Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

Gao, Ning; Qin, Zhijin; Jing, Xiaojun; Ni, Qiang

doi:10.1109/icc.2019.8762016

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…A power control anti-jamming scheme for massive multiple-input multiple-output (MIMO) systems [16] uses a policy hill-climbing (PHC) algorithm to select the transmit power of the base station based on the previous SINR and received jamming power to improve the average SINR and the sum data rate of all user equipments in the system. A deep Q-network-based anti-jamming scheme [17] formulates a Stackelberg dynamic game between an intelligent jammer UAV and mobile users on the ground. It then optimizes user mobility to reduce the received jamming power of the users.…”

Section: Related Workmentioning

confidence: 99%

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

Wang

et al. 2021

Intell. and Converged Netw.

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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.

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Section: Related Workmentioning

confidence: 99%

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

Wang

et al. 2021

Intell. and Converged Netw.

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“…Orderly quotes theoretically reach equilibrium prices faster than random quotes. Therefore, the Stackelberg model 19 is introduced, and the method of random quotes is improved through a dynamic game with a sequence of leaders and followers. Users and edge‐cloud servers have their own reserve prices for the price evaluation of the sensing tasks, which are their private information.…”

Section: System Model and Problem Definitionmentioning

confidence: 99%

Incentive mechanism design for edge‐cloud collaboration in mobile crowd sensing

Zhang

Chen

2020

Trans Emerging Tel Tech

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In this article, we propose a pricing‐based incentive mechanism for edge‐cloud collaboration (PIM‐EC) in mobile crowd sensing. In PIM‐EC, data can be exchanged among different regions with the support of edge‐cloud servers, which improves the data efficiency. For the utility conflicts between mobile sensing users and cloud servers in the traditional one‐stage game, we design a two‐stage game which includes the bargaining game between users and edge‐cloud servers, as well as a data trading game among different edge‐cloud servers deployed in different regions. For the first stage game, based on Stackelberg game model and Rubinstein bargaining model, we design a finite‐period dynamic bargaining algorithm. For the second stage game, based on the optimal auction mechanism and using the augmented Lagrange multiplier method, a quasi‐Newton iterative pricing algorithm is proposed. We investigate the performance of PIM‐EC through simulations. Compared with SWMA and IMC‐SS, the social welfare of PIM‐EC is increased by 41% and 29%, respectively.

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“…Several interesting issues arise from the study of flying UAVs in wireless networks, such as trajectory planning [2]- [4], energy consumption [4]- [6], security [3], etc. Particularly, the problem of quasi-stationary UAV deployment, where the locations of UAV are unchanged for the duration of interest to maximize the communication metrics, like throughput, radio coverage radius, etc., has been extensively studied in recent works [7].…”

Section: Introductionmentioning

confidence: 99%

3-D Deployment of UAV Swarm for Massive MIMO Communications

Gao

Jin

et al. 2021

IEEE J. Select. Areas Commun.

Self Cite

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We consider the uplink transmission between a multi-antenna ground station and an unmanned aerial vehicle (UAV) swarm. The UAVs are assumed as intelligent agents, which can explore their optimal three dimensional (3-D) deployment to maximize the channel capacity of the multiple input multiple output (MIMO) system. Specifically, considering the limitations of each UAV in accessing the global information of the network, we focus on a decentralized control strategy by noting that each UAV in the swarm can only utilize the local information to achieve the optimal 3-D deployment. In this case, the optimization problem can be divided into several optimization sub-problems with respect to the rank function. Due to the non-convex nature of the rank function and the fact that the optimization sub-problems are coupled, the original problem is NP-hard and, thus, cannot be solved with standard convex optimization solvers. Interestingly, we can relax the constraint condition of each sub-problem and solve the optimization problem by a formulated UAVs channel capacity maximization game. We analyze such game according to the designed reward function and the potential function. Then, we discuss the existence of the pure Nash equilibrium in the game. To achieve the best Nash equilibrium of the MIMO system, we develop a decentralized learning algorithm, namely decentralized UAVs channel capacity learning. The details of the algorithm are provided, and then, the convergence, the effectiveness and the computational complexity are analyzed, respectively. Moreover, we give some insightful remarks based on the proofs and the theoretical analysis. Also, extensive simulations illustrate that the developed learning algorithm can achieve a high MIMO channel capacity by optimizing the 3-D UAV swarm deployment with the local information.

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Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

Cited by 5 publications

References 30 publications

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

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

Incentive mechanism design for edge‐cloud collaboration in mobile crowd sensing

3-D Deployment of UAV Swarm for Massive MIMO Communications

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