This paper investigates a multiple unmanned aerial vehicles (UAVs) enabled cooperative secure transmission scheme in the presence of multiple potential eavesdroppers. Specifically, multiple source UAVs (SUAVs) send confidential information to multiple legitimate ground users and in the meantime multiple jamming UAVs (JUAVs) cooperatively transmit interference signals to multiple eavesdroppers in order to improve the legitimate users' achievable secrecy rate. By taking into account the limited energy budget of UAV, our goal is to maximize the system secrecy energy efficiency (SEE), namely the achievable secrecy rate per energy consumption unit, by jointly optimizing the UAV trajectory, transmit power and user scheduling under the constraints of UAV mobility as well as the maximum transmit power. The resulting optimization problem is shown to be a non-convex and mixed-integer fractional optimization problem, which is challenging to solve. We decompose the original problem into three sub-problems, and then an efficient iterative algorithm is proposed by leveraging the block coordinate descent and Dinkelbach method in combination with successive convex approximation techniques. Simulation results show that the proposed scheme outperforms the other benchmarks significantly in terms of the system SEE.

This paper considers a new wireless information surveillance scenario, where a legitimate monitor tries to eavesdrop on the suspicious messages sent by an unmanned aerial vehicle (UAV)-aided suspicious transmitter. By using the characteristic of the suspicious system, we propose an effective proactive jamming scheme to maximize the achievable eavesdropping rate. A successive convex approximation based iterative algorithm is first developed to solve the original non-convex optimization problem. Based on this result, the eavesdropping rate maximization problem is then solved by applying the one-dimensional search method. Moreover, we also study the special case of two suspicious receivers for gaining more useful insight on designing the optimal jamming transmission strategy. Numerical results show that the proposed proactive jamming approach can significantly enhance the achievable eavesdropping rate compared to the reference scheme.

As more vehicles connect to the internet, they become an important and growing segment of Internet of Things (IoT). To enhance the performance of vehicle-to-network (V2N) communication with green objectives, satellite and unmanned aerial vehicle (UAV) are jointly applied to aid ground communication facility by leveraging the coordinated multi-point transmission technique. This paper investigates a novel architecture of V2N communication in the space-air-ground integrated IoT network consisted of ground base stations (BSs), a UAV as well as one satellite. In the V2N communication model, the UAV, receiving the requested data from the satellite, and BS cooperatively serve the ground vehicle. The goal of this paper is to maximize the achievable rate of the ground vehicle by jointly optimizing the transmit power allocations (i.e., UAV and ground BS) and UAV trajectory, subjecting to the UAV energy constraint, UAV transmission constraint as well as UAV mobility constraint. However, there exists an intractable issue of the formulated problem in a complicated non-convex form. To this end, we decompose it into two sub-problems by fixing UAV trajectory and the power allocations alternately. Specifically, the closed-form expressions are derived to solve the sub-problem with the given UAV trajectory. For the sub-problem with the given power allocations, the UAV trajectory is calculated by using successive convex approximation (SCA) technique. Through the alternation of two methods and iterative operation, the joint optimal solution to the problem is achieved. Finally, numerical results verify that UAV plays a pivotal role in overcoming the tradeoff between higher performance and green communication.

In this paper, we address the downlink resource (subcarriers and power jointly) allocation problem for energy harvesting-based device-to-device (D2D) communication in a railway carriage communication network to improve the energy efficiency (EE) of the system. The considered problem is formulated as maximizing the weighted EE and is solved by leveraging a game-theoretic learning approach. Specifically, we first propose a new performance metric for evaluating the EE and optimize its lower bound. However, there exists an intractable issue of mixing the integer nature into the feasible region. To this end, we decompose the optimization problem into two subproblems by fixing the subcarrier and power allocations alternately. These two subproblems are formulated as two exact potential games, and the optimal properties of their solutions are analyzed. Accordingly, we respectively design a virtual distributed learning algorithm for the power control to find the optimum solution (i.e., Nash equilibrium (NE) point) based on the derived conditions of uniqueness of NE which can effectively accelerate convergence, and a fully distributed Max-logit algorithm for the subcarrier allocations to obtain the best NE with an arbitrarily high probability in which only local information needs to be exchanged. Through the alternation of two algorithms and iterative operation, the optimal solution to the problem is achieved. Finally, numerical results verify the effectiveness of the proposed scheme.

This letter tackles the problem of sum-rate maximization via joint user association and power allocation in heterogeneous cloud radio access networks, while guaranteeing the quality-of-service requirements of all users. A generalized Stackelberg game is applied to this problem with the coupled strategy set of power allocation, and a centralized-distributed method is designed to achieve the optimal solution. Specifically, the user association problem is efficiently solved in a centralized manner and a distributed power allocation algorithm is proposed by using variational inequality theory.

This paper investigates the joint power control and subchannel allocation problem for device-to-device (D2D) and small cell (SC) uplink communications in a cellular network, with the aim of improving the network throughput. For this throughput maximization problem, we leverage a game-theoretic learning approach to solve it. However, there is an intractable issue for the feasible region of the optimization problem, which is intermixed with the integer nature. To tackle this problem, we first present the closed-form expressions of the optimal power under different scenarios by presetting the subchannel allocation profile. Based on the optimal power control, we then formulate the subchannel allocation problem as a game theoretical framework, and define a welfare function which has the same optimum as the optimization objective. To optimize the welfare function, a distributed trial and error learning algorithm is proposed to converge to a stochastically stable state. Since the achieved stable state cannot guarantee to be the optimal solution, we reformulate this problem as an exact potential game model and propose another distributed learning algorithm to find a better Nash equilibrium (NE) which is the global optimal solution (or a superior solution). Moreover, to accelerate the convergence, these two algorithms are modified by getting rid of these unavailable strategic profiles. Finally, numerical results verify the effectiveness of the proposed schemes.