arXiv subjects

Zhen Gao

Publications and source records attributed to Zhen Gao.

At least 181 records · Page 10Linked to original sources

arXiv2021

CS-Based CSIT Estimation for Downlink Pilot Decontamination in Multi-Cell FDD Massive MIMO

Efficient channel state information at transmitter (CSIT) for frequency division duplex (FDD) massive MIMO can facilitate its backward compatibility with existing FDD cellular networks. To date, several CSIT estimation schemes have been proposed for FDD single-cell massive MIMO systems, but they fail to consider inter-cell-interference (ICI) and suffer from downlink pilot contamination in multi-cell scenario. To solve this problem, this paper proposes a compressive sensing (CS)-based CSIT estimation scheme to combat ICI in FDD multi-cell massive MIMO systems. Specifically, angle-domain massive MIMO channels exhibit the common sparsity over different subcarriers, and such sparsity is partially shared by adjacent users. By exploiting these sparsity properties, we design the pilot signal and the associated channel estimation algorithm under the framework of CS theory, where the channels associated with multiple adjacent BSs can be reliably estimated with low training overhead for downlink pilot decontamination. Simulation results verify the good downlink pilot decontamination performance of the proposed solution compared to its conventional counterparts in multi-cell FDD massive MIMO.

cs.IT↗

arXiv2021

Joint Active User Detection and Channel Estimation for Grant-Free NOMA-OTFS in LEO Constellation Internet-of-Things

The flourishing low-Earth orbit (LEO) constellation communication network provides a promising solution for seamless coverage services to Internet-of-Things (IoT) terminals. However, confronted with massive connectivity and rapid variation of terrestrial-satellite link (TSL), the traditional grant-free random-access schemes always fail to match this scenario. In this paper, a new non-orthogonal multiple-access (NOMA) transmission protocol that incorporates orthogonal time frequency space (OTFS) modulation is proposed to solve these problems. Furthermore, we propose a two-stages joint active user detection and channel estimation scheme based on the training sequences aided OTFS data frame structure. Specifically, in the first stage, with the aid of training sequences, we perform active user detection and coarse channel estimation by recovering the sparse sampled channel vectors. And then, we develop a parametric approach to facilitate more accurate result of channel estimation with the previously recovered sampled channel vectors according to the inherent characteristics of TSL channel. Simulation results demonstrate the superiority of the proposed method in this kind of high-mobility scenario in the end.

cs.IT↗

arXiv2021

Three-Dimensional Trajectory Design for Multi-User MISO UAV Communications: A Deep Reinforcement Learning Approach

In this paper, we investigate a multi-user downlink multiple-input single-output (MISO) unmanned aerial vehicle (UAV) communication system, where a multi-antenna UAV is employed to serve multiple ground terminals. Unlike existing approaches focus only on a simplified two-dimensional scenario, this paper considers a three-dimensional (3D) urban environment, where the UAV's 3D trajectory is designed to minimize data transmission completion time subject to practical throughput and flight movement constraints. Specifically, we propose a deep reinforcement learning (DRL)-based trajectory design for completion time minimization (DRL-TDCTM), which is developed from a deep deterministic policy gradient algorithm. In particular, to represent the state information of UAV and environment, we set an additional information, i.e., the merged pheromone, as a reference of reward which facilitates the algorithm design. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can continuously and adaptively learn how to adjust the UAV's movement strategy. Finally, simulation results show the superiority of the proposed DRL-TDCTM algorithm over the conventional baseline methods.

cs.IT↗

arXiv2021

Angle Estimation for Terahertz Ultra-Massive MIMO-Based Space-to-Air Communications

This paper investigates terahertz ultra-massive (UM)-MIMO-based angle estimation for space-to-air communications, which can solve the performance degradation problem caused by the dual delay-beam squint effects of terahertz UM-MIMO channels. Specifically, we first design a grouping true-time delay unit module that can significantly mitigate the impact of delay-beam squint effects to establish the space-to-air THz link. Based on the subarray selection scheme, the UM hybrid array can be equivalently considered as a low-dimensional fully-digital array, and then the fine estimates of azimuth/elevation angles at both UAVs and satellite can be separately acquired using the proposed prior-aided iterative angle estimation algorithm. The simulation results that close to Cramér-Rao lower bounds verify the effectiveness of our solution.

cs.IT↗

arXiv2021

Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach

In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a sophisticated three-dimensional (3D) environment, where the UAV's trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplified two-dimensional scenario and the availability of perfect channel state information (CSI), this paper considers a practical 3D urban environment with imperfect CSI, where the UAV's trajectory is designed to minimize data collection completion time subject to practical throughput and flight movement constraints. Specifically, inspired from the state-of-the-art deep reinforcement learning approaches, we leverage the twin-delayed deep deterministic policy gradient (TD3) to design the UAV's trajectory and present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. In particular, we set an additional information, i.e., the merged pheromone, to represent the state information of UAV and environment as a reference of reward which facilitates the algorithm design. By taking the service statuses of IoT nodes, the UAV's position, and the merged pheromone as input, the proposed algorithm can continuously and adaptively learn how to adjust the UAV's movement strategy. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can achieve a near-optimal navigation strategy. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.

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arXiv2021

Massive Access in Media Modulation Based Massive Machine-Type Communications

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive MIMO base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit a doubly structured sparsity in both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed algorithms in both uncoded and coded cases. Also, our results verify the validity of the proposed data-aided CSI update strategy.

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arXiv2021

An Edge Computing Paradigm for Massive IoT Connectivity over High-Altitude Platform Networks

With the advent of the Internet-of-Things (IoT) era, the ever-increasing number of devices and emerging applications have triggered the need for ubiquitous connectivity and more efficient computing paradigms. These stringent demands have posed significant challenges to the current wireless networks and their computing architectures. In this article, we propose a high-altitude platform (HAP) network-enabled edge computing paradigm to tackle the key issues of massive IoT connectivity. Specifically, we first provide a comprehensive overview of the recent advances in non-terrestrial network-based edge computing architectures. Then, the limitations of the existing solutions are further summarized from the perspectives of the network architecture, random access procedure, and multiple access techniques. To overcome the limitations, we propose a HAP-enabled aerial cell-free massive multiple-input multiple-output network to realize the edge computing paradigm, where multiple HAPs cooperate via the edge servers to serve IoT devices. For the case of a massive number of devices, we further adopt a grant-free massive access scheme to guarantee low-latency and high-efficiency massive IoT connectivity to the network. Besides, a case study is provided to demonstrate the effectiveness of the proposed solution. Finally, to shed light on the future research directions of HAP network-enabled edge computing paradigms, the key challenges and open issues are discussed.

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arXiv2021

LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?

The rapid development of communication technologies in the past decades has provided immense vertical opportunities for individuals and enterprises. However, conventional terrestrial cellular networks have unfortunately neglected the huge geographical digital divide, since high bandwidth wireless coverage is concentrated to urban areas. To meet the goal of ``connecting the unconnected'', integrating low Earth orbit (LEO) satellites with the terrestrial cellular networks has been widely considered as a promising solution. In this article, we first introduce the development roadmap of LEO satellite constellations (SatCons), including early attempts in LEO satellites with the emerging LEO constellations. Further, we discuss the unique opportunities of employing LEO SatCons for the delivery of integrating 5G networks. Specifically, we present their key performance indicators, which offer important guidelines for the design of associated enabling techniques, and then discuss the potential impact of integrating LEO SatCons with typical 5G use cases, where we engrave our vision of various vertical domains reshaped by LEO SatCons. Technical challenges are finally provided to specify future research directions.

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arXiv2021

Observation of Weyl point pair annihilation in a gyromagnetic photonic crystal

Weyl semimetals are gapless three-dimensional (3D) phases whose bandstructures contain Weyl point (WP) degeneracies. WPs carry topological charge and can only be eliminated by mutual annihilation, a process that generates the various topologically distinct 3D insulators. Time reversal (T) symmetric Weyl phases, containing a minimum of four WPs, have been extensively studied in real materials, photonic metamaterials, and other systems. Weyl phases with a single WP pair - the simplest configuration of WPs - are more elusive as they require T-breaking. Here, we implement a microwave-scale gyromagnetic 3D photonic crystal, and use field-mapping experiments to track a single pair of ideal WPs whose momentum space locations depend strongly on the biasing magnetic field. By continuously varying the field strength, we observe the annihilation of the WPs, and the formation of a 3D Chern insulator, a previously unrealised member of the family of 3D topological insulators (TIs). Surface measurements show, in unprecedented detail, how the Fermi arc states connecting the WPs evolve into TI surface states.

cond-mat.mes-hall↗

arXiv2021

Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems

This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels' sparsity is exploited for reducing the overhead. Firstly, we consider the uplink channel estimation for time-division duplexing systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Particularly, by exploiting the channels' structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers' channels with significantly enhanced performance. Moreover, we consider the downlink channel estimation and feedback for frequency-division duplexing systems. Similarly, the pilots at the BS and channel estimator at the users can be jointly trained as an encoder and a decoder, respectively. Besides, to further reduce the channel feedback overhead, only the received pilots on part of the subcarriers are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct the spatial-frequency channel matrix. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.

cs.IT↗

arXiv2021

FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture

With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex environments, there are various types of possible faults: soft errors caused by cosmic radiation and radioactive impurities, voltage instability, aging, temperature variations, and malicious attackers. Thus the safety risk of deploying NNs is now drawing much attention. In this paper, after the analysis of the possible faults in various types of NN accelerators, we formalize and implement various fault models from the algorithmic perspective. We propose Fault-Tolerant Neural Architecture Search (FT-NAS) to automatically discover convolutional neural network (CNN) architectures that are reliable to various faults in nowadays devices. Then we incorporate fault-tolerant training (FTT) in the search process to achieve better results, which is referred to as FTT-NAS. Experiments on CIFAR-10 show that the discovered architectures outperform other manually designed baseline architectures significantly, with comparable or fewer floating-point operations (FLOPs) and parameters. Specifically, with the same fault settings, F-FTT-Net discovered under the feature fault model achieves an accuracy of 86.2% (VS. 68.1% achieved by MobileNet-V2), and W-FTT-Net discovered under the weight fault model achieves an accuracy of 69.6% (VS. 60.8% achieved by ResNet-20). By inspecting the discovered architectures, we find that the operation primitives, the weight quantization range, the capacity of the model, and the connection pattern have influences on the fault resilience capability of NN models.

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arXiv2021

Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces

We propose a holographic version of a reconfigurable intelligent surface (RIS) and investigate its application to terahertz (THz) massive multiple-input multiple-output systems. Capitalizing on the miniaturization of THz electronic components, RISs can be implemented by densely packing sub-wavelength unit cells, so as to realize continuous or quasi-continuous apertures and to enable holographic communications. In this paper, in particular, we derive the beam pattern of a holographic RIS. Our analysis reveals that the beam pattern of an ideal holographic RIS can be well approximated by that of an ultra-dense RIS, which has a more practical hardware architecture. In addition, we propose a closed-loop channel estimation (CE) scheme to effectively estimate the broadband channels that characterize THz massive MIMO systems aided by holographic RISs. The proposed CE scheme includes a downlink coarse CE stage and an uplink finer-grained CE stage. The uplink pilot signals are judiciously designed for obtaining good CE performance. Moreover, to reduce the pilot overhead, we introduce a compressive sensing-based CE algorithm, which exploits the dual sparsity of THz MIMO channels in both the angular domain and delay domain. Simulation results demonstrate the superiority of holographic RISs over the non-holographic ones, and the effectiveness of the proposed CE scheme.

cs.IT↗

arXiv2021

Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks

The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the unique {\emph{triple delay-beam-Doppler squint effects}} of aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designed {\emph{Rotman lens}}-based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the data-aided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramér-Rao lower bounds verify the effectiveness of our solution.

cs.IT↗

arXiv2020

Deep Reinforcement Learning for Smart Grid Protection Against Coordinated Multistage Transmission Line Attacks

With the increase of connectivity in power grid, a cascading failure may be triggered by the failure of a transmission line, which can lead to substantial economic losses and serious negative social impacts. Therefore, it is very important to identify the critical lines under various types of attacks that may initiate a cascading failure and deploy defense resources to protect them. Since coordinated multistage line attacks can lead to larger negative impacts compared with a single-stage attack or a multistage attack without coordination, this paper intends to identify the critical lines under coordinated multistage attacks that may initiate a cascading failure and deploy limited defense resources optimally. To this end, we first formulate a total generation loss maximization problem with the consideration of multiple attackers and multiple stages. Due to the large size of solution space, it is very challenging to solve the formulated problem. To overcome the challenge, we reformulate the problem as a Markov game and design its components, e.g., state, action, and reward. Next, we propose a scalable algorithm to solve the Markov game based on multi-agent deep reinforcement learning and prioritized experience replay, which can determine the optimal attacking line sequences. Then, we design a defense strategy to decide the optimal defense line set. Extensive simulation results show the effectiveness of the proposed algorithm and the designed defense strategy.

eess.SY↗

arXiv2020

High order low-bit Sigma-Delta quantization for fusion frames

We construct high order low-bit Sigma-Delta $(ΣΔ)$ quantizers for the vector-valued setting of fusion frames. We prove that these $ΣΔ$ quantizers can be stably implemented to quantize fusion frame measurements on subspaces $W_n$ using $\log_2( {\rm dim}(W_n)+1)$ bits per measurement. Signal reconstruction is performed using a version of Sobolev duals for fusion frames, and numerical experiments are given to validate the overall performance.

cs.IT↗

arXiv2020

Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces

Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput. Most existing work assumes the ideal channel estimation, which can be challenging due to the high-dimensional cascaded MIMO channels and passive reflecting elements. Therefore, this paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead. Specifically, we first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. At the channel training stage, only a small proportion of elements will be successively activated to sound the partial channels. Moreover, the complete channel matrix can be reconstructed from the limited measurements based on compressive sensing, whereby the common sparsity of angular domain mmWave MIMO channels among different subcarriers is leveraged for improved accuracy. Besides, a complex-valued denoising convolution neural network (CV-DnCNN) is further proposed for enhanced performance. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art solutions.

cs.IT↗

arXiv2020

Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO

This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation problems as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize simultaneous active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity presented in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.

eess.SP↗

arXiv2020

Massive Access in Cell-Free Massive MIMO-Based Internet of Things: Cloud Computing and Edge Computing Paradigms

This paper studies massive access in cell-free massive multi-input multi-output (MIMO) based Internet of Things and solves the challenging active user detection (AUD) and channel estimation (CE) problems. For the uplink transmission, we propose an advanced frame structure design to reduce the access latency. Moreover, by considering the cooperation of all access points (APs), we investigate two processing paradigms at the receiver for massive access: cloud computing and edge computing. For cloud computing, all APs are connected to a centralized processing unit (CPU), and the signals received at all APs are centrally processed at the CPU. While for edge computing, the central processing is offloaded to part of APs equipped with distributed processing units, so that the AUD and CE can be performed in a distributed processing strategy. Furthermore, by leveraging the structured sparsity of the channel matrix, we develop a structured sparsity-based generalized approximated message passing (SS-GAMP) algorithm for reliable joint AUD and CE, where the quantization accuracy of the processed signals is taken into account. Based on the SS-GAMP algorithm, a successive interference cancellation-based AUD and CE scheme is further developed under two paradigms for reduced access latency. Simulation results validate the superiority of the proposed approach over the state-of-the-art baseline schemes. Besides, the results reveal that the edge computing can achieve the similar massive access performance as the cloud computing, and the edge computing is capable of alleviating the burden on CPU, having a faster access response, and supporting more flexible AP cooperation.

cs.IT↗