Cognitive radar is a concept proposed by Simon Haykin in 2006 as a new generation of radar system that imitates human cognitive features. Different from the adaptive signal processing at the receiver in adaptive radar, the cognitive radar realizes closed-loop adaptive policy adjustment of both transmitter and receiver in the continuous interaction with the environment. As a networked radar may significantly enhance the flexibility and robustness than its monostatic counterpart, the wireless networked cognitive radar (WNCR) attracts increasing research. This article firstly reviews the concept and development of cognitive radar, especially the related researches of networked cognitive radar. Then, the co-design of cognitive radar and communication is investigated. Although the communication quality between radar sensing nodes is the premise of detection, tracking, imaging and anti-jamming performance of the WNCR, the latest researches seldom consider the communication architecture design for WNCR. Therefore, this article mainly focuses on the proposal of WNCR concept based on the researches of cognitive radar and analyzes research challenges of WNCR system in practical application, and the corresponding guidelines are proposed to inspire future research.

The global Internet is composed of more than 70,000 autonomous domain networks interconnected through the Border Gateway Protocol (BGP). Studying the ecological evolution of BGP network is of great significance for analyzing the evolution trend of the global Internet. This paper focuses on the evolution of Country-Level BGP network ecosystems in 24 years, and innovatively studies the relationship between Country-Level BGP network and economy, breaking through the limitations of traditional research that only focuses on BGP network. The results revealed that the number of global BGP networks has increased by nearly 23 times and that network interconnection has increased nearly 80 times over in 24 years. It was found that the growth of the global BGP network ecosystem has slowed overall due to major global security events, although the BGP network ecosystem in some Southeast Asian countries is developing against the trend. At the same time, there is a significant positive correlation between the BGP network ecology and the national economy in the time dimension; there is a strong positive correlation in the spatial dimension, but the trend is weakening year by year.

In mmWave massive multiple-input multiple-output (MIMO) communication systems, the extension of low-complexity narrowband precoding schemes to be operated on wideband systems under frequency-selective channels remains an important challenge at the current time. This paper investigates a low complexity wideband hybrid precoding scheme for mmWave massive MIMO multicarrier systems under a single-user, fully-connected hybrid architecture. We show that the radio frequency (RF) precoding/combining vectors can be directly derived from the eigenvectors of the optimal fully-digital covariance matrix over all subcarriers in order to maximize the sum rate of spectral efficiency. We also suggest a new method that iteratively reduces the residual error between the covariance matrix and the sum of products of precoding matrices over all the subcarriers to improve the performance in the case where the number of RF chains is higher than the number of streams. The results of the simulation show that the proposed schemes' complexity is low compared to the present methods, and their performance can almost reach the upper bound achieved by the optimal full-baseband design.

Recently, cell-free (CF) massive multiple-input multiple-output (MIMO) becomes a promising architecture for the next generation wireless communication system, where a large number of distributed access points (APs) are deployed to simultaneously serve multiple user equipments (UEs) for improved performance. Meanwhile, a clustered CF system is considered to tackle the backhaul overhead issue in the huge connection network. In this paper, taking into account the more realistic mobility scenarios, we propose a hybrid small-cell (SC) and clustered CF massive MIMO system through classifications of the UEs and APs, and constructing the corresponding pairs to run in SC or CF mode. A joint initial AP selection of this paradigm for all the UEs is firstly proposed, which is based on the statistics of estimated channel. Then, closed-form expressions of the downlink achievable rates for both the static and moving UEs are provided under Ricean fading channel and Doppler shift effect. We also develop a semi-heuristic search algorithm to deal with the AP selection for the moving UEs by maximizing the weight average achievable rate. Numerical results demonstrate the performance gains and effective rates balancing of the proposed system.

Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In this paper, Gaussian Mixture learned approximate message passing (GM-LAMP) network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems. Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit (OMP) and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high. This drawback can be addressed using classical iterative algorithms such as approximate message passing (AMP), which has comparatively low computational complexity. The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders. In this paper, the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP (LAMP) network, and is further enhanced as GM-LAMP network using the concept of Gaussian Mixture distribution of the hybrid precoders. The simulation results show that the proposed GM-LAMP network achieves optimal hybrid precoder design with enhanced achievable rates, better accuracy and low computational complexity compared to the existing algorithms.

The massive connectivity and limited energy pose significant challenges to deploy the enormous devices in energy-efficient and environmentally friendly in the Internet of Things (IoT). Motivated by these challenges, this paper investigates the energy efficiency (EE) maximization problem for downlink cooperative non-orthogonal multiple access (C-NOMA) systems with hardware impairments (HIs). The base station (BS) communicates with several users via a half-duplex (HD) amplified-and-forward (AF) relay. First, we formulate the EE maximization problem of the system under HIs by jointly optimizing transmit power and power allocated coefficient (PAC) at BS, and transmit power at the relay. The original EE maximization problem is a non-convex problem, which is challenging to give the optimal solution directly. First, we use fractional programming to convert the EE maximization problem as a series of subtraction form sub-problems. Then, variable substitution and block coordinate descent (BCD) method are used to handle the sub-problems. Next, a resource allocation algorithm is proposed to maximize the EE of the systems. Finally, simulation results show that the proposed algorithm outperforms the downlink cooperative orthogonal multiple access (C-OMA) scheme.

Activity and motion recognition using Wi-Fi signals, mainly channel state information (CSI), has captured the interest of many researchers in recent years. Many research studies have achieved splendid results with the help of machine learning models from different applications such as healthcare services, sign language translation, security, context awareness, and the internet of things. Nevertheless, most of these adopted studies have some shortcomings in the machine learning algorithms as they rely on recurrence and convolutions and, thus, precluding smooth sequential computation. Therefore, in this paper, we propose a deep-learning approach based solely on attention, i.e., the sole Self-Attention Mechanism model (Sole-SAM), for activity and motion recognition using Wi-Fi signals. The Sole-SAM was deployed to learn the features representing different activities and motions from the raw CSI data. Experiments were carried out to evaluate the performance of the proposed Sole-SAM architecture. The experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolutions like Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN). Sole-SAM archived a 0.94% accuracy level, which is 0.04% better than RNN and 0.02% better than LSTM.

This paper considers link scheduling in a wireless network comprising of two types of nodes: (i) hybrid access points (HAPs) that harvest solar energy, and (ii) devices that harvest radio frequency (RF) energy whenever HAPs transmit. Our aim is to derive the shortest possible link schedule that determines the transmission time of inter-HAPs links, and uplinks from devices to HAPs. We first outline a mixed integer linear program (MILP), which can be run by a central node to determine the optimal schedule and transmit power of HAPs and devices. We then outline a game theory based protocol called Distributed Schedule Minimization Protocol (DSMP) that is run by HAPs and devices. Advantageously, it does not require causal energy arrivals and channel gains information. Our results show that DSMP produces schedule lengths that are at most 1.99x longer than the schedule computed by MILP.

Satellite-to-ground terahertz communication is limited by the power of signal source and antenna gain level, and has large path loss, which is difficult to implement. In this paper, a feasible scheme of satellite-to-ground terahertz communication using High Altitude Platforms (HAPs) as relay is presented, and the path loss on terahertz communication links is modeled and analyzed. Combined with the path loss model, the transmission loss along HAP-to-ground paths under different seasons and complex weather environment in Ali, Xizang, China is calculated. The results show that the transmission characteristics of terahertz waves in winter and summer are significantly different, mainly reflected in the number and bandwidth of usable atmospheric windows. Furthermore, the additional attenuation caused by the typical sand dust and ice cloud environment on terahertz band can reach 6.1 dB and 1.9 dB at the maximum respectively. With the aid of high gain antenna, the usable communication frequencies of the HAP-to-ground links in winter are significantly more than those in summer. When the transmitting and receiving antenna gain is 40 dBi respectively, the usable communication frequency can reach 1.35 THz in winter, while it is limited to less than 1 THz in summer, up to 0.493 THz.

Deep neural networks (DNNs) are potentially susceptible to adversarial examples that are maliciously manipulated by adding imperceptible perturbations to legitimate inputs, leading to abnormal behavior of models. Plenty of methods have been proposed to defend against adversarial examples. However, the majority of them are suffering the following weaknesses: 1) lack of generalization and practicality. 2) fail to deal with unknown attacks. To address the above issues, we design the adversarial nature eraser (ANE) and feature map detector (FMD) to detect fragile and high-intensity adversarial examples, respectively. Then, we apply the ensemble learning method to compose our detector, dealing with adversarial examples with diverse magnitudes in a divide-and-conquer manner. Experimental results show that our approach achieves 99.30% and 99.62% Area under Curve (AUC) scores on average when tested with various $L_p$ norm-based attacks on CIFAR-10 and ImageNet, respectively. Furthermore, our approach also shows its potential in detecting unknown attacks.

The received signals used for sparse code multiple access (SCMA) detection are usually contaminated with noise during transmission, which exposes an issue of low decoding efficiency. To address this issue, a novel detector based on a residual network (ResNet) perception fusion framework (RSMPA) is proposed for uplink SCMA system in this paper. Specifically, we first formulate a joint design of perception system and traditional communication module. A perception framework based on ResNet is applied to cancel the noise component and enhance the communication system performance. The ResNet model is designed and trained using the clean and noisy SCMA signal, respectively. Based on the denoised output, information iteration process is executed for multi-user detection. Simulation results indicate that the perception model achieves an excellent denoising performance for SCMA system and the proposed scheme outperforms the conventional detection algorithms in terms of SER performance.

The cache-based covert channel is one of the common vulnerabilities exploited in the Spectre attacks. Current mitigation strategies focus on blocking the eviction-based channel by using a random/encrypted mapping function to translate memory address to the cache address, while the updated-based channel is still vulnerable. In addition, some mitigation strategies are also costly as it needs software and hardware modifications. In this paper, our objective is to devise low-cost, comprehensive-protection techniques for mitigating the Spectre attacks. We proposed a novel cache structure, named EBCache, which focuses on the RISC-V processor and applies the address encryption and blacklist to resist the Spectre attacks. The addresses encryption mechanism increases the difficulty of pruning a minimal eviction set. The blacklist mechanism makes the updated cache lines loaded by the malicious updates invisible. Our experiments demonstrated that the EBCache can prevent malicious modifications. The EBCache, however, reduces the processor's performance by about 23% but involves only a low-cost modification in the hardware.

The air traffic management (ATM) system is an intelligent system, which integrates the ground computer network, airborne network and space satellite (communication and navigation) network by the ground-air data link system. Due to the openness and widely distribution of ATM system, the trust relationship of all parties in the system is pretty complex. At present, public key infrastructure (PKI) based identity authentication method is more and more difficult to meet the growing demand of ATM service. First, through the analysis of the organizational structure and operation mode of ATM system, this paper points out the existing identity authentication security threats in ATM system, and discusses the advantages of adopting blockchain technology in ATM system. Further, we briefly analyze some shortcomings of the current PKI-based authentication system in ATM. Particularly, to address the authentication problem, this paper proposes and presents a trusted ATM Security Authentication Model and authentication protocol based on blockchain. Finally, this paper makes a comprehensive analysis and simulation of the proposed security authentication scheme, and gets the expected effect.

Emerging long-range industrial IoT applications (e.g., remote patient monitoring) have increasingly higher requirements for global deterministic delay. Although many existing methods have built deterministic networks in small-scale networks through centralized computing and resource reservation, they cannot be applied on a global scale.
The emerging mega-constellations enable new opportunities for realizing deterministic delay globally. As one constellation (e.g., Starlink) might be managed by a single operator (e.g., SpaceX), packets can be routed within deterministic number of hops. Moreover, the path diversity brought by the highly symmetrical network structure in mega-constellations can help to construct a congestion free network by routing. This paper leverages these unique characteristics of mega-constellations to avoid the traditional network congestion caused by multiple inputs and single output, and to determine the routing hops, and thus realizing a global deterministic network (DETSPACE). The model based on the 2D Markov chain theoretically verifies the correctness of DETSPACE. The effectiveness of DETSPACE in different traffic load conditions is also verified by extensive simulations.

A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production. Today, plenty of work is being focussed on Doubly Fed Induction Generators (DFIG) utilized in wind energy systems. DFIG is found to be the best option in the Wind Energy Conversion Systems (WECS) to mitigate the issues caused by power converters. In this work, a new Artificial Neural Network (ANN) is proposed with the Diffusion and Dispersal strategy that works on Maximum Power Point Tracking (MPPT) along with Wind Energy Conversion System (WECS)to minimize electrical faults. The controller focus was not just to increase performance but also to reduce damage owing to any phase to phase fault or Phase to phase to ground fault. To ensure optimal MPPT for the proposed WECS, ANN achieves the optimal PI controller parameters for the indirect control of active and reactive power of DFIG. The optimal allocation and size of the DGs within the distributed system and for MPPT control are obtained using a population of agents. The generated solutions are evaluated and on being successful, the agents test their hypothesis again to create a positive feedback mechanism. Simulations are carried out, and the proposed IoT framework efficiency indicates performance improvement and faster recovery against faults by 9 percent for phase to ground fault and by 7.35 percent for phase to phase fault.

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks. However, edge computing servers (ECSs) from different operators may not trust each other, and thus the incentives for collaboration cannot be guaranteed.
In this paper, we propose a consortium blockchain enabled collaborative edge computing framework, where users can offload computing tasks to ECSs from different operators.
To minimize the total delay of users, we formulate a joint task offloading and resource optimization problem, under the constraint of the computing capability of each ECS. We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution. Finally, we propose a reputation based node selection approach to facilitate the consensus process, and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain. Simulation results validate the effectiveness of the proposed algorithm, and the total delay can be reduced by up to 40% compared with the non-cooperative case.

This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group, where the framework of the eigenvector-based channel state information (CSI) feedback problem is firstly provided. Then a basic Transformer backbone for CSI feedback referred to EVCsiNet-T is proposed. Moreover, a series of potential enhancements for deep learning based (DL-based) CSI feedback including i) data augmentation, ii) loss function design, iii) training strategy, and iv) model ensemble are introduced. The experimental results involving the comparison between EVCsiNet-T and traditional codebook methods over different channels are further provided, which show the advanced performance and a promising prospect of Transformer on DL-based CSI feedback problem.

Satellite communication systems are facing serious electromagnetic interference, and interference signal recognition is a crucial foundation for targeted anti-interference. In this paper, we propose a novel interference recognition algorithm called HDCGD-CBAM, which adopts the time-frequency images (TFIs) of signals to effectively extract the temporal and spectral characteristics. In the proposed method, we improve the Convolutional Long Short-Term Memory Deep Neural Network (CLDNN) in two ways. First, the simpler Gate Recurrent Unit (GRU) is used instead of the Long Short-Term Memory (LSTM), reducing model parameters while maintaining the recognition accuracy. Second, we replace convolutional layers with hybrid dilated convolution (HDC) to expand the receptive field of feature maps, which captures the correlation of time-frequency data on a larger spatial scale. Additionally, Convolutional Block Attention Module (CBAM) is introduced before and after the HDC layers to strengthen the extraction of critical features and improve the recognition performance. The experiment results show that the HDCGD-CBAM model significantly outperforms existing methods in terms of recognition accuracy and complexity. When Jamming-to-Signal Ratio (JSR) varies from -30dB to 10dB, it achieves an average accuracy of 78.7% and outperforms the CLDNN by 7.29% while reducing the Floating Point Operations(FLOPs) by 79.8% to 114.75M. Moreover, the proposed model has fewer parameters with 301k compared to several state-of-the-art methods.

In recent years, many unknown protocols are constantly emerging, and they bring severe challenges to network security and network management. Existing unknown protocol recognition methods suffer from weak feature extraction ability, and they cannot mine the discriminating features of the protocol data thoroughly. To address the issue, we propose an unknown application layer protocol recognition method based on deep clustering. Deep clustering which consists of the deep neural network and the clustering algorithm can automatically extract the features of the input and cluster the data based on the extracted features. Compared with the traditional clustering methods, deep clustering boasts of higher clustering accuracy. The proposed method utilizes network-in-network (NIN), channel attention, spatial attention and Bidirectional Long Short-term memory (BLSTM) to construct an autoencoder to extract the spatial-temporal features of the protocol data, and utilizes the unsupervised clustering algorithm to recognize the unknown protocols based on the features. The method firstly extracts the application layer protocol data from the network traffic and transforms the data into one-dimensional matrix. Secondly, the autoencoder is pretrained, and the protocol data is compressed into low dimensional latent space by the autoencoder and the initial clustering is performed with K-Means. Finally, the clustering loss is calculated and the classification model is optimized according to the clustering loss. The classification results can be obtained when the classification model is optimal. Compared with the existing unknown protocol recognition methods, the proposed method utilizes deep clustering to cluster the unknown protocols, and it can mine the key features of the protocol data and recognize the unknown protocols accurately. Experimental results show that the proposed method can effectively recognize the unknown protocols, and its performance is better than other methods.

To mitigate the impact of noise and interference on multi-level-cell (MLC) flash memory with the use of low-density parity-check (LDPC) codes, we propose a dynamic write-voltage design scheme considering the asymmetric property of raw bit error rate (RBER), which can obtain the optimal write voltage by minimizing a cost function. In order to further improve the decoding performance of flash memory, we put forward a low-complexity entropy-based read-voltage optimization scheme, which derives the read voltages by searching for the optimal entropy value via a log-likelihood ratio (LLR)-aware cost function. Simulation results demonstrate the superiority of our proposed dynamic write-voltage design scheme and read-voltage optimization scheme with respect to the existing counterparts.

The selection and coordinated application of government innovation policies are crucial for guiding the direction of enterprise innovation and unleashing their innovation potential. However, due to the lengthy, voluminous, complex, and unstructured nature of regional innovation policy texts, traditional policy classification methods often overlook the reality that these texts cover multiple policy topics, leading to lack of objectivity. In contrast, topic mining technology can handle large-scale textual data, overcoming challenges such as the abundance of policy content and difficulty in classification. Although topic models can partition numerous policy texts into topics, they cannot analyze the interplay among policy topics and the impact of policy topic coordination on enterprise innovation in detail. Therefore, we propose a big data analysis scheme for policy coordination paths based on the latent Dirichlet allocation (LDA) model and the fuzzy-set qualitative comparative analysis (fsQCA) method by combining topic models with qualitative comparative analysis. The LDA model was employed to derive the topic distribution of each document and the word distribution of each topic and enable automatic classification through algorithms, providing reliable and objective textual classification results. Subsequently, the fsQCA method was used to analyze the coordination paths and dynamic characteristics. Finally, experimental analysis was conducted using innovation policy text data from 31 provincial-level administrative regions in China from 2012 to 2021 as research samples. The results suggest that the proposed method effectively partitions innovation policy topics and analyzes the policy configuration, driving enterprise innovation in different regions.