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Reinforcement learning based energy efficient robot relay for unmanned aerial vehicles against smart jamming

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Science China(Information Sciences) 2022年第1期65卷 234-246页

作者： Xiaozhen LU Jingfang JIE Zihan LIN Liang XIAO Jin LI Yanyong ZHANG Department of Information and Communication Engineering Xiamen University School of Computer Science and Technology University of Science and Technology of China School of Computer Science and Educational Software Guangzhou University

Unmanned aerial vehicles(UAVs) with limited energy resources, severe path loss, and shadowing to the ground base stations are vulnerable to smart jammers that aim to degrade the UAV communication performance and exhaust the UAV energy. The UAV anti-jamming communication performance, such as the outage probability, degrades if the robot relay is not aware of the jamming policies and the UAV network topology. In this paper, we propose a robot relay scheme for UAVs against smart jamming, which combines reinforcement learning with a function approximation approach named tile coding, to jointly optimize the robot moving distance and relay power with the unknown jamming channel states and locations. The robot mobility and relay policy are chosen based on the received jamming power, the robot received signal quality,location and energy consumption, and the bit error rate of the UAV messages. We also present a deep reinforcement learning version for the robot with sufficient computing resources. It uses three deep neural networks to choose the robot mobility and relay policy with reduced sample complexity, so as to avoid exploring dangerous policies that lead to the high outage probability of the UAV messages. The network architecture of the three networks is designed with fully connected layers instead of convolutional layers to reduce the computational complexity, which is analyzed by theoretical analyses. We provide the performance bound of the proposed schemes in terms of the bit error rate, robot energy consumption and utility based on a game-theoretic study. Simulation results show that the performance of our proposed relay schemes,including the bit error rate, the outage probability, and the robot energy consumption outperforms the existing schemes.

关键词： unmanned aerial vehicles relay jamming game theory reinforcement learning

Towards reinforcement learning in UAV relay for anti-jamming maritime communications

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Digital Communications and Networks 2023年第6期9卷 1477-1485页

作者： Chuhuan Liu Yi Zhang Guohang Niu Luliang Jia Liang Xiao Jiangxia Luan Department of Information and Communication Engineering Xiamen UniversityXiamen361005China School of Space Information Space Engineering UniversityBeijing101400China Xiamen Meiya Pico Information Co. Ltd.Xiamen361005China

Maritime communications with sea surface reflections and sea wave occlusions are susceptible to jamming attacks due to the wide geographical area and intensive wireless communication services.Unmanned Aerial Vehicles(UAVs)help relay messages to improve communication performance,but the relay policy that depends on the rapidly changing maritime environments is difficult to optimize.In this paper,a reinforcement learning-based UAV relay policy for maritime communications is proposed to resist jamming attacks.Based on previous transmission performance,the relay location,the received power of the transmitted signal and the received jamming power,this scheme optimizes the UAV trajectory and relay power to save the energy consumption and decrease the Bit-Error-Rate(BER)of the maritime signals.A deep reinforcement learning-based scheme is also proposed,which designs a deep neural network with dueling architecture to further improve the communication performance and computational complexity.The performance bounds regarding the signal to interference plus noise ratio,energy consumption and the communication utility are provided based on the Nash equilibrium of the game against jamming,and the computational complexity of the proposed schemes is analyzed.Simulation results show that the proposed schemes improve the energy efficiency and decrease the BER compared with the benchmark.

关键词： Maritime communications jamming Unmanned aerial vehicle Relay Reinforcement learning

VANET jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

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Computers, Materials & Continua 2022年第5期71卷 3589-3605页

作者： Haeri Kim Jong-Moon Chung Department of Vehicle Convergence Engineering Yonsei UniversitySeoul03722Korea School of Electrical&Electronic Engineering Yonsei UniversitySeoul03722Korea

The development of Vehicular Ad-hoc Network(VANET)technology is helping Intelligent Transportation System(ITS)services to become a reality.Vehicles can use VANETs to communicate safety messages on the road(while driving)and can inform their location and share road condition information in real-time.However,intentional and unintentional(e.g.,packet/frame collision)wireless signal jamming can occur,which will degrade the quality of communication over the channel,preventing the reception of safety messages,and thereby posing a safety hazard to the vehicle’s passengers.In this paper,VANET jamming detection applying Support Vector Machine(SVM)machine learning technology is used to classify jamming and non-jamming situations.The analysis is based on two cases which include normal traffic and heavy traffic conditions,where the results show that the probability of packet dropping will increase when many vehicles are using the wireless channel simultaneously.When using SVM classification,the most appropriate feature set applied in determining a jamming situation shows an accuracy of 98%or higher.Furthermore,more advanced jamming attacks need to be considered for preparation of more reliable and safer autonomous ITS services.Such research can use vehicular communication transmission and reception data based on selected published datasets.In this paper,an additional adversarial defense algorithm using the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)method is proposed,which assumes that evolutionary attacks of the jammer will attempt to confuse the trained classifier.The simulation results show that applying DBSCAN can improve the accuracy by elimination of outliers before conducting classification testing.

关键词： Vehicle safety VANET jamming SVM adversarial defense

jamming of packings of frictionless particles with and without shear

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Chinese Physics B 2018年第6期27卷 315-319页

作者：郑文张世允徐宁 Hefei National Laboratory for Physical Sciences at the Microscale Key Laboratory of Soft Matter Chemistry of Chinese Academy of Sciencesand Department of Physics University of Science and Technology of China

By minimizing the enthalpy of packings of frictionless particles, we obtain jammed solids at desired pressures and hence investigate the jamming transition with and without shear. Typical scaling relations of the jamming transition are recovered in both cases. In contrast to systems without shear, shear-driven jamming transition occurs at a higher packing fraction and the jammed solids are more rigid with an anisotropic force network. Furthermore, by introducing the macrofriction coefficient, we propose an explanation of the packing fraction gap between sheared and non-sheared systems at fixed pressure.

关键词： jamming shear macro-friction force network

Anti-follower jamming wide gap multi-pattern frequency hopping communication method

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Defence Technology（防务技术） 2020年第2期16卷 453-459页

作者： Yao-bei Wang Hou-de Quan Hui-xian Sun Pei-zhang Cui Department of Electronic and Optical Engineering Army Engineering UniversityShijiazhuang 050003China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu 611731China

This paper deals with the follower jamming(FJ)resistance for the frequency hopping(FH)communication system over additive white Gaussian noise(AWGN)channel.Conventional FH systems are susceptible to be jammed by FJ,and multi-pattern frequency hopping(MPFH)has good resistance to FJ.To further improve the FJ rejection capability of MPFH,we propose a wide gap multi-pattern frequency hopping(WGMPFH)scheme.WGMPFH uses channels to represent messages,and the data channel and complementary channel are hopping on orthogonal frequency slots according to wide gap FH patterns.The transmitted signal lures FJ to aim at the data channel and the complementary channel is away from FJ by adopting wide gap frequency patterns.FJ does not affect the complementary channel but increases the signal energy in the data channel,thus the effect of FJ is reduced.Its bit error rate(BER)is derived under FJ and the effects of three FJ parameters(tracking success probability,jamming duration ratio and jamming bandwidth ratio)on the BER performance of WGMPFH are investigated versus the co nventional FH/BFSK and MPFH system.Numerical and simulation results show that when under the worst-case FJ,the proposed WGMPFH outperforms the MPFH by about 1-3 dB and outperforms the conventional FH/BFSK by more than 4 dB.The proposed WGMPFH shows superior jamming rejection performance under FJ especially in severe signal-to-jamming ratio(SJR).

关键词： Frequency hopping communication Multi-pattern frequency hopping Follower jamming AWGN channel

A jamming method against bistatic SAR based on modulation theory

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Journal of Systems Engineering and Electronics 2019年第3期30卷 504-510页

作者： RAN Jinhe LI Xiuhe SHEN Yang Electronic Countermeasure Institute National University of Defense Technology

This paper focuses on the jamming problem of bistatic synthetic aperture radar (BiSAR), and a jamming method against BiSAR based on modulation theory is proposed. The proposed jamming method modulates the BiSAR signal with the cosinusoidal phase to generate multi-false targets in range, and further rotates the jammer to generate multi-false targets in azimuth. The range multi-false targets and azimuth multi-false targets form the two-dimensional cover jamming or deception jamming, which can protect the important targets efficiently. The number of false targets, the interval of false targets, and the jamming square can be adjusted flexibly by setting different range jamming parameters and azimuth jamming parameters. The jamming performance and the choosing criteria of jamming parameters are also discussed. Finally, the simulated data verify the effectiveness of the jamming method.

关键词： bistatic synthetic aperture radar (BiSAR) cosinusoidal phase cover jamming micro-motion modulation

Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks

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Journal of Rock Mechanics and Geotechnical Engineering 2020年第1期12卷 21-31页

作者： Rohola Hasanpour Jamal Rostami Jürgen Schmitt Yilmaz Ozcelik Babak Sohrabian Institute for Tunneling and Construction Management Ruhr-University BochumBochumGermany Department of Mining Engineering Colorado School of MinesGoldenUSA Department of Civil Engineering Darmstadt University of Applied ScienceDarmstadtGermany Department of Mining Engineering Hacettepe UniversityAnkaraTurkey Faculty of Mining and Metallurgical Engineering Urmia University of TechnologyUrmiaIran

This study presents an application of artificial neural network(ANN)and Bayesian network(BN)for evaluation of jamming risk of the shielded tunnel boring machines(TBMs)in adverse ground conditions such as squeezing grounds.The analysis is based on database of tunneling cases by numerical modeling to evaluate the ground convergence and possibility of machine entrapment.The results of initial numerical analysis were verified in comparison with some case studies.A dataset was established by performing additional numerical modeling of various scenarios based on variation of the most critical parameters affecting shield jamming.This includes compressive strength and deformation modulus of rock mass,tunnel radius,shield length,shield thickness,in situ stresses,depth of over-excavation,and skin friction between shield and rock.Using the dataset,an ANN was trained to predict the contact pressures from a series of ground properties and machine parameters.Furthermore,the continuous and discretized BNs were used to analyze the risk of shield jamming.The results of these two different BN methods are compared to the field observations and summarized in this paper.The developed risk models can estimate the required thrust force in both cases.The BN models can also be used in the cases with incomplete geological and geomechanical properties.

关键词： Bayesian network(BN) Artificial neural network(ANN) Shielded tunnel boring machine(TBM) jamming risk Numerical simulation Squeezing ground

Impact of Tactical Parameters of Aircraft on jamming Effectiveness of Surface-Source IR Decoy

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Computer Modeling in Engineering & Sciences 2019年第2期118卷 425-446页

作者： Shenbo Li Qi Tong Chaozhe Wang Aeronautics Engineering Institute Air Force Engineering UniversityXi’an710038China.

As the platform for surface-source infrared decoys,the target aircraft is the aim of attacks in air-to-air combat.It can quickly and accurately evaluate the jamming effectiveness of the surface-source IR decoy in various states of motion is important for enhancing the security of the aircraft.This paper proposes a model of surface-source IR decoy,and compares and analyzes simulated and measured infrared images of diffusion.A system to assess the effectiveness of the jamming of the surface-source IR decoy,target aircraft,and infrared guided missile called“Trinity”is established.Simulations were conducted to assess the impact of flight height,flight speed,and the maneuvering of the target aircraft on the jamming effectiveness of the surface-source IR decoy.The results show that with an increase in flight height and speed,the jamming probability of a surface-source IR decoy first increases and then decreases,where evasive maneuvering by the target aircraft can effectively improve this probability.When the radius of the vertical snake maneuver was 125 m,that of the horizontal snake maneuver was 200 m,and the radius of the barrel roll maneuver was 250 m,the jamming probability of the surface-source IR decoy was relatively high.By comparing the simulation results with measured data,the study verified the accuracy of the proposed method.

关键词： Surface-source IR decoy tactical parameters of aircraft jamming effectiveness infrared countermeasures effectiveness evaluation.

Distributed reinforcement learning based framework for energy-efficient UAV relay against jamming

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Intelligent and Converged Networks 2021年第2期2卷 150-162页

作者： Weihang Wang Zefang Lv Xiaozhen Lu Yi Zhang Liang Xiao Department of Information and Communication Engineering Xiamen UniversityXiamen 361005China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing 100190China

Unmanned aerial vehicle(UAV)network is vulnerable to jamming attacks,which may cause severe damage like communication outages.Due to the energy constraint,the source UAV cannot blindly enlarge the transmit power,along with the complex network topology with high mobility,which makes the destination UAV unable to evade the jammer by flying at will.To maintain communication with a limited battery capacity in the UAV networks in the presence of a greedy jammer,in this paper,we propose a distributed reinforcement learning(RL)based energy-efficient framework for the UAV networks with constrained energy under jamming attacks to improve the communication quality while minimizing the total energy consumption of the network.This framework enables each relay UAV to independently select its transmit power based on historical state-related information without knowing the moving trajectory of other UAVs as well as the jammer.The location and battery level of each UAV need not be shared with other UAVs.We also propose a deep RL based anti-jamming relay approach for UAVs with portable computation equipment like Raspberry Pi to achieve higher and faster performance.We study the Nash equilibrium(NE)and the performance bounds based on the formulated power control game.Simulation results show that the proposed schemes can reduce the bit error rate(BER)and reduce energy consumption of the UAV network compared with the benchmark method.

关键词： unmanned aerial vehicles relay jamming reinforcement learning