In this paper, a compressive feedback scheme based on compressed sensing is investigated to reduce feedback load and enhance the sum throughput at low feedback rates for underwater acoustic MIMO systems. Meanwhile, to avoid the out-of-date channel state information at the transmitter under the high dynamic underwater channel, we present a modified SL0 (M-SL0) algorithm to recover the CSI at the transmitter, which adopts more steep approximate hyperbolic tangent function to approximate the l 0 norm in the recovery. The minimization problem of the l 0 norm can be transformed into a convex optimization problem for the smoothed function. Then, in order to solve the problem of slow convergence and inaccuracy estimation caused by notched effect of the gradient method, hybrid optimization algorithm that combines the advantages of the gradient method and the revised Newton method is introduced to improve the accuracy of sparse recovery. Numerical simulations show that the recovery accuracy and speed are improved by the proposed M-SL0 algorithm comparing with SL0, OMP algorithm. Meanwhile, CSI feedback based compressive sensing performs better than the feedback based on a vector quantitation codebook.
A secrecy transmission method with robust power control is investigated in this paper for a downlink two-tier femtocell network, where an eavesdropper attempts to wiretap the legitimate macrocell users. Considering the imperfect channel gains, a probability constraint robust optimization problem is formulated to satisfy the quality-of-service (QoS) of users. We aim to maximize the secrecy rate with the constrains of power outage probability and limited power at base stations (BSs). Firstly, the problem of power outage probability is solved by a novel conversion method. Unfortunately, the object function is a non-convex. A simple method based on variable substitution and Taylor expansion is presented to solve this puzzle in this paper. Then, the iterative algorithm is provided to get the practical operation for users. Finally, numerical simulation results are given to illustrate the effectiveness of the proposed algorithm.
In this article, the joint relay selection and power allocation problem is studied to maximize the uplink cumulative performance for the time-varying energy harvesting-driven underwater acoustic sensor networks (EH-UASNs). We propose a stratification-based model-free deep reinforcement learning framework, which consists of deep deterministic policy gradient (DDPG) and deep Q network (DQN) algorithms, to solve the complex joint optimization problem. More specifically, the DQN is employed to optimize the discrete relay selection strategies; the DDPG is employed to optimize the continuous power allocation strategies. The stratification-based framework can intelligently track the complex state in a divide-and-conquer perspective; as a result, the proposed algorithm can explore larger solution space with high learning efficiency. Thereinto, we reconstruct the state by introducing available outdated channel information and the capacity of the battery for enriching effective learning information. Furthermore, to equilibrate the instantaneous demand and long-term quality of service (QoS), we propose a reward mechanism that can induce the agent to adaptively adjust the power allocation strategies to match the dynamic environment. Simulation results validate the high effectiveness of our algorithm.
Multiple autonomous underwater vehicles (AUVs) have been widely used in various of missions in underwater environment, such as tracking and searching. However, multi-AUV search targets in local area face to security challenges still. To solve this issue, in this paper, we propose a secure, energy-saving multi-AUV cooperative operation scheme blockchain-based. In order to enhance the security, control commands and state information are packaged into blocks by encryption algorithm of blockchain. Considering energy-efficient of the entire network, we present an improved Raft consensus algorithm based on energy model, named E-Raft. Balancing the excess of leader's energy consumption by setting thresholds. Our proposed approach not only guarantees the security of data, but extends working time of the network and the energy utilization. Simulation results justify the effectiveness of the scheme.
Global phase synchronization for a class of dynamical complex networks composed of multiinput multioutput pendulum-like systems with time-varying coupling delays is investigated. The problem of the global phase synchronization for the complex networks is equivalent to the problem of the asymptotical stability for the corresponding error dynamical networks. For reducing the conservation, no linearization technique is involved, but by Kronecker product, the problem of the asymptotical stability of the high dimensional error dynamical networks is reduced to the same problem of a class of low dimensional error systems. The delay-dependent criteria guaranteeing global asymptotical stability for the error dynamical complex networks in terms of Liner Matrix Inequalities (LMIs) are derived based on free-weighting matrices technique and Lyapunov function. According to the convex characterization, a simple criterion is proposed. A numerical example is provided to demonstrate the effectiveness of the proposed results.
In this study, adversarial graph bandit theory is used to rapidly select the optimal attack node in underwater acoustic sensor networks (UASNs) with unknown topology. To ensure the flexibility and elusiveness of underwater attack, we propose a bandit-based hybrid attack mode that combines active jamming and passive eavesdropping. We also present a virtual expert-guided online learning algorithm to select the optimal node without priori topology information and complex calculation. The virtual expert mechanism is proposed to guide the algorithm learning. The expert establishes a virtual topology configuration, which addresses the blind exploration and energy consumption of attackers to a large extent. With the acoustic broadcast characteristic, we also put forward an expert self-updating method to follow the changes of real networks. This method enables the algorithm to commendably adapt to the dynamic environments. Simulation results verify the strong adaptability and robustness of the proposed algorithm.
An advanced control scheme for managing a hybrid energy generation system (HEGS) is presented in this paper. A hierarchical management and control architecture based on multi-agent systems (MAS) is discussed. MAS will account for the complex behavior of a hybrid energy supply system. The management and control strategies are implemented through a system of agents based on three tiers. The upper level agents develop overall energy management strategies for a hybrid energy supply system. The middle-level agents integrate coordinated switching controllers. The lower level agents are responsible for dealing with local control strategies. Coordinated switching controllers within the middle-level agents are designed as event-triggered hybrid controllers based on differential hybrid Petri-net (DHPN) models. The operation modes of distributed energy resources (DERs) can smoothly transfer in a coordinated manner due to the coordinated action of the switching controllers according to variation in operating conditions. Finally, simulation results from different scenarios verifying the feasibility of the proposed scheme are offered.
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