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In this letter, the problem of phase synchronization for a group of drones acting as relays to build a connection between a base station (BS) and a ground receiver (GR) is investigated. The received signal strength (RSS) of the GR significantly degrades due to phase-mismatch caused by mobility of the drones. To tackle this challenge, a distributed synchronization algorithm that utilizes a few feedbacks is proposed and its convergence is proved for a specified bound on the frequency deviation. To evaluate the performance of the proposed method, the received signal strength (RSS) of the GR is presented assuming different deviation frequencies and number of drones. Provided simulation results demonstrate that our proposed method is capable of synchronizing the drones’ phases for all the considered deviation frequencies and number of drones. Moreover, the proposed method outperforms the existing cumulative positive one-bit feedback based approaches in terms of convergence speed and RSS, which renders the proposed algorithm viable for practical deployment.

Passive methods in compressed images forensics are more preferable than active ones like watermarking. In passive methods, the deviation of natural characteristics of the compressed image plays a key role in performance. In this paper, based on the blockiness effect and its corresponding discontinuity imposed on block edges, we propose a universal normalized feature for passive analyses. Using this feature, we devise a high-precision straightforward procedure for JPEG block size extraction, and a machine learning approach for JPEG quality factor estimation. We also show that by extracting the messiness parameter of the image and designing a two-stage classifier, it is possible to boost the classification performance. Our simulation results validate the accuracy and efficiency of the proposed scheme.

Device-to-device (D2D) communication is considered as a promising resource reuse in cellular networks. However, the allocation of channel resources and power to D2D communication require precise coordination since D2D user equipments (UEs) can cause interference to cellular UEs. In this paper, we develop Nash Bargaining Solution (NBS) and Nash Competitive games frameworks to model the power allocation problem. NBS is a type of cooperative games, which has been applied for solving resource allocation problem among competing players. In the NBS game, the optimization problem is not convex. We propose a method to make the problem convex. In the Nash Competitive game, the problem is convex so that we apply KKT conditions and find a closed form solution for Nash Equilibrium (NE). Finally, we perform computer simulations to study the performance of the games. Simulation results show that our method for finding NBS has a significant accuracy. Results also indicate that D2D users have higher rates in NBS game compared to Nash Competitive game which states that the cooperation helps D2D users to gain more efficiency.

In order to compensate inefficiency of traditional macrocell base stations (MBS), femtocell base stations (FBS) are deployed in cell areas. This deployment can enhance the Quality of Service (QoS) for the users which have difficulty communicating with the MBS. However, the presence of FBSs causes interference for MBS that should be controlled. Guaranteed QoS such as delay-bounds is required in real-time video applications. Moreover, the rapid growth of mobile-video traffic and time varying nature of wireless channels make it difficult to guarantee stringent delay constraints. However, providing delay-bounds based on effective capacity looks more appropriate for unreliable channels. This paper proposes a power allocation scheme based on Nash Bargaining Solution(NBS). NBS is a cooperative solution for Nash bargaining competitive game, which leads to a fair resource allocation and also satisfies the Pareto efficiency. We derive NBS in a tractable closed form formula by taking into account the delay-bounds and interference constraints. Analyzing the simulation results demonstrates that our proposed solution reaches a high level of fairness among users.