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IEEE 802.11ax is the standard for the new generation WiFi networks. In this paper, we formulate the problem of joint access point (AP) placement and power-channel-resource unit assignment for 802.11ax-based dense WiFi. The objective is to minimize the number of APs. Two quality-of-service (QoS) requirements are to be fulfilled: (1) a two-tier throughput requirement which ensures that the throughput of each station is good enough, and (2) a fault tolerance requirement which ensures that the stations could still use WiFi even when some APs fail. We prove that this problem is NP-hard. To tackle this problem, we first develop an analytic model to derive the throughput of each station under the OFDMA mechanism and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results under both fixed-user and mobile-user cases show that: (1) when the area is small (50 x 50 m2), our algorithm gives the optimal solutions; when the area is larger (80 x 60 m2), our algorithm can reduce the number of APs by 34.9-87.7% as compared to the Random and Greedy algorithms. (2) Our algorithm can always get feasible solutions that fulfill the QoS requirements.

Driven by a wide range of essential applications, significant achievements are made to explore WiFi-based Human Activity Recognition (HAR) techniques that utilize the information collected by commercial off-the-shelf WiFi infrastructures to infer human activities without the need for subjects to carry any devices. Although existing WiFi-based HAR systems achieve satisfactory performance in some instances, they are faced with a severe challenge that the impacts of ubiquitous Co-channel Interference (CCI) on WiFi signals are inevitable. This downgrades the performance of these HAR systems significantly. To address this challenge, we propose PhaseAnti, a novel WiFi-based HAR system to exploit the CCI-independent phase component, Nonlinear Phase Error Variation (NLPEV), of WiFi Channel State Information to cope with the negative effects of CCI. The stability of NLPEV data and the sensibility of this component to motions are rigorously analyzed. Furthermore, validated by extensive properly designed experiments, this phase component across subcarriers is invariant under various CCI scenarios while sufficiently distinct for different motions. Therefore, the NLPEV data can be used and processed effectively to perform HAR in CCI scenarios. Extensive experiments with various daily activities in different indoor rooms demonstrate the superior effectiveness and generalizability of the proposed PhaseAnti system under various CCI scenarios.

In VANETs, it is important to support fast and reliable multi-hop broadcast for safety-related applications. The performance of multi-hop broadcast schemes is greatly affected by relay selection strategies. However, the relationship between the relay selection strategies and the expected broadcast performance has not been fully characterized yet. Furthermore, conventional broadcast schemes usually attempt to minimize the waiting time difference between adjacent relay candidates to reduce the waiting time overhead, which makes the relay selection process vulnerable to internal interference, occurring due to retransmissions from previous forwarders and transmissions from redundant relays. In this paper, we jointly take both of the relay selection and the internal interference mitigation into account and propose a fast, reliable, opportunistic multi-hop broadcast scheme, in which we utilize a novel metric called the expected broadcast speed in relay selection and propose a delayed retransmission mechanism to mitigate the adverse effect of retransmissions from previous forwarders and an expected redundancy probability based mechanism to mitigate the adverse effect of redundant relays. The performance evaluation results show that the proposed scheme yields the best broadcast performance among the four schemes in terms of the broadcast coverage ratio and the end-to-end delivery latency.

The proposed approach is designed to boost the IEEE 802.15.4 / ZigBee-based WSN streaming efficiency by introducing multipurpose routing. We investigate how those networks can be designed in order to achieve optimum throughput using computational models to explain intra-track and inter-track interference in multipath routing networks. One mode of action in particular – Spatial-TDMA (S-TDMA), Zig Bee dependent WSNs, will greatly reduce the amount of interference, both in one- and multi-track environments. It is also seen that two-way networks can be stronger than their one-way counterparts by means of deliberately selected implementation parameters. Finally, a greater degree of spatial isolation between the routes used indicates improved average efficiency in multi-path scenarios. The simulation is one of our results.

This work considers a cognitive radio (CR) network consisting of a set of CR transmit-receive node pairs, one fusion center (FC), multiple primary user emulation attack (PUEA) nodes, an eavesdropper (Eav) node and a set of friendly jammers for protection of CR data from eavesdropping. At the initial time slot, simultaneous energy harvesting (EH) and spectrum sensing are done through power splitting (PS). CR nodes then amplify and forward the samples of primary user (PU) and the PUEA to the FC for cooperative spectrum sensing (CSS). Based on CSS decision, CR transmit nodes either perform EH over the entire duration or make an opportunistic data transmission in time division mode. The closed form expressions of the optimal sensing duration, power allocation factor and transmit power for each secondary user (SU) are found. The sum secondary throughput of the network is maximized under the constraints of meeting the sensing reliability of the PU, individual energy causality for each SU and the best selected jammer, interference at the PU receiver, individual secondary and secrecy outage probability. Simulation results show a performance gain on the maximum value of the sum secondary throughput by ~ 17.56% and ~ 49.69% over the existing works.

We propose a novel interference alignment (IA) technique with receive antenna partitioning (RAP) for simultaneous wireless information and power transfer (SWIPT)-enabled downlink fog radio access networks (F-RANs). The basic idea of the proposed scheme is to apply a generalized downlink IA for each access point (AP) and to divide receive antennas at each user into two groups for information decoding (ID) and energy harvesting (EH), respectively. With the proposed scheme, an optimal receive antenna configuration at each user is obtained to maximize the weighted sum of achievable rate and the sum of harvested energy of the network. Also we develop an antenna configuration selection algorithm to obtain near-optimal performance with low computational complexity. Through extensive computer simulations, it is shown that the proposed scheme significantly improves a rate-energy region compared with existing techniques.

Multi-agent reinforcement learning is widely used and can successfully solve many problems in the real world. In the multi-agent reinforcement learning system, a global critic network is used to guide each agent’s strategy to update to learn the most beneficial strategy for the collective. However, the global critic network also makes the current agent’s learning be affected by other agents’ strategies, which leads to unstable learning. To solve this problem, we propose Dynamic Decomposed Multi-Agent Deep Deterministic Policy Gradient (DD-MADDPG): a new network that considers both global and local evaluations and adaptively adjusts the agent’s attention to the two evaluations. Besides, the use of the experience replay buffer by Multi-Agent Deep Deterministic Policy Gradient(MADDPG) produces outdated experience, and the outdated strategies of other agents further affect the learning of the current agent. To reduce the influence of other agents’ outdated experience, we propose TDError and Time-based experience sampling (T2-PER) based on DD-MADDPG. We evaluate the proposed algorithm’s performance according to the learning stability and the average return obtained by the agents. We have conducted experiments in the MPE environment. The results show that the proposed method has better stability and higher learning efficiency than MADDPG and has a certain generalization ability.

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we propose MDSR, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, MDSR takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph, MDSR improves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluate MDSR on the testbed using commodity mmWave radios. The evaluation results demonstrate that MDSR improves network throughput by multi-folds compared with the state-of-the-art one.

Recently, the correlation filter (CF) and Siamese network have become the two most popular frameworks in object tracking. Existing CF trackers, however, are limited by feature learning and context usage, making them sensitive to boundary effects. In contrast, Siamese trackers can easily suffer from the interference of semantic distractors. To address the above problems, we propose an end-to-end target-insight correlation network (TICNet) for object tracking, which aims at breaking the above limitations on top of a unified network. TICNet is an asymmetric dual-branch network involving a target-background awareness model (TBAM), a spatial-channel attention network (SCAN), and a distractor-aware filter (DAF) for end-to-end learning. Specifically, TBAM aims to distinguish a target from the background in the pixel level, yielding a target likelihood map based on color statistics to mine distractors for DAF learning. SCAN consists of a basic convolutional network, a channel-attention network, and a spatial-attention network, aiming to generate attentive weights to enhance the representation learning of the tracker. Especially, we formulate a differentiable DAF and employ it as a learnable layer in the network, thus helping suppress distracting regions in the background. During testing, DAF, together with TBAM, yields a response map for the final target estimation. Extensive experiments on seven benchmarks demonstrate that TICNet outperforms the state-of-the-art methods while running at real-time speed.

Wave run-up on surface-piercing columns is an important phenomenon in offshore engineering. Previous studies have mainly focused on a single column and circular column arrays. This study is concerned with the wave run-up ratio and the wave force on a fixed array of four rounded square columns. A series of regular wave tests were conducted, and the effects of wave periods and steepness were investigated. The nonlinearity of the wave run-up under incident waves with a small period is more sensitive to wave steepness. Utilizing empirical mode decomposition (EMD) and fast Fourier transform (FFT) methods, the measured signals were separated into three components representing characteristics of the incident wave component (IMF1), near-field interference (IMF2), and far-field low frequency reflected wave (IMF3), respectively. The proposed methods are helpful for studying the mechanism of near-field interference, and the experimental data are essential benchmarks for validating further numerical simulations of wave run-ups on rounded square columns.

Stiffened panels have been widely utilized in fuselages and wings as critical load-bearing components. These structures are prone to be damaged under long-term and extreme loads, and their health monitoring has been a common concern. The guided wave-based monitoring method is regarded as an efficient approach to detect the damage in stiffened plates because of its wide monitoring range and high sensitivity to micro-damage. Efficient simulation of wave propagation can theoretically demonstrate the detection mechanism of the method. In this study, a Time-Domain Spectral Finite Element Method (TD-SFEM) is adopted to study the wavefield in stiffened plates, where continuous Absorbing Layers with Increasing Damping (ALID) strategy is proposed to circumvent the disturbance of reflected waves on boundaries. After the convergence analysis, the developed TD-SFEM with ALID is validated by the finite element method first. Then, wave scattering and the influence of the stiffener are investigated in detail by comparing the results with the non-stiffened structure. Finally, the effects of the parameters of the stiffener, such as the height and width, on wave propagation are studied, respectively. The results illustrate that the proposed TD-SFEM with ALID is an efficient approach to study the wave propagation in the stiffened plate and can reveal the mechanism of influence of the stiffener. It is found that the height of the stiffener changes the interference of wavefield in the plate, while the effects of the width are mainly in wave scattering and mode conversion.

Conflict tasks have become one of the most dominant paradigms within cognitive psychology, with their key finding being the conflict effect: That participants are slower and less accurate when task-irrelevant information conflicts with task-relevant information (i.e., incompatible trials), compared to when these sources of information are consistent (i.e., compatible trials). However, the conflict effect can consist of two separate effects: Facilitation effects, which is the amount of benefit provided by consistent task-irrelevant information, and interference effects, which is the amount of impairment caused by conflicting task-irrelevant information. While previous studies have attempted to disentangle these effects using neutral trials, which contrast compatible and incompatible trials to trials that are designed to have neutral task-irrelevant information, these analyses rely on the assumptions of Donder's subtractive method, which are difficult to verify and may be violated in some circumstances. Here, we develop a model-based approach for disentangling facilitation and interference effects, which extends the existing diffusion model for conflict tasks (DMC) framework to allow for different levels of automatic activation in compatible and incompatible trials. Comprehensive parameter recovery assessments display the robust measurement properties of our model-based approach, which we apply to nine previous data sets from the flanker (6) and Simon (3) tasks. Our findings suggest asymmetric facilitation and interference effects, where interference effects appear to be present for most participants across most studies, whereas facilitation effects appear to be small or nonexistent. We believe that our novel model-based approach provides an important step forward for understanding how information processing operates in conflict tasks, allowing researchers to assess the convergence or divergence between experimental-based (i.e., neutral trials) and model-based approaches when investigating facilitation and interference effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Recently, reconfigurable intelligent surface (RIS) has attracted continuous attention in wireless communications. Because the normal incidence condition of electromagnetic waves cannot always be satisfied in practical applications, the angular insensitivity in RISs has become an important issue. The angular sensitivity in RISs may lead to the failure of RIS-assisted wireless communication networks that rely on the reciprocity of wireless channels. In this work, an angle-insensitive 3-bit RIS meta-atom is proposed. By introducing metallic vias in the substrate of programmable meta-atoms, the interference induced by multiple reflections in the multilayered structure is restrained, and the decoupling between adjacent meta-atoms makes the metasurface insensitive to the incident angle. Hence the angular insensitivity can be achieved. An RIS is fabricated using the proposed angleinsensitive programmable meta-atom with metallic vias, which is capable of maintaining stable phase and amplitude responses at oblique incidences. Both simulated and measured results show stable angle performance. A maximum of 3150 phase range and eight digital coding states with 450 stable interval are obtained for wide incidence angles from 00 to 600. Finally, numerical simulations and experimental results of anomalous reflections are performed to verify the angular reciprocity of the proposed RIS, which is important in wireless communications.

Diffusion-based molecular communication (DBMC) systems allow messenger molecules to reach the intended receiver using laws of diffusion. The Brownian motion of messenger molecules introduces delay in the system causing the problem of inter-symbol interference (ISI). Along with ISI, the problem of inter-link interference (ILI) comes into picture in the diffusion-based molecular multiple-input multiple-output (DB-MoMIMO) systems. To combat against ISI and ILI issues, index modulation (IM) approaches are recently proposed in the literature for improving the performance of DB-MoMIMO systems. Despite the existence of IM approaches, problems of ISI and ILI still persist due to the slow random motion of molecules in DB-MoMIMO channels. Motivated by this, a novel coded modulation approach called molecular spatio-temporal coded modulation (MSTCM) is proposed in this paper which shows promising improvement in the performance of DB-MoMIMO systems by encoding the information both in space and time axes. Further, if system has access to two types of molecules, the dual-type modulation schemes are discussed for the same. Moreover, the encoder and decoder designs for the proposed mapping strategy are provided. In addition, a theoretical bit error rate expression for the MSTCM scheme is derived and simulation results are shown to corroborate the theoretical ones.

Infrared (IR) small target detection is one of the key technologies of infrared search and track (IRST) systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure (PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously. With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker (RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.

In this paper, Interference Reduction Scheme for Device-to-Device (D2D) Mobile Groups Using Uplink NOMA (ISHU) is proposed to maximize the throughput of the network. To achieve this goal, we integrated uplink non-orthogonal multiple access (NOMA) in the D2D mobile groups (DMGs). DMGs improve the spectral efficiency by sharing the resources with cellular mobile users (CMUs) whereas, uplink NOMA in DMGs associate the large number of D2D mobile users (DMUs) with the D2D transmitter (DDT). ISHU jointly optimizes the user group association and resource allocation in the uplink NOMA-enabled DMGs. The problem of joint user association and resource allocation is formulated as a mixed integer non-linear programming. To address this problem, we divided the problem of interference mitigation in two sub-problems and solved it independently. Firstly, for joint user group association and subcarrier assignment, a 3-D matching game is designed between the DMUs, DDT, and sub-carriers, respectively. Secondly, to optimize the power of DMUs across each sub-carriers, the branch and bound (BB) technique is used. Also, to reduce the complexity of ISHU scheme, the successive convex approximation low complexity (SCALE) technique is used across each subcarrier.

Memory representations can be stored in a passive state in a visual working memory (VWM) task. However, it remains unclear whether the representations stored in the passive state are prone to interference and decay. To explore this issue, we asked participants to successively remember two sets of memory items (M1 and M2) in three test manners: a combined test (both M1 and M2 are probed simultaneously), a backward test (probe M2 first and M1 second), or a forward test (probe M1 first and M2 second). We found that the contralateral delay activity (CDA) amplitude after the onset of M2 only tracked M2 independently of M1 in the two separate tests (Experiments 1-3), and the accuracy of M1 was well above chance. These results implied that the M1 representations had been transferred from the online state into the passive state after the onset of M2. Furthermore, the accuracy of M1 (two representations were transferred from the online state into the passive state and retrieved later) in the backward test was worse than M2 (2 representations in the online state throughout) in the backward test (Experiments 1-2), but was comparable to M1 (two representations were transferred from the online state into the passive state and retrieved first) in the forward test (Experiment 2). These results demonstrated that the memory representations were impaired during state switching. Importantly, once the representations had been stored in the passive state, they were robust with little memory loss during latent retention. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Identifying a spectral frequency band with abundant fault information from spectral coherence is essential for improved envelope spectrum-based bearing diagnosis. Both blind features and targeted features have been employed to distinguish informative spectral frequency band of spectral coherence. However, how to select appropriate feature to correctly discriminate the optimal frequency band of spectral coherence in different scenarios is problematic. In this study, a new targeted feature is presented to quantify the signal-to-noise ratio in narrow frequency bands of spectral coherence, and further a method based on the proposed feature is developed to distinguish an optimal spectral frequency band of spectral coherence for bearing diagnostics. The efficiency of the developed method, typical blind feature-based methods and typical targeted feature-based methods in identifying the defect-sensitive frequency band of spectral coherence and bearing fault diagnosis is validated and compared using simulated signals with different interference noises and bearing experimental signals. The advantages and limitations of typical blind and targeted feature-based methods in different scenarios are summarized to guide the application. The results demonstrate that the developed targeted feature can efficiently evaluate bearing failure information in the cyclic frequency domain, and the presented approach can accurately discriminate the failure-related spectral frequency band of spectral coherence and detect different bearing faults compared with the methods based on the state-of-the-art features.

Bushing plays a significant role in insulation and mechanical support for power transformers. Its working state is closely related to the reliable operation of power transformers. To overcome the electromagnetic interference and security risks during sensor installation, a Sagnac interference optical technique for partial discharge (PD) detection is proposed in this paper. By establishing a simulation model of the sensing structure, it is verified that output signal amplitude varies periodically with the length of sensing and delay fibers. Moreover, the output value decreases with increasing probe radius. The results show that a delay fiber of 1.5 km, a sensing fiber of 3 m, and a bending radius of 5 mm are optimal parameter combinations to detect PD in a 72.5 kV defective bushing. In addition, consistent detection waveforms induced by both internal and external defects are obtained by the optical sensor and high frequency current transformer (HFCT) simultaneously, then the effectiveness of the optical sensor on detecting PD signals for bushing is verified. It is of great help to carry out online monitoring of bushing insulation defects through optical non-destructive installation.

Autonomous Vehicles (AVs) rely on a set of radar sensors operated on frequency modulated waveforms. Due to the large bandwidth requirement of frequency modulated radars, only a limited number of proximate vehicles can be allowed for a concurrent transmission within the available spectrum. However, with the explosive growth of AVs, the available spectrum may soon reach its capacity. As a results, the coexistence of multiple AVs working on same resource, may lead to the problem of Radar-to-Radar (R2R) interference, also known as radar blindness. In this paper, we propose the notion of coded waveforms to minimize the R2R interference among the vehicles operating on the same resource. Specifically, the spread spectrum codes have been used as another degree of freedom (i.e., along with time-frequency resources) to spread the inter-vehicular radar interference over the wider spectrum, which enable to orthogonalize more number of AVs over the available range of spectrum. In addition, we have formulated a Spread Spectrum-based Radar Transmission Scheme (SS-RTS), and described the transmission and reception through SS-RTS. Also, the SS-RTS has been compared with the existing Graph-based Resource Allocation (GRA) scheme. Further, simulation results verified that SS-RTS significantly reduces the inter-vehicular R2R interference and outperforms GRA in terms of blind probability.

Signal time-frequency analysis is widely used in industrial fields, for example, machinery fault diagnosis. Current methods cannot well address the multi-components signals with crossing frequency trajectories. For example, the adaptive decomposition methods have mode mixing problem. The linear transforms have smear problems due to the mutual interference caused by crossing frequencies. To address these issues, this paper proposes an iterative approach, named adaptive linear chirplet transform (ALCT). It works by 1) calculating the signal linear chirplet transform (LCT) with a series of chirp rates as parameters; 2) iteratively estimating the characteristics of the signal mode with the highest amplitude in the residual LCT and updating the residual LCT by removing the LCT of the detected mode; 3) constructing the signal time-frequency representation (TFR) based on the estimated characteristics of the detected modes. The ALCT can clearly reveal the TFRs of signals with crossing frequency trajectories. It does not have the frequency estimation errors caused by mode mixing and avoids the mutual interference of the crossing frequencies. It is efficient, as it can obtain the LCT of the detected mode by directly weighting and shifting the LCT reference. The effectiveness of the ALCT is validated using simulations and real-world signals.

Uncertainty is inevitable in the decision-making process of real applications. Quantum mechanics has become an interesting and popular topic in predicting and explaining human decision-making behaviors, especially regarding interference effects caused by uncertainty in the process of decision making, due to the limitations of Bayesian reasoning. In addition, complex evidence theory (CET), as a generalized Dempster-Shafer evidence theory, has been proposed to represent and handle uncertainty in the framework of the complex plane, and it is an effective tool in uncertainty reasoning. Particularly, the complex mass function, also known as a complex basic belief assignment in CET, is complex-value modeled, which is superior to the classical mass function in expressing uncertain information. CET is considered to have certain inherent connections with quantum mechanics since both are complex-value modeled and can be applied in handling uncertainty in decision-making problems. In this article, therefore, by bridging CET and quantum mechanics, we propose a new complex evidential quantum dynamical (CEQD) model to predict interference effects on human decision-making behaviors. In addition, uniform and weighted complex Pignistic belief transformation functions are proposed, which can be used effectively in the CEQD model to help explain interference effects. The experimental results and comparisons demonstrate the effectiveness of the proposed method. In summary, the proposed CEQD method provides a new perspective to study and explain the interference effects involved in human decision-making behaviors, which is significant for decision theory.

We develop a scheme for the computation of the full-counting statistics of transport described by Markovian master equations with an arbitrary time dependence. It is based on a hierarchy of generalized density operators, where the trace of each operator yields one cumulant. This direct relation offers a better numerical efficiency than the equivalent number-resolved master equation. The proposed method is particularly useful for conductors with an elaborate time-dependence stemming, e.g., from pulses or combinations of slow and fast parameter switching. As a test bench for the evaluation of the numerical stability, we consider time-independent problems for which the full-counting statistics can be computed by other means. As applications, we study cumulants of higher order for two time-dependent transport problems of recent interest, namely steady-state coherent transfer by adiabatic passage and Landau-Zener-St\"uckelberg-Majorana interference in an open double quantum dot., Comment: 7 pages, 5 figures

In daily pipeline inspection, it is significant to ensure good network communication and security. With the development of drone technology, it is possible to apply drones as air routers to collect information from pipeline networks and transmit it to pipeline inspectors. It is also crucial to achieve optimal drone deployment in pipeline networks. This article proposes a two-phase evolution optimal 3-D drone layout algorithm to deploy drones in pipeline networks. First, a 3-D pipeline graph model is designed to represent the possible projection position of drones, and the objective function is proposed for optimal drone deployment. Then, in the first phase, based on the features of the 3-D pipeline graph, the drone flight rules and constraint conditions are presented to calculate the number of drones and the initial layout sequence. In the second phase, according to the objective function and the above results, every drone is continuously moved in a small area to achieve a tradeoff between signal coverage and interference. Moreover, the key parameters of the objective function can be discussed to further optimize drone deployment. Simulation results are presented to illustrate the effectiveness and advantages of the proposed algorithm.

The Internet of Vehicles (IoV) has always attracted attention as the emerging communication network with the most development potential in the 5G era. However, the performance of IoV under 5G ultra-dense networks is an open issue, especially in practice the outage probability and ergodic capacity of the relay cooperative IoV network under aggregate interference are still unclear. Therefore, an opportunistic Decoding and Forwarding (DF) relay cooperative transmission algorithm was proposed in this paper when the destination node of IoV has aggregated interference. In addition, based on mathematical theoretical knowledge such as numerical analysis, the closed expressions of the outage probability and ergodic capacity of the IoV system under aggregated interference was derived. Finally, simulation experiments verify the effectiveness of the proposed scheme and the correctness of the theoretical analysis, which improves the transmission rate of the system.

In this paper, the estimation problem of Toolface is investigated for the dynamic point-the-bit rotary steerable system (DPRSS), and it is essentially a nonlinear filtering problem under strong interference. Firstly, the DPRSS is modeled by a continuous-time nonlinear system which involves polynomial nonlinearities and strong interference whose amplitude is much larger than that of system states. By employing the Carleman approximation approach, a linear parameter varying system is derived from the polynomial nonlinear system, and a novel polynomial filter is constructed. Subsequently, several sufficient conditions in terms of parameter-dependent linear matrix inequalities (PDLMIs) are obtained to ensure the input-to-state stability of the filtering error system. Furthermore, the filter parameter can be calculated by solving PDLMIs through the sum-of-squares decomposition method. Finally, an experimental study on the DPRSS prototype is provided to show the effectiveness and applicability of the developed filtering scheme.

Seabed reflection and scattering measurements were conducted at the New England Mud Patch to better understand the acoustic properties of fine-grained (muddy) sediments. The measurement philosophy and the measurements themselves are summarized. In addition, geoacoustic information accessed directly from the data in the time and frequency domains is presented. The main result is the existence of an angle of intromission. This observation proves that the mud sound speed is less than that of the water and yields a sediment sound speed ratio 0.9865 with outer bounds {0.985 0.989}. Another result is the observation of strong scattered arrivals from within the mud volume at/near normal incidence but not at low grazing angles. These are likely due to anisotropic sediment heterogeneities with a large horizontal to vertical scale. Evidence is also presented for a highly heterogeneous mud–sand horizon with lateral variability down to scales of order meters. Finally, the reflection measurements successfully capture Bragg interference patterns. Their importance is their substantial geoacoustic information content, which can be accessed by several inversion methods.

Identification of transmit-antenna number is of importance in cognitive Internet of Things with multiple-input multiple-output (MIMO). Previous studies on transmit-antenna number detection only consider Gaussian noise and ignore impulsive interference. In the practical wireless communication, impulsive interference may exist due to low-frequency atmospheric noise, multiple access and electromagnetic disturbance. Such interference can usually be modeled as symmetric alpha stable (SαS), which cause the performance degradation of conventional algorithms based on Gaussian model. In this paper, we present a novel scheme to detect the transmit-antenna number for MIMO systems in cognitive Internet of Things, assuming that signals are corrupted by both SαS interference and Gaussian noise. We first introduce a new approach to characterize the generalized correlation matrix, and provide its bound with SαS interference. Then, the discriminating feature vector is constructed by utilizing the higher-order moments (HOM) of eigenvalues of the generalized correlation matrix. Finally, an advanced clustering algorithm is employed to detect the transmit-antenna number, using the cluster where the minimum eigenvalue is located. The proposed algorithm avoids the need for a priori information about the transmitted signals, such as coding mode, modulation type and pilot patterns. Simulation experiments demonstrate the feasibility of the proposed transmit-antenna number detection scheme in MIMO systems with Gaussian noise and SαS interference.

To guarantee stringent real-time requirements of time-critical traffic in industrial systems, the IEEE time-sensitive networking (TSN) task group has standardized time-aware shaping (TAS) in IEEE 802.1Qbv, which schedules precise and periodic transmission times using pre-assigned traffic information. However, non-periodic/unexpected but time-critical traffic such as emergency events or alarms may occur in real industrial scenarios, and TAS does not provision for performance of traffic that are unknown a priori, nor the impact thereof on prescheduled traffic. Moreover, recalculating the schedule for every sporadic, non-isochronous event traffic is extremely difficult, complex, and costly. To address these challenges, we propose a novel enhancement to TAS, referred to as eTAS, which defines a new scheduling rule for immediate forwarding of emergency traffic to guarantee real-time performance, while dynamically extending the scheduled time windows to protect scheduled time-critical traffic from the interference of emergency traffic. We evaluate eTAS through extensive simulations on OMNeT++ under an advanced driver assistance system (ADAS) scenario for autonomous driving to show that eTAS effectively allows rapid transmission of event traffic with minimal impact on scheduled traffic, even for highly congested networks.

Cognitive Industrial Internet of Things (CIIoT) can improve transmission performance by utilizing the spectrum licensed to primary user (PU), providing that the normal communications of the PU is not disturbed. However, the traditional spectrum access schemes for the CIIoT are difficult to adapt to the various communication environments. In this paper, Q-learning based dynamic spectrum access is proposed for the CIIoT to intelligently utilize the spectrum resources in three access scenarios: orthogonal multiple access (OMA), underlay spectrum access and non-orthogonal multiple access (NOMA). In the OMA scheme, the CIIoT learns to access the idle channels to avoid distributing the PUs, but its communication continuity can not be guaranteed when most of the channels are occupied by the PUs. In the underlay scheme, the CIIoT learns to utilize the busy channels to ensure the communication continuity by limiting its transmit power within the tolerance of the PU. However, the interference to the PU cannot be eliminated, which will decrease the PU's throughput. In the NOMA scheme, however, the CIIoT can utilize the busy channels by cancelling the interference to the PU with Successive Interference Cancellation (SIC), which will guarantee both the transmission performance of the CIIoT and PU. A Q-learning based spectrum access algorithm is proposed to improve the transmission performance of the CIIoT in the three schemes. The simulation results have shown the advantages of the Q-learning based NOMA scheme in terms of guaranteeing the throughput of the CIIoT nodes and decreasing the interference to the PUs.

For a multiple-input multiple-output (MIMO) system, or a multiuser MIMO system, it is important to acquire the accurate channel state information. In this article, a superimposed-pilot-based channel estimation scheme is proposed for multicell multiuser MIMO uplink systems. In this scheme, the pilot sequences and data sequences are superimposed, but the space generated by the pilot sequences and the space spanned by data sequences are separated far enough in order to reduce interference. Based on this scheme, the minimum mean-square error channel estimation is deduced, and the optimal power allocation between the pilots and the data signals is derived to maximize throughput. These results show that our proposed scheme can overcome the pilot contamination and achieve better performance than the traditional time-multiplexing scheme with mutually orthogonal pilot sequences for different users when the coherent time slot is limited.

In order to solve the problems of highly redundant spatial information and motion noise in the heart rate (HR) estimation from facial videos based on remote Photoplethysmography(rPPG), this paper proposes a novel HR estimation method based on spatial-temporal attention model. Firstly, to reduce the redundant information and strengthen the association relationships of long-range videos, the spatial-temporal facial features are extracted by the 2D convolutional neural network (2DCNN) and 3D convolutional neural network (3DCNN), respectively. The aggregation function is adopted to incorporate feature maps into short segment spatial-temporal feature maps. Secondly, the spatial-temporal strip pooling is designed in the spatial-temporal attention module to reduce head movement noises. Then, via the two-part loss function, the model can focus more on the rPPG signal rather than the interference. We conduct extensive experiments on two public datasets to verify the effectiveness of our model. The experimental results show that the proposed method achieves significantly better performances than the state-of-the-art baselines: The mean absolute error could be reduced by 11% on the PURE dataset, and by 25% on the COHFACE dataset.

The grid-connected inverters (GCIs) that use droop control can avoid instability issues caused by the phase lock loops (PLLs) when connected to weak grids. Thus, the droop-controlled GCIs (DGCIs) have attracted more and more attentions. However, one of the limitations of the droop control is that its performance is highly influenced by the line impedances. Virtual inductances (VIs) can be used to overcome this limitation. However, if LCL filters are used by the GCIs to attenuate current harmonics, the interference between the VIs and the LCL filters will cause a new type of system oscillations. In this paper, the effects of the VI on the stability of the LCL filter have been explored when the grid-side current (GC) or the inverter-side current (IC) of the LCL filter is used as the feedback for the VI. It has been revealed that the stabilities of the system when using these two currents by the VI are different. The feasible ranges of the parameter values of the VI have been derived, with which the VI can be used without affecting the stability of the DGCIs. Simulation and experimental results have been provided, which verify the correctness of the analysis in this paper.

Multiple-input–multiple-output (MIMO) is a key technology used to meet the requirements of 5G systems. In millimeter-wave (mmWave) bands, directional beamforming is applied at both the transmitter and the receiver to establish an efficient radio link. The mmWave bands specified in 5G specifications range from 24.25 to 40.0 GHz. Accordingly, different countries may allocate different bands to support 5G services because of the availability of the spectrum and the countries’ radio management policies. For international roaming, user equipment (UE) must support multiband operations based on a fixed antenna hardware structure that is designed to target a specific operating band. Hence, the frequency discrepancy that results from using different operating bands causes distortion in the achieved beamforming pattern. In this article, we investigate the impact of frequency discrepancy on beamforming performance in mmWave phased array antenna systems at the UE side, considering the resultant beam pattern and interference. In this effort, we studied three major side effects—deviation of the beamforming direction, change in the effective array length, and change in the normalized interelement spacing. The numerical results indicate that a large frequency discrepancy causes severe distortion in the resultant beamforming pattern and introduces more interference into the system.

With the rapid development of non-geostationary orbit (NGSO) constellation networks, the co-frequency interference between geostationary orbit (GSO) and NGSO systems is becoming increasingly inevitable. Due to the characteristics of complex multi-mission planning and the large spatial and temporal scale of NGSO constellations, it is difficult to maximize the system performance under the constraints of GSO networks' interference protections. In this paper, based on an iterative greedy algorithm, a modified Q-learning approach in reinforcement learning (RL) is proposed to upgrade the system performance of NGSO network. Compared with the traditional RL algorithms, this modified approach has the advantages of improving the system performance and meeting the aggregate interference protections of GSO networks, by redefining the actions, rewards, and Q-tables. Simulation results show that by adopting the proposed algorithm, all NGSO links timely select and flexibly adapt their actions, including access objects, transmission powers, communication rates, as well as modulation and coding schemes, to achieve more global rewards with co-frequency interference constraints.

We study the propagation and distribution of information-carrying signals injected in dynamical systems serving as reservoir computers. Through different combinations of repeated input signals, a multivariate correlation analysis reveals measures known as the consistency spectrum and consistency capacity. These are high-dimensional portraits of the nonlinear functional dependence between input and reservoir state. For multiple inputs, a hierarchy of capacities characterizes the interference of signals from each source. For an individual input, the time-resolved capacities form a profile of the reservoir's nonlinear fading memory. We illustrate this methodology for a range of echo state networks.

The presence of array back lobe is a major source of interference, power loss, and degrades the signal-to-interference ratio (SIR) in wireless communication systems. In this paper, new distributed beamforming techniques based on an optimized elliptical arc geometry (EAG) are proposed for back lobe cancellation of linear antenna arrays (LAA). The optimized EAG allows for flexible control of the elements' locations and initial transmission phases of their radiations, allowing them to add constructively towards the main beam direction and add destructively towards the back lobe direction. Firstly, two EAG based beamforming techniques are introduced using either a single ellipse-EAG (SE-EAG) or double ellipses-EAGs (DE-EAG). In these techniques, the back lobes are minimized by optimizing both the minor axes of the elliptical arcs and the elements' angular distributions using the particle swarm optimization (PSO) technique. While the elements' transmission energies are kept the same as that of the original LAA. For further back lobe minimization, the double ellipses-EAG with optimized energies (DE-EAG-WOE) beamforming technique is introduced, where both elements' transmission energies and positions are optimized. To verify the superiority and the possibility of practical validation of the proposed techniques, the proposed distributed array configurations based on the DE-EAG-WOE are realized using the CST Microwave Studio software package using dipole elements.

The data transmission is always necessary for wireless power transfer system, however, the interference between power and data is the major problem for the full-duplex simultaneous wireless power and data transfer (SWPDT) system. To solve this problem, in this paper, a novel SWPDT system based on the double-side LCCL and dual-notch filter is proposed. In the power transfer channel, by the double-side LCCL topology the high impedance of power branches at the data carrier frequency reduces the power interference largely. In the data transfer channel, the dual-notch filter is utilized to separate the data carriers and realize the full-duplex communication, which could reduce the ipsilateral data interference and improve the signal-to-noise ratio (SNR). The transfer functions of data transmission is derived by the analysis on the impedance of each part. Since the interferences of power and ipsilateral data source are analyzed, the expression of SNR could be derived and the corresponding factors to improve the SNR are discussed. An optimization of data transfer channel to maximize the SNR is proposed by shifting the dual-notch filter and transformers. The experimental setup with 400W output power, 200kb/s data rate is conducted to verify the feasibility of proposed SWPDT system and the correctness of the design method.

Liquid leakage can cause industrial accidents. Current liquid leakage detection methods judge the leakage state by analyzing signals from special intrusion sensors, which is not ideal for early leakage due to sensor limited sensitivity. Visual information from surveillance systems deployed in industrial environments can reflect early leakage that cannot be monitored by such pressure sensors. In this paper, we propose a visual early leakage detection system based on a visual background extractor (Vibe) and EfficientNetB0. First, we extract the translucent and small potential leakage candidates based on Vibe, which include leakage targets and environmental interference. Then, to further recognize leakage targets in potential leakage candidates, we explore CNN models and a few recently proposed methods, and compare them with two different evaluation criteria. Our model based on EfficientNetB0 performs best and achieves 99.526% accuracy. Additionally, our CNN model for leakage recognition with a smaller size is feasible for industrial application. Experiments are conducted on the leakage dataset from surveillance video from the Tianjin Binhai Heating Station, and the detection results are consistent with real leakage situations. Our leakage detection system has high sensitivity and accuracy, which meets the requirements of early leakage detection.

Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs). Meanwhile, intelligent reflecting surface (IRS) has been recently proposed as a promising approach to enhance SE and energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. In this paper, we consider an IRS-assisted downlink CR system, in which a secondary access point (SAP) communicates with multiple SUs without affecting multiple PUs in the primary network and all nodes are equipped with multiple antennas. Our design objective is to maximize the achievable weighted sum rate (WSR) of SUs subject to the total transmit power constraint at the SAP and the interference constraints at PUs, by jointly optimizing the transmit precoding at the SAP and the reflecting coefficients at the IRS. To deal with the complex objective function, the problem is reformulated by employing the well-known weighted minimum mean-square error (WMMSE) method and an alternating optimization (AO)-based algorithm is proposed. Furthermore, a special scenario with only one PU is considered and AO algorithm is adopted again. It is worth mentioning that the proposed algorithm has a much lower computational complexity than the above algorithm without the performance loss. Finally, some numerical simulations have been provided to demonstrate that the proposed algorithm outperforms other benchmark schemes.

Steering vector and covariance matrix estimation mismatch along with moving jammer/platform, can affect the performance of null-steering beamformer. In this paper, by utilizing null-widening, null-deepening and diagonal loading shrinkage in space–time adaptive processing (STAP) structure, two methods for nullifying GNSS (global navigation satellite system) jamming/interference in cruise moving platforms are introduced. The enhanced performance of the proposed methods is investigated through numerical simulations. The results indicate improvement in the number of acquired GNSS satellites, in heavy jamming (GNSS denied) scenario, by reconstructing and acquiring at least 87% of satellites.

International audience; This paper presents a semi-analytical approximation of Symbol Error Rate (SER) for the well known LoRa Internet of Things (IoT) modulation scheme in the following two scenarios: 1) in multi-path frequency selective fading channel with Additive White Gaussian Noise (AW GN) and 2) in the presence of a second interfering LoRa user in flat-fading AW GN channel. Performances for both coherent and non-coherent cases are derived by considering the common Discrete Fourier transform (DF T) based detector on the received LoRa waveform. By considering these two scenarios, the detector exhibits parasitic peaks that severely degrade the performance of the LoRa receiver. We propose in that sense a theoretical expression for this result, from which a unified framework based on peak detection probabilities allows us to derive SER, which is validated by Monte Carlo simulations. Fast computation of the derived closedform SER allows to carry out deep performance analysis for these two scenarios.

In this paper, we consider the application of a cell-free (CF) Massive multiple input multiple output (MIMO) system to low power Internet of things (IoT) networks. CF Massive MIMO distributes many access points (APs) in a given area, and supports many IoT devices simultaneously using the same time and frequency resources. To support many IoT devices that have more than the number of service antennas within limited coherence time/frequency, reference signal (RS) should be reused. Generally, CF Massive MIMO with massive connectivity requires a lot of power, and thus it is difficult to use in low power IoT networks. In this regard, we propose an energy efficient (EE) power control scheme that can significantly reduce the radiation power of IoT devices. We notice that with many IoT devices, there is a quite large amount of interference, and if we choose the radiation power based on this information, the signal-to-interference plus noise ratio (SINR) becomes approximately independent of the radiation power. We show that our scheme can reduce the radiation power more than 90% compared to normal operation. We also show that with many IoT devices, there is SE hardening effect, and maximum ratio (MR) processing and minimum mean square error (MMSE) processing present similar performance, and thus MR processing could be sufficient for use in CF Massive MIMO with massive IoT connectivity.

Timeliness is an emerging requirement for many Internet of Things (IoT) applications. In IoT networks, where a large-number of nodes are distributed, severe interference may incur during the transmission phase which causes age of information (AoI) degradation. It is therefore important to study the performance limit of AoI as well as how to achieve such limit. In this paper, we aim to optimize the AoI in random access Poisson networks. By taking into account the spatio-temporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on explicit expressions of the optimal peak AoI and the corresponding optimal system parameters including the packet arrival rate and the channel access probability are further derived. It is shown that with a given packet arrival rate (resp. a given channel access probability), the optimal channel access probability (resp. the optimal packet arrival rate), is equal to one under a small node deployment density, and decrease monotonically as the spatial deployment density increases due to the severe interference caused by spatio-temproal coupling between transmitters. When joint tuning of the packet arrival rate and channel access probability is performed, the optimal channel access probability is always set to be one. Moreover, with the sole tuning of the channel access probability, it is found that the optimal peak AoI performance can be improved with a smaller packet arrival rate only when the node deployment density is high, which is contrast to the case of the sole tuning of the packet arrival rate, where a higher channel access probability always leads to better optimal peak AoI regardless of the node deployment density. In all the cases of optimal tuning of system parameters, the optimal peak AoI linearly grows with the node deployment density as opposed to an exponential growth with fixed system parameters.

In this paper, we consider wireless-powered two-way communication in an N-user interference channel with imperfect channel state information (CSI). In the system considered, the receivers harvest energy and receive information simultaneously from data signals sent by transmitters using a time switching (TS) policy, before transmitting response signals back to the transmitters in a subsequent phase using the harvested energy. We aim to find the resource allocation that allows the transmit power and TS ratio to be determined jointly to maximize the sum rate of the response links whilst guaranteeing a predetermined rate requirement for each data link, even in the presence of errors in the estimated CSI. To deal with the non-convexity of our optimization problem, we first introduce a gradient algorithm with a barrier function that finds suboptimal solutions heuristically. Moreover, to overcome the limitations of the gradient algorithm, e.g., its high computational complexity and vulnerability to channel error, we devise a robust strategy for resource allocation based on deep learning, in which artificially distorted CSI is fed into the deep neural network (DNN) during training to compensate for the incompleteness of the derived solutions caused by channel error. The performances of the considered schemes are examined through simulations, in which the proposed DNN scheme achieves a near-optimal performance with respect to the sum rate of the response links and outage probability under imperfect CSI, which validates its usefulness and robustness.

Here, we report measurements made with an external vibration-insensitive single-shot interferometer, which is applicable for on-machine measurement. Previously, we developed a simple single-shot interferometry method in which the optical intensities of interference fringes appear not as sinusoidal distributions but have a sawtooth-like distribution, and the peak distortion direction depicts the slope of the measured surfaces. These properties allow use of this method in vibration-insensitive interferometry. In this study, we have experimentally confirmed this concept through measurement of a quarter of a Si wafer specimen 50 mm in diameter. The vibration source was a motorized linear stage the surface of which fluctuated between ±400 nm in height. The obtained surface profiles of the Si wafer were consistent with measurements made with a commercial interferometer within 30 nm.

Targeting global EDF scheduling, this paper proposes an optimal algorithm for parallelizing tasks with parallelization freedom. For this, we extend the interference-based sufficient schedulability analysis and derive monotonic increasing properties of both tolerance and interference for the schedulability. Leveraging those properties, we propose a one-way search based optimal algorithm with polynomial time complexity. We present a formal proof of the optimality of the proposed algorithm. We first address the single-segment task model and then extend to the multi-segment task model. Our extensive experiments through both simulation and actual implementation show that our proposed approach can significantly improve the schedulability.