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Huici Wu

Publications and source records attributed to Huici Wu.

At least 37 records · Page 2Linked to original sources

Capacity and Delay of Unmanned Aerial Vehicle Networks with Mobility

Unmanned aerial vehicles (UAVs) are widely exploited in environment monitoring, search-and-rescue, etc. However, the mobility and short flight duration of UAVs bring challenges for UAV networking. In this paper, we study the UAV networks with n UAVs acting as aerial sensors. UAVs generally have short flight duration and need to frequently get energy replenishment from the control station. Hence the returning UAVs bring the data of the UAVs along the returning paths to the control station with a store-carry-and-forward (SCF) mode. A critical range for the distance between the UAV and the control station is discovered. Within the critical range, the per-node capacity of the SCF mode is O(n/log n) times higher than that of the multi-hop mode. However, the per-node capacity of the SCF mode outside the critical range decreases with the distance between the UAV and the control station. To eliminate the critical range, a mobility control scheme is proposed such that the capacity scaling laws of the SCF mode are the same for all UAVs, which improves the capacity performance of UAV networks. Moreover, the delay of the SCF mode is derived. The impact of the size of the entire region, the velocity of UAVs, the number of UAVs and the flight duration of UAVs on the delay of SCF mode is analyzed. This paper reveals that the mobility and short flight duration of UAVs have beneficial effects on the performance of UAV networks, which may motivate the study of SCF schemes for UAV networks.

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Spectrum Sharing between UAV-based Wireless Mesh Networks and Ground Networks

The unmanned aerial vehicle (UAV)-based wireless mesh networks can economically provide wireless services for the areas with disasters. However, the capacity of air-to-air communications is limited due to the multi-hop transmissions. In this paper, the spectrum sharing between UAV-based wireless mesh networks and ground networks is studied to improve the capacity of the UAV networks. Considering the distribution of UAVs as a three-dimensional (3D) homogeneous Poisson point process (PPP) within a vertical range, the stochastic geometry is applied to analyze the impact of the height of UAVs, the transmit power of UAVs, the density of UAVs and the vertical range, etc., on the coverage probability of ground network user and UAV network user, respectively. The optimal height of UAVs is numerically achieved in maximizing the capacity of UAV networks with the constraint of the coverage probability of ground network user. This paper provides a basic guideline for the deployment of UAV-based wireless mesh networks.

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Integrated Sensing and Communication enabled Multiple Base Stations Cooperative Sensing Towards 6G

Driven by the intelligent applications of sixth-generation (6G) mobile communication systems such as smart city and autonomous driving, which connect the physical and cyber space, the integrated sensing and communication (ISAC) brings a revolutionary change to the base stations (BSs) of 6G by integrating radar sensing and communication in the same hardware and wireless resource. However, with the requirements of long-range and accurate sensing in the applications of smart city and autonomous driving, the ISAC enabled single BS still has a limitation in the sensing range and accuracy. With the networked infrastructures of mobile communication systems, multi-BS cooperative sensing is a natural choice satisfying the requirement of long-range and accurate sensing. In this article, the framework of multi-BS cooperative sensing is proposed, breaking through the limitation of single-BS sensing. The enabling technologies, including unified ISAC performance metrics, ISAC signal design and optimization, interference management, cooperative sensing algorithms, are introduced in details. The performance evaluation results are provided to verify the effectiveness of multi-BS cooperative sensing schemes. With ISAC enabled multi-BS cooperative sensing (ISAC-MCS), the intelligent infrastructures connecting physical and cyber space can be established, ushering the era of 6G promoting the intelligence of everything.

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Spectrum Sharing Towards Delay Deterministic Wireless Network: Delay Performance Analysis

To accommodate Machine-type Communication (MTC) service, the wireless network needs to support low-delay and low-jitter data transmission, realizing delay deterministic wireless network. This paper analyzes the delay and jitter of the wireless network with and without spectrum sharing. When sharing the spectrum of the licensed network, the spectrum band of wireless network can be expanded, such that the delay and jitter of data transmission are reduced. The challenge of this research is to model the relation between the delay/jitter and the parameters such as node distribution, transmit power, and bandwidth, etc. To this end, this paper applies stochastic geometry and queueing theory to analyze the outage probability of the licensed network and the delay performance of the wireless network with and without spectrum sharing. By establishing the M/G/1 queueing model for the queueing of the Base Station (BS) in the wireless network, the downlink delay and jitter are derived. Monte Carlo simulation results show that the spectrum sharing reduces the delay and jitter without causing serious interference to the licensed network, which can lay a foundation for the application of spectrum sharing in delay deterministic wireless network supporting MTC service.

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Fast Neighbor Discovery for Wireless Ad Hoc Network with Successive Interference Cancellation

Neighbor discovery (ND) is a key step in wireless ad hoc network, which directly affects the efficiency of wireless networking. Improving the speed of ND has always been the goal of ND algorithms. The classical ND algorithms lose packets due to the collision of multiple packets, which greatly affects the speed of the ND algorithms. Traditional methods detect packet collision and implement retransmission when encountering packet loss. However, they does not solve the packet collision problem and the performance improvement of ND algorithms is limited. In this paper, the successive interference cancellation (SIC) technology is introduced into the ND algorithms to unpack multiple collision packets by distinguishing multiple packets in the power domain. Besides, the multi-packet reception (MPR) is further applied to reduce the probability of packet collision by distinguishing multiple received packets, thus further improving the speed of ND algorithms. Six ND algorithms, namely completely random algorithm (CRA), CRA based on SIC (CRA-SIC), CRA based on SIC and MPR (CRA-SIC-MPR), scan-based algorithm (SBA), SBA based on SIC (SBA-SIC), and SBA based on SIC and MPR (SBA-SIC-MPR), are theoretically analyzed and verified by simulation. The simulation results show that SIC and MPR reduce the ND time of SBA by 69.02% and CRA by 66.03% averagely.

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Mutual Information Metrics for Uplink MIMO-OFDM Integrated Sensing and Communication System

As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evaluate the performance of communication and sensing. In this paper, firstly, the upper and lower bounds of communication and sensing MI are derived in details based on the interaction between communication and sensing. And the ISAC waveform is optimized by maximizing the weighted sum of sensing and communication MI. The Monte Carlo simulation results show that, compared with other waveform optimization schemes, the proposed ISAC scheme has the best overall performance.

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Waveform Design for MIMO-OFDM Integrated Sensing and Communication System: An Information Theoretical Approach

Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing.

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Joint Data Collection and Sensor Positioning in Multi-UAV-Assisted Wireless Sensor Network

Due to the high mobility and easy deployment, unmanned aerial vehicles (UAVs) have attracted much attention in the field of wireless communication and positioning. To meet the challenges of lack of infrastructure coverage, uncertain sensor position and large amount of sensing data collection in wireless sensor network (WSN), this paper presents an efficient joint data collection and sensor positioning scheme for WSN supported by multiple UAVs. Specifically, a UAV is set as the main UAV to collect data, and other UAVs are used as auxiliary UAVs for sensor positioning using time difference of arrival (TDoA). A mixed-integer non-convex optimization problem with uncertain sensor position is established. The goal is to minimize the average positioning error of all sensors by jointly optimizing the UAV trajectories, sensor transmission schedule and positioning observation points (POPs). To solve this optimization model, the original problem is decomposed into two sub-problems based on the path discrete method. Firstly, the block coordinate descent (BCD) and successive convex approximation (SCA) techniques are applied to iteratively optimize the trajectory of the main UAV and the sensor transmission schedule, so as to maximize the minimum amount of data uploaded by the sensor. Then, based on the trajectory of the main UAV, a particle swarm optimization (PSO)-based algorithm is designed to optimize the POPs of UAVs. Finally, the spline curve is applied to generate the trajectories of auxiliary UAVs. The simulation results show that the proposed scheme can meet the requirements of data collection and has a good positioning performance.

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Symbol-level Integrated Sensing and Communication enabled Multiple Base Stations Cooperative Sensing

With the support of integrated sensing and communication (ISAC) technology, mobile communication system will integrate the function of wireless sensing, thereby facilitating new intelligent applications such as smart city and intelligent transportation. Due to the limited sensing accuracy and sensing range of single base station (BS), multi-BS cooperative sensing can be applied to realize high-accurate, long-range and continuous sensing, exploiting the specific advantages of large-scale networked mobile communication system. This paper proposes a cooperative sensing method suitable to mobile communication systems, which applies symbol-level sensing information fusion to estimate the location and velocity of target. With the demodulation symbols obtained from the echo signals of multiple BSs, the phase features contained in the demodulation symbols are used in the fusion procedure, which realizes cooperative sensing with the synchronization level of mobile communication system. Compared with the signal-level fusion in the area of distributed aperture coherence-synthetic radars, the requirement of synchronization is much lower. When signal-to-noise ratio (SNR) is -5 dB, it is evaluated that symbol-level multi-BS cooperative sensing effectively improves the accuracy of distance and velocity estimation of target. Compared with single-BS sensing, the accuracy of distance and velocity estimation is improved by 40% and 72%, respectively. Compared with data-level multi-BS cooperative sensing based on maximum likelihood (ML) estimation, the accuracy of location and velocity estimation is improved by 12% and 63%, respectively. This work may provide a guideline for the design of multi-BS cooperative sensing system to exploit the widely deployed networked mobile communication system.

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Coherent Compensation based ISAC Signal Processing for Long-range Sensing

Integrated sensing and communication (ISAC) will greatly enhance the efficiency of physical resource utilization. The design of ISAC signal based on the orthogonal frequency division multiplex (OFDM) signal is the mainstream. However, when detecting the long-range target, the delay of echo signal exceeds CP duration, which will result in inter-symbol interference (ISI) and inter-carrier interference (ICI), limiting the sensing range. Facing the above problem, we propose to increase useful signal power through coherent compensation and improve the signal to interference plus noise power ratio (SINR) of each OFDM block. Compared with the traditional 2D-FFT algorithm, the improvement of SINR of range-doppler map (RDM) is verified by simulation, which will expand the sensing range.

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Throughput of Hybrid UAV Networks with Scale-Free Topology

Unmanned Aerial Vehicles (UAVs) hold great potential to support a wide range of applications due to the high maneuverability and flexibility. Compared with single UAV, UAV swarm carries out tasks efficiently in harsh environment, where the network resilience is of vital importance to UAV swarm. The network topology has a fundamental impact on the resilience of UAV network. It is discovered that scale-free network topology, as a topology that exists widely in nature, has the ability to enhance the network resilience. Besides, increasing network throughput can enhance the efficiency of information interaction, improving the network resilience. Facing these facts, this paper studies the throughput of UAV Network with scale-free topology. Introducing the hybrid network structure combining both ad hoc transmission mode and cellular transmission mode into UAV Network, the throughput of UAV Network is improved compared with that of pure ad hoc UAV network. Furthermore, this work also investigates the optimal setting of the hop threshold for the selection of ad hoc or cellular transmission mode. It is discovered that the optimal hop threshold is related with the number of UAVs and the parameters of scale-free topology. This paper may motivate the application of hybrid network structure into UAV Network.

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Spectrum Sharing between High Altitude Platform Network and Terrestrial Network: Modeling and Performance Analysis

Achieving seamless global coverage is one of the ultimate goals of space-air-ground integrated network, as a part of which High Altitude Platform (HAP) network can provide wide-area coverage. However, deploying a large number of HAPs will lead to severe congestion of existing frequency bands. Spectrum sharing improves spectrum utilization. The coverage performance improvement and interference caused by spectrum sharing need to be investigated. To this end, this paper analyzes the performance of spectrum sharing between HAP network and terrestrial network. We firstly generalize the Poisson Point Process (PPP) to curves, surfaces and manifolds to model the distribution of terrestrial Base Stations (BSs) and HAPs. Then, the closed-form expressions for coverage probability of HAP network and terrestrial network are derived based on differential geometry and stochastic geometry. We verify the accuracy of closed-form expressions by Monte Carlo simulation. The results show that HAP network has less interference to terrestrial network. Low height and suitable deployment density can improve the coverage probability and transmission capacity of HAP network.

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Iterative Signal Processing for Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC), with sensing and communication sharing the same wireless resources and hardware, has the advantages of high spectrum efficiency and low hardware cost, which is regarded as one of the key technologies of the fifth generation advanced (5G-A) and sixth generation (6G) mobile communication systems. ISAC has the potential to be applied in the intelligent applications requiring both communication and high accurate sensing capabilities. The fundamental challenges of ISAC system are the ISAC signal design and ISAC signal processing. However, the existing ISAC signal has low anti-noise capability. And the existing ISAC signal processing algorithms have the disadvantages of quantization errors and high complexity, resulting in large energy consumption. In this paper, phase coding is applied in ISAC signal design to improve the anti-noise performance of ISAC signal. Then, the effect of phase coding method on improving the sensing accuracy is analyzed. In order to improve the sensing accuracy with low-complexity algorithm, the iterative ISAC signal processing methods are proposed. The proposed methods improve the sensing accuracy with low computational complexity, realizing energy efficient ISAC signal processing. Taking the scenarios of short distance and long distance sensing into account, the iterative two-dimensional (2D) fast Fourier transform (FFT) and iterative cyclic cross-correlation (CC) methods are proposed, respectively, realizing high sensing accuracy and low computational complexity. Finally, the feasibility of the proposed ISAC signal processing methods are verified by simulation results.

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5G PRS-Based Sensing: A Sensing Reference Signal Approach for Joint Sensing and Communication System

The emerging joint sensing and communication (JSC) technology is expected to support new applications and services, such as autonomous driving and extended reality (XR), in the future wireless communication systems. Pilot (or reference) signals in wireless communications usually have good passive detection performance, strong anti-noise capability and good auto-correlation characteristics, hence they bear the potential for applying in radar sensing. In this paper, we investigate how to apply the positioning reference signal (PRS) of the 5th generation (5G) mobile communications in radar sensing. This approach has the unique benefit of compatibility with the most advanced mobile communication system available so far. Thus, the PRS can be regarded as a sensing reference signal to simultaneously realize the functions of radar sensing, communication and positioning in a convenient manner. Firstly, we propose a PRS based radar sensing scheme and analyze its range and velocity estimation performance, based on which we propose a method that improves the accuracy of velocity estimation by using multiple frames. Furthermore, the Cramer-Rao lower bound (CRLB) of the range and velocity estimation for PRS based radar sensing and the CRLB of the range estimation for PRS based positioning are derived. Our analysis and simulation results demonstrate the feasibility and superiority of PRS over other pilot signals in radar sensing. Finally, some suggestions for the future 5G-Advanced and 6th generation (6G) frame structure design containing the sensing reference signal are derived based on our study.

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UAV Assisted Data Collection for Internet of Things: A Survey

Thanks to the advantages of flexible deployment and high mobility, unmanned aerial vehicles (UAVs) have been widely applied in the areas of disaster management, agricultural plant protection, environment monitoring and so on. With the development of UAV and sensor technologies, UAV assisted data collection for Internet of Things (IoT) has attracted increasing attentions. In this article, the scenarios and key technologies of UAV assisted data collection are comprehensively reviewed. First, we present the system model including the network model and mathematical model of UAV assisted data collection for IoT. Then, we review the key technologies including clustering of sensors, UAV data collection mode as well as joint path planning and resource allocation. Finally, the open problems are discussed from the perspectives of efficient multiple access as well as joint sensing and data collection. This article hopefully provides some guidelines and insights for researchers in the area of UAV assisted data collection for IoT.

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Intelligent Reflecting Surface assisted Integrated Sensing and Communication System

High-speed communication and accurate sensing are of vital importance for future transportation system. Integrated sensing and communication (ISAC) system has the advantages of high spectrum efficiency and low hardware cost, satisfying the requirements of sensing and communication. Therefore, ISAC is considered to be a promising technology in the future transportation system. However, due to the low transmit power of signal and the influence of harsh transmission environment on radar sensing, the signal to noise ratio (SNR) at the radar receiver is low, which affects the sensing performance. This paper introduces the intelligent reflecting surface (IRS) into ISAC system. With IRS composed of M sub-surfaces implemented on the surface of the target. The SNR at the radar receiver is 20lg(M) times larger than the scheme without IRS. Correspondingly, radar detection probability is significantly improved, and Cramer-Rao Lower Bound (CRLB) for ranging and velocity estimation is reduced. This paper proves the efficiency of IRS enabled ISAC system, which motivates the implementation of IRS to enhance the sensing capability in ISAC system.

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MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review

With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor calibration, and sensor fusion, which are reviewed comprehensively. Specifically, we classify the fusion methods into data level, decision level, and feature level fusion methods. In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future. Finally, we summarize this article.

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Three-Dimensional Spectrum Occupancy Measurement using UAV: Performance Analysis and Algorithm Design

Spectrum sharing, as an approach to significantly improve spectrum efficiency in the era of 6th generation mobile networks (6G), has attracted extensive attention. Radio Environment Map (REM) based low-complexity spectrum sharing is widely studied where the spectrum occupancy measurement (SOM) is vital to construct REM. The SOM in three-dimensional (3D) space is becoming increasingly essential to support the spectrum sharing with space-air-ground integrated network being a great momentum of 6G. In this paper, we analyze the performance of 3D SOM to further study the tradeoff between accuracy and efficiency in 3D SOM. We discover that the error of 3D SOM is related with the area of the boundary surfaces of licensed networks, the number of discretized cubes, and the length of the edge of 3D space. Moreover, we design a fast and accurate 3D SOM algorithm that utilizes unmanned aerial vehicle (UAV) to measure the spectrum occupancy considering the path planning of UAV, which improves the measurement efficiency by requiring less measurement time and flight time of the UAV for satisfactory performance. The theoretical results obtained in this paper reveal the essential dependencies that describe the 3D SOM methodology, and the proposed algorithm is beneficial to improve the efficiency of 3D SOM. It is noted that the theoretical results and algorithm in this paper may provide a guideline for more areas such as spectrum monitoring, spectrum measurement, network measurement, planning, etc.

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