Spectrum sensing is the premise and foundation of cognitive radio technology to solve the scarcity of spectrum resources. In this paper, a spectrum sensing method (WT-ResNet) based on wavelet transform (WT) and residual network (ResNet) is proposed for the problem of spectrum sensing of non-stationary signals with a low signal-to-noise ratio (SNR). The method uses the wavelet transform to process the signal for the characteristics of non-stationary signals, obtains the time-frequency matrix of the signal as the test statistic, and combines the residual network. The method is data-driven and does not require any a priori information about the signal. In addition, we not only test the commonly used wavelet functions but also analyze the SNR robustness and generalization ability of the algorithm. Finally, the simulation results show that the method outperforms other spectrum sensing methods. In particular, the method achieves a detection probability of 85% at SNR=-16dB when the false alarm probability is 10%.

This paper investigates the problem of multidimensional resource management in multi-access mobile edge computing (MEC) networks against external dynamic jamming. The objective is to minimize the long-term computational cost of the MEC network while satisfying the task computation delay requirements of user equipment (UE) by jointly optimizing computing and communication resource allocation. To overcome challenges such as frequency conflict and dynamic jamming attacks, a distributed multi-agent hierarchical deep reinforcement learning (MAHDRL) MEC framework based on hybrid heterogeneous decision-making is proposed. Specifically, a hierarchical MEC anti-jamming data offloading optimization model is constructed, and the MEC resource management problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). Based on this, a distributed MAHDRL algorithm based on the actor-critic (AC) model is designed to solve the multi-agent high-dimensional nonlinear hybrid integer programming NP-hard problem: the high-level network in the base station (BS) optimizes discrete channel access strategies, while the low-level network in UEs learns data offloading strategies. Additionally, the computational complexity is discussed and a theoretical proof of the algorithm convergence is presented. Simulation results demonstrate the superiority of the proposed algorithm, which reduces energy consumption and data processing delay across the network.

This letter investigates the scenario of resisting intelligent reactive jammer in mobile edge computing (MEC) networks. To solve the multi-dimensional offloading allocation problem while avoiding malicious jamming attacks, a hierarchical reinforcement learning (HRL) based hybrid hidden strategy is proposed to allocate multi-dimensional resources and reduce the probability of being perceived in the offloading process. Specifically, the HRL framework is divided into two layers for asynchronous update: the upper layer with the discrete policy network taking charge of outputting the channel access strategies to resist the dynamic jamming attack in the frequency domain, and the lower layer with the continuous policy network network mainly offering hidden offloading strategies to avoid the detection of intelligent reactive jammers. By integrating the double deep Q-Network and twin delayed deep deterministic network, and exploiting their advantages in solving the hybrid strategy problems, the mobile device (MD) is able to execute effective hidden offloading strategies to reduce the computing cost as well as to significantly avoid intelligent reactive jamming in dynamic and unknown environments.

Flying Ad-Hoc Network (FANET) is a hot topic in current research. The design of routing mechanism is challenging because when the scale of Unmanned Aerial Vehicle (UAV) nodes is large, vast amount of routing overhead may lead to network collapse. An elastic routing mechanism is proposed for large-scale small UAVs multitasking scenarios. Firstly, the New-Unifying Connected Dominating Set (N-UCDS) algorithm is proposed to construct a virtual backbone network based on the connected dominating set. The number of neighboring nodes, remaining energy and link duration are considered to influence the UAV network performance when electing backbone nodes. Secondly, by deploying and running the New Better Approach to Mobile Ad-Hoc Network-Advanced (NBATMAN-ADV) routing protocol on the backbone nodes, the link quality can be evaluated by using the received signal strength index and signal-to-noise ratio of the physical layer data. In this way, the change of the link can be quickly sensed while reducing the routing overhead. The simulation results show that the routing protocol proposed in this paper has significantly improved average packet delivery rate, end-to-end delay and received throughput compared with other traditional proactive routing protocols.

Unmanned-aerial-vehicle (UAV)-aided data collection for Internet of Things applications has attracted increasing attention. This paper investigates motion planning for UAV collecting low-power ground sensor node (SN) data in a dynamic jamming environment. We targeted minimizing the flight energy consumption via optimization of the UAV trajectory while considering the indispensable constraints which cover the collection data demodulation threshold, obstacle avoidance, data collection volume, and motion principle. Firstly, we formulate the UAV-aided data collection problem as an energy consumption minimization problem. To solve this nonconvex optimization problem, we rewrite the original problem by introducing relaxation variables and constructing equivalence constraints to obtain a new relaxation convex problem, which can be solved iteratively using the successive convex approximation (SCA) method. However, SCA is susceptible to initial values, especially in dynamic environments where fixed initial values may lead to a wide range of results, making it difficult to obtain a truly optimal solution to the optimization problem. To solve the initial value problem in dynamic environments, we further propose a communication-flight-corridor(CFC)-based initial path generation method to improve the reliability and convergence speed of the SCA method by constructing reliable communication regions and resilient secure paths in real time. Finally, simulation results validate the performance of the proposed algorithm compared to the benchmark algorithms under different parameter configurations.

In this paper, a MVDR-beamformer-based anti-jamming scheme by using covariance matrix reconstruction (CMR) is proposed only with little prior knowledge. First, the lacking prior knowledge is divided into two parts, including steering vector and covariance matrix, where the former is acquired by adopting sliding window in Capon spatial spectrum and the latter is extracted by integrating over angular sectors complementary to sliding window. Then, we develop two efficient approximate methods to reduce the complexity. In proposed-1 methods, the Gauss-Legendre quadrature (GLQ) is used to simply the integral operation. Furthermore, the proposed-2 method employs the iterative conjugate gradient to avoid the explicit inversion of the matrix by updating the beam vectors with iterative techniques. Finally, simulation results show that the proposed beamformer can achieve good performance only with little prior konwledge, which can further attain a flexible tradeoff between complexity and performance by using approximate methods..