Kan Zheng

Beijing University of Posts and Telecommunications | BUPT · School of Infomormation and Communication Engineering

Wireless networks are expected to support a diverse range of quality of service requirements and traffic characteristics. This paper undertakes stochastic performance analysis of a wireless finite-state Markov channel (FSMC) by using {\em stochastic network calculus}. Particularly, delay and backlog upper bounds are derived directly based on the an...

The escalating tele-traffic growth imposed by the proliferation of smart-phones and tablet computers outstrips the capacity increase of wireless communications networks. Furthermore, it results in substantially increased carbon dioxide emissions. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentiall...

With the rapid development of the Intelligent Transportation System (ITS), vehicular communication networks have been widely studied in recent years. Dedicated Short Range Communication (DSRC) can provide efficient real-time information exchange among vehicles without the need of pervasive roadside communication infrastructure. Although mobile cell...

Vehicle-to-vehicle (V2V) communications are considered to be a significant step forward toward a highly secure and efficient intelligent transportation system. In this paper, we propose the use of graph theory to formulate the problem of cooperative communications scheduling in vehicular networks. In lieu of exhaustive search with intractable compl...

Vehicular ad hoc networks are expected to significantly improve traffic safety and transportation efficiency while providing a comfortable driving experience. However, available communication, storage, and computation resources of the connected vehicles are not well utilized to meet the service requirements of intelligent transportation systems. Ve...

In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and loa...

Beamforming techniques have been widely used in the millimeter wave (mmWave) bands to mitigate the path loss of mmWave radio links as the narrow straight beams by directionally concentrating the signal energy. However, traditional mmWave beam management algorithms usually require excessive channel state information overhead, leading to extremely hi...

Beamforming techniques have been widely used in the millimeter-wave (mm-wave) bands to mitigate the path loss of mm-wave radio links as narrow straight beams by directionally concentrating the signal energy. However, traditional mm-wave beam management algorithms usually require excessive channel state information (CSI) overhead, leading to extreme...

In this article, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and l...

With a plethora of remote sensing (RS) images, deep neural network-based semantic segmentation models (SegModels) achieve commendable road extraction performance. However, the occlusions caused by vehicles, roadside objects and shadows cannot be directly identified as road pixels, especially on high-resolution RS images. Therefore, relying only on...

With the wide range of Internet of things (IoT) applications, Federated Learning (FL) is commonly adopted to protect the privacy of IoT data. FL enables privacy-preserving model training while keeping the data locally available. To alleviate the additional load caused by FL, an improved hierarchical aggregation framework is presented in this paper...

Internet of things, supported by machine-to-machine (M2M) communications, is one of the most important applications for the 6th generation (6G) systems. A major challenge facing by 6G is enabling a massive number of M2M devices to access networks in a timely manner. Therefore, this paper exploits the spatial selectivity of massive multi-input multi...

With the emergence of Blockchain-based Internet of Things (BIoT) applications, smart contracts have become one of the most appealing aspects because they reduce the cost and complexity of distributed administration. However, the immaturity of smart contracts may result in significant financial losses or the leakage of sensitive information. The pap...

Autonomous driving vehicles can reduce congestion and improve safety while increasing traffic efficiency. To reflect the quality of driving more comprehensively, the driving safety, efficiency and occupant comfort should be jointly optimized for autonomous vehicles. Furthermore, in order to cope with complicated traffic environments and achieve sat...

Internet of things, supported by machine-to-machine (M2M) communications, is one of the most important applications for future 6th generation (6G) systems. A major challenge facing by 6G is enabling a massive number of M2M devices to access networks in a timely manner. Therefore, this paper exploits the spatial selectivity of massive multi-input mu...

The dual-function radar communication (DFRC) is an essential technology in Internet of Vehicles (IoV). Consider that the road-side unit (RSU) employs the DFRC signals to sense the vehicles' position state information (PSI), and communicates with the vehicles based on PSI. The objective of this paper is to minimize the maximum communication delay am...

Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle. However, it is more challenging to learn a stable and efficient car-following policy when there are multiple following vehicles in a platoon, especially with unpredictable leading vehicle b...

Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management. In this paper, deep reinforcement learning (DRL) is applied to learn an optimal policy for making joint energy dispatch (ED) and unit commitment (UC) decisions in an isolated MG, with the...

The impact of Vehicle-to-Everything (V2X) communications on platoon control performance is investigated. Platoon control is essentially a sequential stochastic decision problem (SSDP), which can be solved by Deep Reinforcement Learning (DRL) to deal with both the control constraints and uncertainty in the platoon leading vehicle's behavior. In this...

The impact of Vehicle-to-Everything (V2X) communications on platoon control performance is investigated. Platoon control is essentially a sequential stochastic decision problem (SSDP), which can be solved by Deep Reinforcement Learning (DRL) to deal with both the control constraints and uncertainty in the platoon leading vehicle’s behavior. In this...

The dual-function radar communication (DFRC) is an essential technology in Internet of Vehicles (IoV). Consider that the road-side unit (RSU) employs the DFRC signals to sense the vehicles' position state information (PSI), and communicates with the vehicles based on PSI. The objective of this paper is to minimize the maximum communication delay am...

div> The conventional deep learning model performs well for traffic flow analysis by training with a large number of labeled data using a one-model-for-one-task approach, leading to huge computational complexity in dynamic intelligent transportation system (ITS) applications. To overcome this limitation, this paper propose a Token-based Self-Super...

div> The conventional deep learning model performs well for traffic flow analysis by training with a large number of labeled data using a one-model-for-one-task approach, leading to huge computational complexity in dynamic intelligent transportation system (ITS) applications. To overcome this limitation, this paper propose a Token-based Self-Super...

Autonomous vehicles in a platoon determine the control inputs based on the system state information collected and shared by the Internet of Things (IoT) devices. Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle. However, it is more challen...

The integration of the blockchain and Internet of Things (IoT) systems can effectively guarantee data security in IoT applications. To facilitate the use of blockchain on resource-constrained IoT end-devices, we propose RAFT+ with a new leader selection scheme in this paper, which is based on the distributed consensus algorithm RAFT. The design of...

Consider a base station (BS) relying on a massive antenna array, which transmits information to multiple vehicles of vehicular networks. In order to jointly consider both the communication resource consumption and the road-traffic efficiency, we define a metric given by the BS’s downlink power normalized by the vehicular velocity. We refer to it as...

To accurately localize unmanned aerial vehicles (UAVs) is one of the key issues to deal with the security threats caused by UAVs. Thus, this article proposes a UAV localization scheme that utilizes the area grid quantization and transmit power statistical calibration techniques, in which the location and transmit power of the UAV are unknown. First...

This paper presents a comprehensive survey of federated reinforcement learning (FRL), an emerging and promising field in reinforcement learning (RL). Starting with a tutorial of federated learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance...

Applying of network slicing in vehicular networks becomes a promising paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse quality of service (QoS) requirements. However, achieving effective network slicing in dynamic vehicular communications still faces many challenges, particularly time-varying traffic of Vehicle-to-Veh...

This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance...

Applying of network slicing in vehicular networks becomes a promising paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse quality of service (QoS) requirements. However, achieving effective network slicing in dynamic vehicular communications still faces many challenges, particularly time-varying traffic of Vehicle-to-Veh...

Network slicing is a key paradigm in 5G and is expected to be inherited in future 6G networks for the concurrent provisioning of diverse quality of service (QoS). Unfortunately, effective slicing of Radio Access Networks (RAN) is still challenging due to time-varying network situations. This paper proposes a new intelligent RAN slicing strategy wit...

Efficiency and security have become critical issues during the development of the long-range (LoRa) system for Internet-of-Things (IoT) applications. The centralized work method in the LoRa system, where all packages are processed and kept in the central cloud, cannot well exploit the resources in LoRa gateways and also makes it vulnerable to secur...

The integration of multi-access edge computing (MEC) and RAFT consensus makes it feasible to deploy blockchain on trustful base stations and gateways to provide efficient and tamper-proof edge data services for Internet of Things (IoT) applications. However, reducing the latency of storing data on blockchain remains a challenge, especially when an...

The integration of multi-access edge computing (MEC) and RAFT consensus makes it feasible to deploy blockchain on trustful base stations and gateways to provide efficient and tamper-proof edge data services for Internet of Things (IoT) applications. However, reducing the latency of storing data on blockchain remains a challenge, especially when an...

Efficiency and security have become critical issues during the development of the long-range (LoRa) system for Internet-of-Things (IoT) applications. The centralized work method in the LoRa system, where all packages are processed and kept in the central cloud, cannot well exploit the resources in LoRa gateways and also makes it vulnerable to secur...

Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This paper focuses on deep reinforcement learning (DRL)-based energy dispatch for IoT-drive...

Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service (QoS) in the 5th Generation (5G)-beyond and the 6th Generation (6G) networks. However, effective slicing of Radio Access Network (RAN) is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks. In th...

In this letter, an angle adjustment method is proposed to improve the accuracy of the sampling frequency offset (SFO) estimation for the very high throughput wireless local area networks (WLANs). This angle adjustment can work together with existing least square (LS) and weighted least square (WLS) to achieve better system performance. Simulation r...

A successive interference cancellation (SIC)-based weighted least-squares (WLS) estimation for the carrier frequency offset (CFO) and the sampling frequency offset (SFO) is presented for wireless local area networks (WLANs) based on orthogonal frequency division multiplexing (OFDM). The proposed SIC-based WLS performs the estimation by exploiting t...

Big data analytics has been rapidly integrated into Intelligent Transportation System (ITS), empowering diverse applications such as real-time traffic prediction and management. However, incomplete traffic time-series data during the data analysis are nearly inevitable due to the constraints of data collection or packet loss in the communication pr...

Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability. However, it is usually complex and has to be solved by large-scale simulation which requires extensive computing resources. In this paper, we propose a novel anomaly detection scheme in non-linear dynamical system based on Long Short-Term Mem...

Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability. However, it is usually complex and has to be solved by large-scale simulation which requires extensive computing resources. In this paper, we propose a novel anomaly detection scheme in non-linear dynamical system based on Long Short-Term Mem...

With the rapid development of wireless communications, space-domain technology has been widely studied in the past decades. Multiple-input multiple-output (MIMO), as a typical space-domain technology, has vast potential to provide high information rates and to improve system reliability, and thus was adopted in the fourth generation (4G) cellular n...

With the rapid development of communications and computing, the concept of connected vehicles emerges to improve driving safety, traffic efficiency and the infotainment experience. Due to the limited capabilities of sensors and information processing on a single vehicle, vehicular networks (VNETs) play a vital role for the realization of connected...

To efficiently support real-time control applications, networked control systems operating with ultra-reliable and low-latency communications (URLLCs) become a fundamental technology for future Internet of things (IoT). However, the design of control, sensing and communications is generally isolated at present. In this paper, we investigate the joi...

The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an ex...

By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet of Things (IoT) devices can be processed and analyzed at the network edge. However, the MEC system usually only has the limited virtual resources, which are shared and competed by IoT edge applications. Thus, we propose a resource allocation policy for the IoT ed...

In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on hidden Markov model (HMM) is proposed for auto...

The rapid development of the fifth generation mobile communication systems accelerates the implementation of vehicle-to-everything communications. Compared with the other types of vehicular communications, vehicle-to-vehicle (V2V) communications mainly focus on the exchange of driving safety information with neighboring vehicles, which requires ult...

To efficiently support the real-time control applications, networked control systems operating with ultra-reliable and low-latency communications (URLLCs) become fundamental technology for future Internet of things (IoT). However, the design of control, sensing and communications is generally isolated at present. In this paper, we propose the joint...

The rapid development of the fifth generation mobile communication systems accelerates the implementation of vehicle-to-everything communications. Compared with the other types of vehicular communications, vehicle-to-vehicle (V2V) communications mainly focus on the exchange of driving safety information with neighboring vehicles, which requires ult...

Ultra-reliable and low-latency communication (URLLC) is one of the three major service classes supported by the fifth generation (5G) New Radio (NR) technical specifications. In this paper, we introduce a physical layer architecture that can meet the low-latency and high-reliability requirements. The downlink system is designed according to the Thi...

In recent years, with the development of the Global Navigation Satellite System (GNSS), the satellite navigation technology has played a crucial role in smartphone navigation. To solve the problem of the low positioning accuracy in the smartphones based on GNSS, this paper proposes to apply real-time dynamic carrier phase difference technique (RTK)...

As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of industry use cases ranging from governance to finance. In this paper, we propose a model to analyze the performance of a Hyperledgerbased system by using Generalised Stochastic Petri Nets (GSPN). This model decomposes a transaction flow into multiple...

This paper focuses on deep reinforcement learning (DRL)-based energy dispatch for isolated microgrids (MGs) with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon Partial Observable Markov Decision Process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future el...

As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of industry use cases ranging from governance to finance. In this paper, we propose a model to analyze the performance of a Hyperledger-based system by using Generalized Stochastic Petri Nets (GSPN). This model decomposes a transaction flow into multiple...

Patent citations are significant components of patents, which play a vital role in the implementation of patent analysis. However, most of the existed models only focus on the text of patents and do not realize that citations can remedy missing information in the text. A method for citation modeling in patent analysis is proposed to generate patent...

To efficiently support safety-related vehicular applications, the ultra-reliable and low-latency communication (URLLC) concept has become an indispensable component of vehicular networks (VNETs). Due to the high mobility of VNETs, exchanging near-instantaneous channel state information (CSI) and making reliable resource allocation decisions based o...

In this paper, we propose an improved weighted least square (IWLS) method to estimate and compensate phase variations utilizing pilots, for Orthogonal Frequency Division Multiplexing (OFDM) based very high throughput wireless local area networks (WLANs). The remaining phase is composed of the common phase error (CPE) and the sampling time offset (S...

Short-term traffic prediction (STTP) is one of the most critical capabilities in Intelligent Transportation Systems (ITS), which can be used to support driving decisions, alleviate traffic congestion and improve transportation efficiency. However, STTP of large-scale road networks remains challenging due to the difficulties of effectively modeling...

The broadcasting services of vehicles in both vehicular networks and unmanned aerial vehicle (UAV) networks can be seen as the typical applications of all‐to‐all (A2A) scenario. In order to achieve efficient information broadcasting in A2A scenarios, this study proposes a two‐stage resource allocation scheme. In the first stage, to improve the freq...

Accurate clock synchronization in Industrial Internet of Things (IIoT) systems forms the cornerstone of distributed interaction and coordination among various infrastructures and machines in industrial environment. However, due to the widespread use of wireless networks in industrial applications, constraints inherent to wireless networks including...

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both the commun...

Long Range (LoRa) network is emerging as one of the most promising Low Power Wide Area (LPWA) networks, since it enables the energy-constraint devices distributed over wide areas to establish affordable connectivity. However, how to implement a cost-effective and flexible LoRa network is still an open challenge. This paper aims at exposing a feasib...

The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, which collect and share information to reflect the status of physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of...