Fourier space. We then study the spectrum properties of hypergraph Fourier transform and explain its connection to mainstream digital signal processing. We derive the novel hypergraph sampling theory and present the fundamentals of hypergraph filter design based on the tensor framework. We present HGSP-based methods for several signal processing and data analysis applications. Our experimental results demonstrate significant performance improvement using our HGSP framework over some traditional signal processing solutions.

eess.SP↗

arXiv2020

Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference

Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge. This paper presents an energy-efficient edge processing framework to execute deep learning inference tasks at the edge computing nodes whose wireless connections to mobile devices are prone to channel uncertainties. Aimed at minimizing the sum of computation and transmission power consumption with probabilistic quality-of-service (QoS) constraints, we formulate a joint inference tasking and downlink beamforming problem that is characterized by a group sparse objective function. We provide a statistical learning based robust optimization approach to approximate the highly intractable probabilistic-QoS constraints by nonconvex quadratic constraints, which are further reformulated as matrix inequalities with a rank-one constraint via matrix lifting. We design a reweighted power minimization approach by iteratively reweighted $\ell_1$ minimization with difference-of-convex-functions (DC) regularization and updating weights, where the reweighted approach is adopted for enhancing group sparsity whereas the DC regularization is designed for inducing rank-one solutions. Numerical results demonstrate that the proposed approach outperforms other state-of-the-art approaches.

cs.IT↗

arXiv2020

Hypergraph Spectral Analysis and Processing in 3D Point Cloud

Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surroundings. To process 3D point clouds efficiently, a suitable model for the underlying structure and outlier noises is always critical. In this work, we propose a hypergraph-based new point cloud model that is amenable to efficient analysis and processing. We introduce tensor-based methods to estimate hypergraph spectrum components and frequency coefficients of point clouds in both ideal and noisy settings. We establish an analytical connection between hypergraph frequencies and structural features. We further evaluate the efficacy of hypergraph spectrum estimation in two common point cloud applications of sampling and denoising for which also we elaborate specific hypergraph filter design and spectral properties. The empirical performance demonstrates the strength of hypergraph signal processing as a tool in 3D point clouds and the underlying properties.

eess.SP↗

arXiv2019

Target Localization with Jammer Removal Using Frequency Diverse Array

A foremost task in frequency diverse array multiple-input multiple-output (FDA-MIMO) radar is to efficiently obtain the target signal in the presence of interferences. In this paper, we employ a novel "low-rank + low-rank + sparse" decomposition model to extract the low-rank desired signal and suppress the jamming signals from both barrage and burst jammers. In the literature, the barrage jamming signals, which are intentionally interfered by enemy jammer radar, are usually assumed Gaussian distributed. However, such assumption is oversimplified to hold in practice as the interferences often exhibit non-Gaussian properties. Those non-Gaussian jamming signals, known as impulsive noise or burst jamming, are involuntarily deviated from friendly radar or other working radio equipment including amplifier saturation and sensor failures, thunderstorms and man-made noise. The estimation performance of the existing estimators, relied crucially on the Gaussian noise assumption, may degrade substantially since the probability density function (PDF) of burst jamming has heavier tails that exceed a few standard deviations than the Gaussian distribution. To capture a more general signal model with burst jamming in practice, both barrage jamming and burst jamming are included and a two-step "Go Decomposition" (GoDec) method via alternating minimization is devised for such mixed jamming signal model, where the $a$ $priori$ rank information is exploited to suppress two kinds of jammers and estimate the desired target. Simulation results verify the robust performance of the devised scheme.

eess.SP↗

arXiv2019

Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems

A new wave of wireless services, including virtual reality, autonomous driving and internet of things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive number of connected devices and ultra low latency. Massive multiple-input multiple-output (MIMO) is one of the critical underlying technologies that allow future wireless networks to meet these service needs. This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular deployment. We develop important insights derived from the physical radio frequency (RF) channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) with low overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.

cs.IT↗

arXiv2019

An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting

Channel state information (CSI) reporting is important for multiple-input multiple-output (MIMO) transmitters to achieve high capacity and energy efficiency in frequency division duplex (FDD) mode. CSI reporting for massive MIMO systems could consume excessive bandwidth and degrade spectrum efficiency. Deep learning (DL)-based compression integrated with channel correlations have demonstrated success in improving CSI recovery. However, existing works focusing on CSI compression have shown little on the efficient encoding of CSI report. In this paper, we propose an efficient DL-based compression framework (called CQNet) to jointly tackle CSI compression, report encoding, and recovery under bandwidth constraint. CQNet can be directly integrated within other DL-based CSI feedback works for further enhancement. CQNet significantly outperforms solutions using uniform CSI quantization and $μ$-law non-uniform quantization. Compared with traditional CSI reporting, much fewer bits are required to achieve comparable CSI reconstruction accuracy.

cs.IT↗

arXiv2019

Practical Modeling and Analysis of Blockchain Radio Access Network

The continually rising demand for wireless services and applications in the era of Internet of things (IoT) and artificial intelligence (AI) presents a significant number of unprecedented challenges to existing network structures. To meet the rapid growth need of mobile data services, blockchain radio access network (B-RAN) has emerged as a decentralized, trustworthy radio access paradigm spurred by blockchain technologies. However, many characteristics of B-RAN remain unclear and hard to characterize. In this study, we develop an analytical framework to model B-RAN and provide some basic fundamental analysis. Starting from block generation, we establish a queuing model based on a time-homogeneous Markov chain. From the queuing model, we evaluate the performance of B-RAN with respect to latency and security considerations. By connecting latency and security, we uncover a more comprehensive picture of the achievable performance of B-RAN. Further, we present experimental results via an innovative prototype and validate the proposed model.

cs.NI↗

arXiv2019

Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications

JPEG2000 (j2k) is a highly popular format for image and video compression.With the rapidly growing applications of cloud based image classification, most existing j2k-compatible schemes would stream compressed color images from the source before reconstruction at the processing center as inputs to deep CNNs. We propose to remove the computationally costly reconstruction step by training a deep CNN image classifier using the CDF 9/7 Discrete Wavelet Transformed (DWT) coefficients directly extracted from j2k-compressed images. We demonstrate additional computation savings by utilizing shallower CNN to achieve classification of good accuracy in the DWT domain. Furthermore, we show that traditional augmentation transforms such as flipping/shifting are ineffective in the DWT domain and present different augmentation transformations to achieve more accurate classification without any additional cost. This way, faster and more accurate classification is possible for j2k encoded images without image reconstruction. Through experiments on CIFAR-10 and Tiny ImageNet data sets, we show that the performance of the proposed solution is consistent for image transmission over limited channel bandwidth.

cs.CV↗

arXiv2019

A New Algorithm for Improved Blind Detection of Polar Coded PDCCH in 5G New Radio

In recent release of the new cellular standard known as 5G New Radio (5G-NR), the physical downlink control channel (PDCCH) has adopted polar codes for error protection. Similar to 4G-LTE, each active user equipment (UE) must blindly detect its own PDCCH in the downlink search space. This work investigates new ways to improve the accuracy of PDCCH blind detection in 5G-NR. We develop a novel design of joint detection and decoding receiver for 5G multiple-input multiple-output (MIMO) transceivers. We aim to achieve robustness against practical obstacles including channel state information (CSI) errors, noise, co-channel interferences, and pilot contamination. To optimize the overall receiver performance in PDCCH blind detection, we incorporate the polar code information during the signal detection stage by relaxing and transforming the Galois field code constraints into the complex signal field. Specifically, we develop a novel joint linear programming (LP) formulation that takes into consideration the transformed polar code constraints. Our proposed joint LP formulation can also be integrated with polar decoders to deliver superior receiver performance at low cost. We further introduce a metric that can be used to eliminate most of wrong PDCCH candidates to improve the computational efficiency of PDCCH blind detection for 5G-NR.

eess.SP↗

arXiv2019

Federated Learning via Over-the-Air Computation

The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center. This stimulates a nascent field termed as federated learning for training a machine learning model on computation, storage, energy and bandwidth limited mobile devices in a distributed manner. To preserve data privacy and address the issues of unbalanced and non-IID data points across different devices, the federated averaging algorithm has been proposed for global model aggregation by computing the weighted average of locally updated model at each selected device. However, the limited communication bandwidth becomes the main bottleneck for aggregating the locally computed updates. We thus propose a novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel. This is achieved by joint device selection and beamforming design, which is modeled as a sparse and low-rank optimization problem to support efficient algorithms design. To achieve this goal, we provide a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection. A DC algorithm is further developed to solve the resulting DC program with global convergence guarantees. The algorithmic advantages and admirable performance of the proposed methodologies are demonstrated through extensive numerical results.

cs.LG↗