Dr. Yifeng Zhao

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Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2020 in IEEE Transactions on Vehicular Technology

Journal Article

Reinforcement Learning Based PHY Authentication for VANETs

Tangwei Xu

27 February 2024 in IEEE Transactions on Wireless Communications

Journal Article

Energy Harvesting UAV-RIS-Assisted Maritime Communications Based on Deep Reinforcement Learning Against Jamming

Kailong Lin

With the rapid development of maritime activities, efficient and reliable maritime communications have attracted ever-increasing attention, and mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, can provide flexible and adaptable services for maritime communications. In this paper, we investigate a UAV-RIS-assisted maritime communication system under a malicious jammer, where a UAV-RIS is deployed to jointly adjust its placement and RIS surface elements to maximize the system energy efficiency (EE) and guarantee quality of service requirements against jamming attacks. In addition, an adaptive energy harvesting scheme is developed for information transmission (IT) and energy harvesting (EH) simultaneously to enhance the endurance of the UAV by deploying different IT times for each RIS element. Considering the non-convex optimization problem and highly complex maritime environments, an intelligent resource management approach based on deep reinforcement learning is proposed to jointly optimize the base station’s transmit power, placement of UAV-RIS, and RISs reflecting beamforming. Furthermore, hindsight experience replay is adopted to improve the learning efficiency and performance. The simulation results demonstrate that the proposed approach achieves the better EE and EH performances under different real-world settings compared with existing popular approaches.

1 May 2023 in IEEE Transactions on Network Science and Engineering

Journal Article

Joint Dynamic Spectrum Allocation for URLLC and eMBB in 6G Networks

6 G is a further evolution and development of 5 G to meet the need of various emerging applications of multifarious user equipment (UE). Ultra-reliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) are two typical application scenarios in 5 G, which will also be the primary service types of 6 G. Therefore, how to jointly schedule the URLLC and eMBB UEs in the 6 G network, especially in the spectrum allocation process, to increase the system throughput while the reliability and delay requirement of URLLC UEs are guaranteed, is worth studying. In this paper, we construct a double-cycle dynamic spectrum allocation model joint URLLC and eMBB, where URLLC and eMBB UEs adopt different scheduling cycles. Road side units (RSUs) are clustered based on the Many-to-Many Matching (MTM-M) algorithm to establish the channel conditions. Then, we carry out the dynamic spectrum resource allocation based on the Advanced K-Granularity Coloring (AKGC) algorithm. Extensive simulation results demonstrate the effectiveness of the dynamic spectrum resource strategy proposed in this work for the enhancement of throughput and the reduction of latency, which significantly improves the quality of experience (QoE).

1 November 2023 in IEEE Transactions on Vehicular Technology

Journal Article

TDRA: A Truthful Dynamic Reverse Auction for DAG Task Scheduling Over Vehicular Clouds

ZHANG LIU

Yifeng Zhao

Seyyedali Hosseinalipour

Zhibin Gao

Lianfen Huang

Huaiyu Dai

Vehicular Clouds (VCs) have attracted tremendous attention for offering commendable computing services to vehicles with computation-intensive tasks. Such tasks are often represented as Directed Acyclic Graphs (DAGs) consisting of several interdependent subtasks and directed edges. Processing of DAG tasks often needs pooling the computation resources of vehicles. However, the selfishness of vehicles prevents them from sharing their resources. To this end, we propose a Truthful Dynamic Reverse Auction (TDRA) mechanism to motivate vehicles to participate in service provisioning. To realize TDRA, we first propose an enumeration-based allocation strategy to optimally allocate subtasks among vehicles and obtain a Vickrey-Clarke-Groves (VCG)-based pricing strategy that can ensure the economic properties of individual rationality and truthfulness. Then, to deal with the high computational complexity of obtaining the optimal solution, we develop a near-optimal Dynamic Bilateral Ranking (DBR) allocation strategy to allocate subtasks within polynomial time and design a critical value-based pricing strategy that can also guarantee the two above-mentioned economic properties. Through simulating real-world movement traces of vehicles, we demonstrate that DBR outperforms the existing benchmarks, and verify our theoretical analysis on the economic properties of our developed pricing strategy.

2 January 2019 in Applied Sciences

Journal Article

Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks

With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems. The large-scale IoT devices access results in system overload and low utilization of energy efficiency under the existing network framework. In this paper, the cluster head uses the LTE-M protocol, and the intra-cluster uses the low-power wide-area network (LPWAN) self-networking protocol in the wireless sensor network. By a detailed analysis of the messages exchanged between the device and the base station, we describe the causes of overload and the steps of data aggregate combined with the physical channel. Then, we explore the cluster head quantity and the optimal scale in the intra-cluster under the traditional K-mean algorithm. When K is 30 under specific resources, the simulation results show that the system’s access success probability and resource utilization are optimal. Also, we propose a distributed dynamic cluster-head selection and clustering scheme based on an improved K-means algorithm. Simulation results show that the proposed scheme can reach 88.07% on the access success probability. The throughput and resource utilization are 3.5 times high than that of the optimal K-means.

26 October 2015 in Multimedia Tools and Applications

Journal Article

Active authentication with reinforcement learning based on ambient radio signals

About Me

Jinxiang Lai received the M.S. degree in Applied Mathematics from Xiamen University, China, in 2009. He is currently an associate professor with the School of General Education, Fujian Polytechnic of Water Conservancy and Electric Power, China. His research interests include actuarial mathematics, communication theory modeling, and deep learning. Deqing Wang received the Ph.D. degree in communication engineering from Xiamen University, China, in 2013. He is currently an associate professor with the Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University, Ministry of Education, Xiamen, China. He was with Georgia Institute of Technology as a visiting scholar during 2016-2017. His research interests include underwater acoustic communication and networking, underwater acoustic signal processing, and underwater acoustic physical-layer network coding. Yifeng Zhao received the Ph.D. degree in communication engineering from Xiamen University, China, in 2014. He is currently an associate professor in the Department of Information and Communication Engineering, Xiamen University. From 2016 to 2017, he was a visiting scholar at the Department of Electrical and Computer Engineering, North Carolina State University, USA. His research topics mainly include 6G wireless communication, wireless multi-domain resource management, Internet of Vehicles, integrated telepresence and computing, and wireless AI.

Dr. Yifeng Zhao

Dr. Yifeng Zhao

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