With the rapid development of emitter individual identification technology in cognitive radio networks, electromagnetic emitter individual target identification based on deep learning has received much attention. However, the confusion of unintentional features (i.e., individual fingerprint features) and modulation features resulting from the received signal might lead to low identification accuracy. In order to address this narrow, we propose an emitter individual identification network based on the competitive collaboration framework, called Specific Emitter Identification with Adaptive Decomposition and Extraction of individual fingerprint features (SEI-ADE), which can adaptively decompose and extract individual fingerprint features. Firstly, a signal adaptive decomposition network is proposed to distinguish the emitter signal and the interference signal by adopting the gradient inversion layer and the non-sequential characteristics of the signal. Then, in order to distinguish and extract corresponding features, the feature extractor and the training loss constraints are constructed for individual fingerprint feature signals, modulation signals, and external emitter interference signals, respectively. The proposed framework can continuously adjust the gradient loss, classification loss, and timing coding contrast loss, thus minimizing the entire training loss. For the separation of the modulation signal and individual fingerprint feature signal, the signal is transformed into the feature domain, and a mask prediction network is proposed to locate the domain of the individual fingerprint feature. The obtained experimental results show the outstanding performance of our proposal, compared with the current benchmarks. All our models and code are available athttps://github.com/jn-z/SEI-ADE.

With the emerge of the network function virtualization (NFV) and software-defined network (SDN), the SDN/NFV-enabled network has been recognized as one of the most promising technologies to efficiently achieve resource allocation for network service. By introducing the SDN/NFV technology, each service can be represented by a service function chain (SFC), which can deploy the virtualized network functions (VNFs) and chain them with corresponding flows allocation. Considering the dynamic and complex nature of mobile terminals in cloud networks, how to efficiently embedding SFCs remains as a challenging problem. However, the traditional methods (e.g., exact, heuristic, meta-heuristic, and game, etc.) are subjected to the complexity of cloud network scenarios with dynamic network states, high-speed computational requirements, and enormous service requests. Recent studies have shown that deep reinforcement learning (DRL) is a promising way to deal with the limitations of the traditional methods. However, DRL agent training easily suffers from the problem of slow convergence performance. In order to overcome this narrow, in this paper, we design a novel DRL framework based on the enhanced deep deterministic policy gradient (E-DDPG) for the efficient SFC embedding in the dynamic and complex cloud network scenarios. Simulation results validate the high efficiency of the proposed DRL framework as it not only converges faster than currently baseline algorithms, but also reduces the end-to-end delay down to at least 28.3% compared to the benchmarks. All our proposed algorithms and code are available at https://github.com/ jn-z/.

With the outbreak of COVID-19, the government has been forced to collect a large amount of detailed information about patients in order to effectively curb the epidemic of the disease, including private data of patients. Searchable encryption is an essential technology for ciphertext retrieval in cloud computing environments, and many searchable encryption schemes are based on attributes to control user's search permissions to protect their data privacy. The existing attribute-based searchable encryption (ABSE) scheme can only implement the situation where the search permission of one person meets the search policy and does not support users to obtain the search permission through collaboration. In this paper, we proposed a new attribute-based collaborative searchable encryption scheme in multi-user setting (ABCSE-MU), which takes the access tree as the access policy and introduces the translation nodes to implement collaborative search. The cooperation can only be reached on the translation node and the flexibility of search permission is achieved on the premise of data security. ABCSE-MU scheme solves the problem that a single user has insufficient search permissions but still needs to search, making the user's access policy more flexible. We use random blinding to ensure the confidentiality and security of the secret key, further prove that our scheme is secure under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. Security analysis further shows that the scheme can ensure the confidentiality of data under chosen-keyword attacks and resist collusion attacks.

Estimating the Direction of Arrival (DOA) of a single source signal from a single observation of an array data still plays an important part in practical application scenarios. To address the problem, this letter proposes two iterative, fast and accurate approaches namely Q-Shift based DOA Estimation Algorithm (QS-DOAEA) and Tradeoff between A&M and Q-Shift based DOA Estimation Algorithm (TAQ-DOAEA). QS-DOAEA adopts the interpolation of shifted DFT coefficients to iteratively obtain the near-optimal estimation. TAQ-DOAEA employs the error function by simultaneously mixing theq-shifted and half-shifted DFT coefficients, which only requires two iterations. Numerical results reveal that QS-DOAEA achieves significant improvement in terms of estimation accuracy and TAQ-DOAEA expedites the convergence. The proposed estimation algorithms demonstrate that they have an asymptotic variance that is at least 1.0013 times asymptotic Cramér-Rao bound (ACRB), and provide more than 80× reduction in the computational cost, compared to the baseline method. All our proposed algorithms and code are available at https://github.com/jn-z/.

The unmanned aerial vehicles (UAVs) networks are very vulnerable to smart jammers that can choose their jamming strategy based on the ongoing channel state accordingly. Although reinforcement learning (RL) algorithms can give UAV networks the ability to make intelligent decisions, the high-dimensional state space makes it difficult for algorithms to converge quickly. This article proposes a knowledge-based RL method, which uses domain knowledge to compress the state space that the agent needs to explore and then improve the algorithm convergence speed. Specifically, we use the inertial law of the aircraft and the law of signal attenuation in free space to guide the highly efficient exploration of the UAVs in the state space. We incorporate the performance indicators of the receiver and the subjective value of the task into the design of the reward function, and build a virtual environment for pretraining to accelerate the convergence of anti-jamming decisions. In addition, the algorithm proposed is completely based on observable data, which is more realistic than those studies that assume the position or the channel strategy of the jammer. The simulation shows that the proposed algorithm can outperform the benchmarks of model-free RL algorithm in terms of converge speed and averaged reward.

Network function virtualization and multiaccess edge computing have been introduced by Internet service providers to deal with various challenges, which hinder them from satisfying the increasing demand of the low-latency network applications for the Internet of Things (IoT). In edge clouds, any network application for the IoT can be expressed as a service function chain (SFC) consisting of several strictly ordered virtual network functions (VNFs), which can be geographically placed onto edge clouds close to the terminals. However, regarding the large number of terminals and constantly dynamics of edge clouds, determining the placement of VNFs and routing service paths that optimizes the end-to-end delays is a challenging problem. This problem can be also called SFC dynamic orchestration problem. To exactly solve the problem, an integer linear programming (ILP) model is formulated. Then, it is difficult to apply the exact method to deal with the SFC dynamic orchestration problem in the large-scale networks, and this article presents a Lagrangian relaxation heuristic-based algorithm for the optimization, thus reducing the computational complexity. It is demonstrated that the proposed algorithm can efficiently achieve a near-optimal solution with a theoretical analysis. The obtained simulation results show that the proposed algorithm can approximate the performance of ILP’s solution and outperform the current benchmarks in terms of the end-to-end delay and service acceptance ratio.

Network function virtualization (NFV) and mobile-edge computing (MEC) have been introduced by Internet service providers (ISPs) to deal with various challenges, which hinder them from satisfying ambitious quality of experience demands of the Internet-of-Things (IoT) applications. In NFV/MEC-enabled IoT networks, any IoT service can be expressed as a service function chain (SFC) consisting of several strictly ordered virtual network functions (VNFs), which can be geographically placed onto edge clouds close to IoT terminals. However, regarding the large number of IoT terminals and constant dynamics of IoT networks, determining the placement of VNFs and routing service paths that optimize the end-to-end delays in the hybrid edge clouds is a challenging problem. This problem is also called SFC dynamic orchestration problem (SFC-DOP). To address the SFC-DOP, we are motivated to creatively present an SFC dynamic orchestration framework for IoT deep reinforcement learning (DRL). Also, a DRL-based algorithm for SFC-DOP with the actor-critic and the deterministic policy gradient scheme is provided, which can efficiently deal with the SFC-DOP in IoT networks. The obtained experimental results show the outstanding performance of our proposal compared with the current benchmarks.

Network function virtualization (NFV) technology continues to gain more attention as a paradigm shift, and telecommunication services can be flexibly deployed and managed. Any service can be represented by a service function chain (SFC) that is a set of virtual network functions (VNFs) to be executed based on the strict order. The NFV-enabled SFCs applied in the future Internet-of-Things (IoT) networks emerge a challenging problem, particularly more and more IoT devices are trying to access their telecommunication services whenever and wherever, SFCs are needed to be dynamically and adaptively reconfigured, thus adapting to the service requests' dynamics for lower resource consumption and higher revenue for Internet service providers (ISPs). In this article, we study the SFC dynamic reconfiguration problem (SFC-DRP) in the IoT networks, a discrete-time Markov decision process (DTMDP)-based IoT SFC-DRP is formulated by guaranteeing the QoS and resource constraints. We subsequently propose a novel deep Dyna-Q (DDQ) approach to solve this model. Our proposal has been evaluated with the obtained results demonstrating an average CPU root-mean-square error (RMSE) of 0.17, compared to 0.75 obtained while using the original approach. Moreover, our proposed SFC reconfiguration technique can approximate the performance of the integer linear programming (ILP) model within a polynomial time, and outperform the existing benchmarks in terms of the reconfiguration overhead and the resource utilization ratio from service provisioning, respectively.