Existing domain adaptation (DA) methods, which focus on realizing the class-level alignment for cross-domain features to solve the problem of fuzzy classification boundaries in feature learning process, however, it is difficult to deal with samples located at the classification boundary in this way, resulting in diagnosis performance being limited. To solve the problem, this article proposes a semi-supervised contrastive DA network (SSCDAN) for realizing the cross-working condition rotating machinery fault diagnosis method. Specifically, an in-domain semi-supervised contrastive learning (SSCL) strategy is designed, with the supervision of class information, which guides the discriminative learning of different classes in each domain to eliminate the fuzzy classification boundaries, and facilitates the DA of cross-domain features which utilizes the local maximum mean discrepancy (LMMD). Meanwhile, the negative impact from poor-quality target domain pseudo-labels on SSCL and DA is mitigated by dynamically limiting the associated contrastive learning loss and DA loss gain, and introducing domain adversarial. Finally, the effectiveness of SSCDAN is validated by the ablation and comparison experiments on the Paderborn University (PU) and wind turbine gearbox (WTG) datasets. Compared with deep subdomain adaptation (DSAN), SSCDAN improves the overall average accuracy by 22.43% and 7.36% on the cross-working condition diagnosis tasks of PU and WTG datasets, respectively, and outperforms other popular DA methods.

The operational state of harmonic drives demonstrates nonlinear and nonstationary characteristics, which pose challenges for traditional methods to extract features. Graph neural networks (GNNs) have shown significant potential in harmonic drive fault diagnosis owing to their ability to capture high-order correlations among nodes and adapt to dynamic changes. However, the industrial application of GNNs is limited by two major constraints: the scarcity of fault samples and the high computational cost. To mitigate these constraints, this paper proposes a diagnostic framework based on dynamic graph data augmentation and adaptive knowledge distillation for graphs (DGDA-AKDG). Specifically, DGDA employs a multi-view feature extraction module and a weighted feature fusion module for graph data augmentation to mitigate the data imbalance problem. AKDG utilizes a policy network and a routing feature fusion mechanism to determine the distillation path, thereby optimizing the distillation location in the GNN model. Ablation and comparative experiments conducted on bearing datasets and industrial robot harmonic drive datasets indicate that DGDA improves dataset accuracy by 2.91% to 12.60% after balancing, while AKDG reduces distillation accuracy loss by a factor of 3.54 to 14.51. These results demonstrate the superiority of DGDA-AKDG in handling heterogeneous distillation under data imbalance conditions, thereby further expanding the application of GNNs in intelligent manufacturing.

The channel hardening effect is less pronounced in the cell-free massive multiple-input multiple-output (mMIMO) system compared to its cellular counterpart, making it necessary to estimate the downlink effective channel gains to ensure decent performance. However, the downlink training inadvertently creates an opportunity for adversarial nodes to launch pilot spoofing attacks (PSAs). First, we demonstrate that adversarial distributed access points (APs) can severely degrade the achievable downlink rate. They achieve this by estimating their channels to users in the uplink training phase and then precoding and sending the same pilot sequences as those used by legitimate APs during the downlink training phase. Then, the impact of the downlink PSA is investigated by rigorously deriving a closed-form expression of the per-user achievable downlink rate. By employing the min-max criterion to optimize the power allocation coefficients, the maximum per-user achievable rate of downlink transmission is minimized from the perspective of adversarial APs. As an alternative to the downlink PSA, adversarial APs may opt to precode random interference during the downlink data transmission phase in order to disrupt legitimate communications. In this scenario, the achievable downlink rate is derived, and then power optimization algorithms are also developed. We present numerical results to showcase the detrimental impact of the downlink PSA and compare the effects of these two types of attacks.

With the aim of reducing the transmission latency, this article proposes a scheme of user-assisted base station (BS) caching and cooperative prefetching for high-speed railway (HSR) communications. In the model under consideration, neighboring BSs periodically exchange information, including the coverage area and communication rate, to facilitate content caching and prefetching. As an additional means, users can cache contents that are different from BSs. Specifically, we construct an optimization problem for content caching and prefetching that minimizes the overall transmission latency. Moreover, this problem is decomposed into two subproblems, namely, the one aiming to maximize the available throughput of BSs and the other attempting to maximize the user cache utilization subject to the constraint of the BS cache. We demonstrate that the objective function of the first subproblem is equivalent to a monotone submodular function subject to matroid constraints. Therefore, it can be efficiently solved with a greedy algorithm. Meanwhile, the user cache issue can be viewed as a weighted sum maximization problem. Simulation results are presented to verify that the proposed scheme can not only reduce the average transmission latency but also improve the hit probability and system throughput.

Massive MIMO systems are vulnerable to pilot spoofing attacks (PSAs) since the estimated channel state information can be contaminated by the eavesdropping link, thus incurring severe information leakage in downlink transmission. To safeguard legitimate communications, this paper proposes a PSA detection method which relies on pilot manipulation. Specifically, users randomly partition pilot sequences into two parts, where the first part remains unchanged and the second one is multiplied with a diagonal matrix. Although a malicious node may follow the same way to send pilots, this makes it more likely to be detected. According to the principle of the likelihood-ratio test, the proposed detector is designed based on a decision metric that does not include the legitimate channel. This feature differentiates our scheme from existing ones and remarkably improves the detection accuracy. Besides, the possibility of performance enhancement by joint detection is discussed. Furthermore, based on pilot manipulation, a jamming-resistant receiver is designed. The key of this receiver is a new channel estimator that is robust to the PSA. Finally, extensive simulations are carried out to validate our proposed algorithms.

In this paper, a joint multi-user constellation is proposed for energy detection-based non-coherent massive multiple-input multiple-output system. This is motivated by the simple design and high energy efficiency it entails for both the transmitter and receiver. First, the orthogonal codes is employed to suppress the multi-user interference. However, this comes at the price of consuming more communications resources. In this study, the key to reduce code redundancy is the design of a joint constellation since it makes energy detection applicable when multiple users employ the same orthogonal codes. Although it is unsolvable initially, our analysis indicates that through minimizing the symbol-error rate (SER), the joint constellation design becomes feasible. Concretely, two analytical expressions of SER based on Gamma and Gaussian distributions are derived. Via minimizing the error probability, an important result that the joint constellation should satisfy is obtained. Accordingly, an isometric constellation design is proposed to find constellations that enable non-coherent reception with multiple users, and reduce SER simultaneously. In addition, decoding regions of symbol decision are optimized to further improve the error performance. In the end, numerical simulations are carried out to highlight the effectiveness of our proposed scheme.

To secure the legitimate communication, this paper proposes a pilot spoofing attack detection algorithm in massive MIMO systems, in which the information of channel statistics is unknown. First, users send pilots to the base station (BS), then the BS transmits the conjugate of its received signal (which may contain spoofing signal) back to users, where the final decision on detection is made. Analytical analysis with respect to probabilities of detection (PD) and false alarm is conducted. It is revealed that PDconverges in regimes of high signal-to-noise ratio (SNR) and large number of BS antennas, while the channel statistics from the active attacker to the BS influences the performance greatly.

In this paper, a joint multi-user constellation is proposed for energy detection-based non-coherent massive multiple-input multiple-output system. This is motivated by the simple design and high energy efficiency it entails for both the transmitter and receiver. Although it is unsolvable initially, our analysis indicates through minimizing the symbol-error rate (SER), the joint constellation design becomes feasible. Concretely, two analytical expressions of SER based on Gamma and Gaussian distributions are derived. Via minimizing the error probability, an important result that the joint constellation must satisfy is obtained. Accordingly, an isometric constellation design is proposed to find constellations that enable non-coherent reception with multiple users, and achieve the minimum SER simultaneously. Finally, numerical simulations are carried out to highlight the effectiveness of our proposed scheme.

In recent studies, a simple non-coherent communication scheme based on energy detection is proposed in massive single-input multiple-output (SIMO) systems. Before data transmission, the transmitter sends pilots to the receiver for the purpose of estimating the channel statistics. However, this training phase unintentionally provides opportunity for a malicious jammer to attack legitimate communication. In order to secure the legitimate communication, this paper proposes a jamming detection method in non-coherent SIMO systems, in which the information of channel statistics is not required. First, the transmitter sends pilots to the receiver, then the receiver sends the conjugate of its received signal (which may contain jammer signal) back to the transmitter, where the final decision on jamming detection is made. According to the likelihood ratio test principle, two detectors based on variance and standard variance normalization are proposed. The performance analysis indicates that these two detectors are of similar detection performance but of different complexity. Furthermore, it is revealed that the probability of detection initially grows with the number of receive antennas but converges quickly then, whereas the channel statistics from the jammer to the receiver always greatly influences the performance. Finally, the numerical simulations are carried out to validate the proposed detection method.

In order to achieve accurate interference detection in complex electromagnetic environments, a two-step cooperative stochastic resonance energy detection (TCSRED) algorithm is proposed to address the problem, where the traditional energy detection (ED) performance is susceptible to noise uncertainty. By combining two thresholds and two-step cooperation, the generalized stochastic resonance is applied to the energy detection, which effectively reduces the complexity and detection time. In particular, when a certain decision result is obtained in the first step of detection, the decision is finished and the second step of detection is unnecessary. Otherwise, the second step of detection is performed to obtain the final decision result. Simulation results show that the proposed algorithm is robust to the noise uncertainty. Even in the case of a low signal-to-noise ratio (SNR), it also performs better than existing methods without significant increment of the complexity.

when manufacturers release patches, they are usually released as binary executable programs.Vendors generally do not disclose the exact location of the vulnerabilities, even they may conceal some of the vulnerabilities, which is not conducive to study the in-depth situation of security for the need of consumers.In this paper we introduce a vulnerability discover method using machine learning based on patch information -SemHunt.Firstly, we use it to compare two versions of the same program to get the potential vulnerability-patched function pairs using binary comparison technology.Then, we combine it with vulnerability and patch knowledge database to classify these function pairs and identify the possible vulnerable functions and the vulnerability types.We completed a prototype of SemHunt, which can effectively identify vulnerable function types and the location of corresponding vulnerabilities, which are not revealed in the released patch files.Finally, we test some programs containing real-world CWE vulnerabilities, and one of the experimental results about CWE843 shows that the results returned from only searching source program are about twice as much as the results from SemHunt.We can see that using SemHunt can significantly reduce false positive rate of discovering vulnerabilities compared with analyzing source files alone.

This study presents a blind carrier frequency offset (CFO) tracking algorithm for orthogonal frequency division multiplexing systems with constant modulus signalling. Both single-input single-output and multiple-input multiple-output (MIMO) systems are considered. Based on the assumption that the channel frequency response (CFR) has been estimated by training symbols and keeps constant over one frame duration, we discover that the CFO can be estimated via minimising the power difference between the received signals and the CFR. The polynomial rooting method is exploited to derive a low complexity solution. The expectation and mean-square error of the proposed method are derived mathematically. Besides, the effect of channel estimation error on the performance of CFO tracking is addressed and it is shown that the proposed algorithm is robust to this error. At last, this blind scheme can be applied to a MIMO system with the aid of space time block codes.

A low-complexity, blind CFO estimator for OFDM systems with constant modulus (CM) signaling is presented. Both single-input single-output (SISO) systems and multiple-input multiple-output (MIMO) systems are considered. Assuming the channel keeps constant during estimation, the CFO can be uniquely and exactly estimated through minimizing the power difference of the received data on the same subcarrier between two consecutive OFDM symbols, thus the identifiability problem is solved. Inspired by the sinusoid-like cost function, curve fitting is applied to simplify our algorithm. This blind scheme can also be applied to a MIMO system.