Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack which can learn the optimal attack strategy in complex communication environments. In this paper, we propose an anti-intelligent UAV jamming strategy, in which the mobile users can learn the optimal defense strategy to prevent jamming. Specifically, the UAV jammer acts as a leader and the users act as followers. The problem is formulated as a stackelberg dynamic game, which includes the leader sub-game and the followers sub-game. As the UAV jammer is only aware of the incomplete channel state information (CSI) of the users, we model the leader sub-game as a partially observable Markov decision process (POMDP). The optimal jamming trajectory is obtained via deep recurrent Q-networks (DRQN) in the three-dimension space. For the followers sub-game, we use the Markov decision process (MDP) to model it. Then the optimal communication trajectory can be learned via deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium. The simulations show that the proposed strategy outperforms the benchmark strategies

A jamming power control game with unknown user’s communication metric

We consider a jamming problem in which a jammer aims to degrade a user’s communication in which the user might differ in applied applications or communication purposes. Such differences are reflected by different communication metrics used by the user. Specifically, signal-to-interference-plus-noise ratio (SINR) is used as a metric to reflect regular data transmission purposes. Meanwhile, as another metric, latency, modeled by the inverse SINR, is used to reflect emergency communication purposes. We consider the most difficult scenario for the jammer where it does not know which application (metric) the user employs. The problem is formulated as a Bayesian game. Equilibrium is found in closed form, and the dependence of equilibrium on network parameters is illustrated

Game theory for collaboration in future networks

Cooperative strategies have the great potential of improving network performance and spectrum utilization in future networking environments. This new paradigm in terms of network management, however, requires a novel design and analysis framework targeting a highly flexible networking solution with a distributed architecture. Game Theory is very suitable for this task, since it is a comprehensive mathematical tool for modeling the highly complex interactions among distributed and intelligent decision makers. In this way, the more convenient management policies for the diverse players (e.g. content providers, cloud providers, home providers, brokers, network providers or users) should be found to optimize the performance of the overall network infrastructure. The authors discuss in this chapter several Game Theory models/concepts that are highly relevant for enabling collaboration among the diverse players, using different ways to incentivize it, namely through pricing or reputation. In addition, the authors highlight several related open problems, such as the lack of proper models for dynamic and incomplete information games in this area.info:eu-repo/semantics/acceptedVersio

Game theory for cooperation in multi-access edge computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio

Outage Constrained Robust BeamformingOptimization for Multiuser IRS-AssistedAnti-Jamming Communications With Incomplete Information

Malicious jamming attacks have been regarded asa serious threat to Internet of Things (IoT) networks, which cansignificantly degrade the quality of service (QoS) of users. Thispaper utilizes an intelligent reflecting surface (IRS) to enhanceanti-jamming performance due to its capability in reconfiguringthe wireless propagation environment via dynamicly adjustingeach IRS reflecting elements. To enhance the communicationperformance against jamming attacks, a robust beamformingoptimization problem is formulated in a multiuser IRS-assistedanti-jamming communications scenario with or without imperfectjammer’s channel state information (CSI). In addition, we furtherconsider the fact that the jammer’s transmit beamforming cannot be known at BS. Specifically, with no knowledge of jammerstransmit beamforming, the total transmit power minimizationproblems are formulated subject to the outage probability re-quirements of legitimate users with the jammer’s statistical CSI,and signal-to-interference-plus-noise ratio (SINR) requirementsof legitimate users without the jammer’s CSI, respectively.By applying the Decomposition-based large deviation inequal-ity (DBLDI), Bernstein-type inequality (BTI), Cauchy-Schwarzinequality, and penalty non-smooth optimization method, weefficiently solve the initial intractable and non-convex problems.Numerical simulations demonstrate that the proposed anti-jamming approaches achieve superior anti-jamming performanceand lower power-consumption compared to the non-IRS schemeand reveal the impact of key parameters on the achievable systemperformance

Optimal decision making in cognitive radio networks

Cognitive Radio Networks are being researched upon heavily in the various layers of the communication structure. The task of bringing software in the physical layer of communication system led to the concept of a smart radio being able to learn, adapt and make intelligent decisions in an autonomous manner by use of a Software Defined Radio. This work provides novel concepts in the areas of spectrum sensing, learning of ongoing transmissions through Reinforcment learning, use of a game theoretic concept such as Zero-sum game for resilience of authorized users in cases of jamming, and decision making of user transmissions through Markov Decision processes. This is highly applicable in dynamic radio environments such as emergency communications required during natural disasters, large scale events and in mobile wireless communications. Such applications come under the "Internet of Things"