Cooperation between wireless network nodes is a promising technique for improving the physical layer security of wireless transmission, in terms of secrecy capacity, in the presence of multiple eavesdroppers. While existing physical layer security literature answered the question “what are the link-level secrecy capacity gains from cooperation?”, this paper attempts to answer the question of “how to achieve those gains in a practical decentralized wireless network and in the presence of a secrecy capacity cost for information exchange?”. For this purpose, we model the physical layer security cooperation problem as a coalitional game with non-transferable utility and propose a distributed algorithm for coalition formation. Through the proposed algorithm, the wireless users can autonomously cooperate and self-organize into disjoint independent coalitions, while maximizing their secrecy capacity taking into account the security costs during information exchange. We analyze the resulting coalitional structures, discuss their properties, and study how the users can self-adapt the network topology to environmental changes such as mobility. Simulation results show that the proposed algorithm allows the users to cooperate and self-organize while improving the average secrecy capacity per user up to 25.32% relative to the non-cooperative case.
Cooperation between wireless network nodes is a promising technique for improving the physical layer security of wireless transmission, in terms of secrecy capacity, in the presence of multiple eavesdroppers. While existing physical layer security literature answered the question “what are the link-level secrecy capacity gains from cooperation?”, this paper attempts to answer the question of “how to achieve those gains in a practical decentralized wireless network and in the presence of a secrecy capacity cost for information exchange?”. For this purpose, we model the physical layer security cooperation problem as a coalitional game with non-transferable utility and propose a distributed algorithm for coalition formation. Through the proposed algorithm, the wireless users can autonomously cooperate and self-organize into disjoint independent coalitions, while maximizing their secrecy capacity taking into account the security costs during information exchange. We analyze the resulting coalitional structures, discuss their properties, and study how the users can self-adapt the network topology to environmental changes such as mobility. Simulation results show that the proposed algorithm allows the users to cooperate and self-organize while improving the average secrecy capacity per user up to 25.32% relative to the non-cooperative case.
Cooperation between wireless network nodes is a promising technique for improving the physical layer security of wireless transmission, in terms of secrecy capacity, in the presence of multiple eavesdroppers. While existing physical layer security literature answered the question "what are the link-level secrecy capacity gains from cooperation?", this paper attempts to answer the question of "how to achieve those gains in a practical decentralized wireless network and in the presence of a secrecy capacity cost for information exchange?". For this purpose, we model the physical layer security cooperation problem as a coalitional game with non-transferable utility and propose a distributed algorithm for coalition formation. Through the proposed algorithm, the wireless users can autonomously cooperate and self-organize into disjoint independent coalitions, while maximizing their secrecy capacity taking into account the security costs during information exchange. We analyze the resulting coalitional structures, discuss their properties, and study how the users can self-adapt the network topology to environmental changes such as mobility. Simulation results show that the proposed algorithm allows the users to cooperate and self-organize while improving the average secrecy capacity per user up to 25.32% relative to the non-cooperative case.
In this paper, we propose a novel hybrid beamforming and jamming strategy to secure the transmission in wireless cooperative networks. It enhances the secrecy capacity by cooperatively using distributed multi-antenna techniques to maximize the secrecy capacity, as well as nullify the information leakage at the eavesdropper and the interference at the legitimate users. The optimum intermediate relays and jammers selection is modeled as a two-level coalitional game problem. A distributed coalition formation algorithm based on merge-and-split rules is devised to solve the problem. The approach enables the intermediate nodes self-organize into independent disjoint coalitions and requires much less computational complexity. Then the optimal relaying and jamming coalitions are selected to secure transmission in a cooperative way. Extensive simulations demonstrate that the proposed coalition formation method can improve the secrecy capacity significantly compared with the existing non-collaborate relaying and jamming nodes selection scheme and random selection approach.
Physical layer security has emerged as a promising technique to safeguard the information security in wireless networks. In this paper, we investigate the physical layer security issue for a wireless network where there coexist multiple users with security concerns. Specifically, we tackle the problem from a distributed perspective and formulate the secure transmissions at different users as a non-cooperative game. Consider the practical situation that the legitimate transmitter may not always have the perfect information regarding the channel state information of the eavesdropper, we adopt the robust secrecy rate to combat the potential worst cases. Accordingly, the robust Nash equilibrium is employed as the solution to the resource competition game among the users. Further, we analyze properties of the equilibrium and derive the optimal transmission strategy for each individual user to maximize its own robust secrecy rate, following which the distributed algorithm is proposed for the network-wide competition to reach the equilibrium. Finally, simulation results are provided to corroborate our theoretical findings.
This paper investigates a dynamic game framework for physical layer security (PLS)-aware wireless network with third-party collaborative users (TCUs). In the considered system, legitimate users (LUs) aim to transmit secret signals under the threat of eavesdroppers (EVs). There exists TCUs rationally selecting to form coalitions with LUs or EVs, strategically enhancing the secrecy transmission rates for LUs or increasing eavesdropping rates for EVs to obtain rewards. Due to the unpredictability of wireless systems (e.g., time-varying channel conditions, user mobility), coalitions among the three parties may be unfixed and dynamically changing. A hierarchical game integrating a matching subgame with a coalition formation subgame is formulated to model the interactions among LUs, EVs, and TCUs. In order to derive the optimal long-term strategies while solving the equilibrium of the hierarchical game, we first introduce a constrained Gale-Shapley algorithm which determines the optimal power, mode, and channel selections of TCUs with the known coalition partition, and then propose a coalition selection algorithm for reaching the stability of coalition partitions. Moreover, we develop a deep reinforcement learning (DRL)-based solution to concatenate the aforementioned algorithms, producing the long-term optimal strategies for LUs, EVs and TCUs over multiple time slots. Simulations evaluate the performance of the proposed scheme, and demonstrate its superiority over counterparts.
In this paper, we propose a novel user cooperation scheme based on weighted fractional Fourier transform (WFRFT), to enhance the physical (PHY) layer security of wireless transmissions against eavesdropping. Specifically, instead of dissipating additional transmission power for friendly jamming, by leveraging the features of WFRFT, the information bearing signal of cooperators can create an identical artificial noise effect at the eavesdropper while causing no performance degradation on the legitimate receiver. Furthermore, to form the cooperation set in an autonomous and distributed manner, we model WFRFT-based PHY-layer security cooperation problem as a coalitional game with non-transferable utility. A distributed merge-and-split algorithm is devised to facilitate the autonomous coalition formation to maximize the security capacity while accounting for the cooperation cost in terms of power consumption. We analyze the stability of the proposed algorithm and also investigate how the network topology efficiently adapts to the mobility of intermediate nodes. Simulation results demonstrate that the WFRFT-based user cooperation scheme leads to a significant performance advantage, in terms of secrecy ergodic capacity, compared with the conventional security-oriented user cooperation schemes, such as relay-jamming and cluster-beamforming.
In this paper, a novel hierarchical game framework for physical layer security (PLS) aware wireless communications with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model strategic interactions among LUs, JAs and EVs. Particularly, we first analyze stability conditions of the trilateral coalitions and propose a hedonic coalition selection and formation algorithm for reaching the stable coalition partition in each time slot. On top of this, we propose a deep reinforcement learning (DRL) based solution, which can achieve the equilibrium with long-term performance guarantees for the hierarchical game running over multiple time slots with dynamic evolutions. Simulations evaluate the proposed solution and show its superiority over counterparts.