Complex Communication Environment Research Articles

Security Analysis of Continuous-Variable Measurement-Device-Independent Quantum Key Distribution Systems in Complex Communication Environments.

Continuous-variable measure-device-independent quantum key distribution (CV-MDI QKD) is proposed to remove all imperfections originating from detection. However, there are still some inevitable imperfections in a practical CV-MDI QKD system. For example, there is a fluctuating channel transmittance in the complex communication environments. Here we investigate the security of the system under the effects of the fluctuating channel transmittance, where the transmittance is regarded as a fixed value related to communication distance in theory. We first discuss the parameter estimation in fluctuating channel transmittance based on these establishing of channel models, which has an obvious deviation compared with the estimated parameters in the ideal case. Then, we show the evaluated results when the channel transmittance respectively obeys the two-point distribution and the uniform distribution. In particular, the two distributions can be easily realized under the manipulation of eavesdroppers. Finally, we analyze the secret key rate of the system when the channel transmittance obeys the above distributions. The simulation analysis indicates that a slight fluctuation of the channel transmittance may seriously reduce the performance of the system, especially in the extreme asymmetric case. Furthermore, the communication between Alice, Bob and Charlie may be immediately interrupted. Therefore, eavesdroppers can manipulate the channel transmittance to complete a denial-of-service attack in a practical CV-MDI QKD system. To resist this attack, the Gaussian post-selection method can be exploited to calibrate the parameter estimation to reduce the deterioration of performance of the system.

SARSA-based delay-aware route selection for SDN-enabled wireless-PLC power distribution IoT

The integration of power line communications (PLC) and wireless communications in power distribution internet of things (PD-IoT) provides a cost-efficient and easy-to-access solution to solve the problems of limited range of access and signal fading due to the influence of distance and obstacles. The traditional route selection methods are difficult to adapt to uncertain global state information (GSI) under dynamic network environment, the integration of PLC and wireless communications, and strong electromagnetic interference. In this paper, we put forward a state-action-reward-state-action (SARSA)-based Delay-aware Route Selection (SDRS) algorithm, which adopts software-defined networking (SDN) to simplify the network configuration and management in wireless-PLC PD-IoT. The proposed algorithm utilizes SARSA to evaluate and learn the optimal route selection strategy that can reduce transmission delay and improve reliability. Moreover, it can also adapt to the dynamic and complex communication environment. Compared with existing shortest route selection (SRS) algorithm and random route selection (RRS) algorithm, numerical results verify that SDRS has excellent performances in terms of transmission delay and reliability.

Link Redundancy Research and Reliability Analysis Based on Dual CAN Architecture

Aiming at the problem that special vehicles still need to ensure the reliability of vehicle-mounted bus communication in the long-term complex and strong interference communication environment, a link layer redundancy scheme based on dual CAN channels is proposed. By adding redundant modules and improving the redundancy mechanism, dual CAN controllers can not only realize the undisturbed switching of data communication in hot redundancy mode, but also realize the function of error correction and retransmission in cold redundancy mode. Finally, a mathematical model of the reliability of dual CAN redundancy is established through the Markov state transfer chain, and this model is compared with other models for analysis and evaluation to verify that its reliability is significantly improved.

Exploring UAV's multi-domain joint anti-jamming intelligent decision algorithm

To understand the complex communication environment of a UAV in battlefield, its unknown channel statistics information and poor intelligent jamming and anti-jamming capability, the multi-domain anti-jamming problem is studied, and a multi-domain joint anti-jamming intelligent decision algorithm is proposed. First, the channel selection method is adopted to deal with jamming in the frequency domain. A multi-arm slot machine's channel selection model is established, and the channel interference level is judged. Secondly, the moderate interference channel is suppressed in the power domain, and the model of its Stackelberg game is established. The game equalization is solved to obtain the best transmission power and reduce the overhead caused by channel switching. The simulation results show that the long-term rewards of the intelligent decision algorithm are significantly higher than those of the traditional multi-arm slot machine's algorithm and the random selection algorithm and that the average throughput of the communication system of the UAV is improved, thus proving the superiority of the intelligent decision algorithm.

Modulation Recognition of Communication Signals Based on Deep Learning Joint Model

In modern communication, it is often required for a non-cooperative party to identify the modulation mode when no prior knowledge is given to facilitate the subsequent demodulation and analysis. However, the traditional modulation recognition process requires cumbersome and uncertain manual signal-feature extraction, making it inapplicable to the complex communication environment. In order to overcome this limitation, this paper proposes a communication-signal modulation recognition model based on the dense connection network (DenseNet) and residual connection network (ResNet). The convolutional block attention mechanism (CBAM) is introduced into the DenseNet and ResNet structures, significantly enhancing the modulation recognition accuracy of the proposed global network model. Besides DenseNet and ResNet, the long short-time memory (LSTM) network is also adopted. The experimental results show the performance of the proposed model.

Performance Analysis and Prediction for Mobile Internet-of-Things (IoT) Networks: A CNN Approach

With the increasingly mature sensor technology and the increasing popularity of broadband network, “the Internet-of-Everything” era is coming, and the mobile Internet of Things (IoT) is booming around the world. However, the mobile IoT communication networks face serious challenges, which are caused by the complex and variable communication environments. The mobile IoT applications can produce large-scale data, which will consume substantial energy. The transmit antenna selection (TAS) and cooperative communication schemes are commonly used to reduce the complexity and the energy consumption, which directly impact the performance of mobile IoT networks. To evaluate the performance of mobile IoT networks, it is important to analyze outage probability (OP) performance. In this article, we investigate the OP performance analysis of mobile IoT communication networks and propose an OP intelligent prediction algorithm based on an improved convolutional neural network (CNN). First, the mobile OP performance is analyzed by combining the TAS and decode-and-forward cooperative schemes, and the exact OP expressions are derived. Then, an improved CNN is designed to avoid the loss of important information, which contains the input layer, three-convolution layer, one fully connected layer, and output layer. The proposed CNN-based prediction approach is compared with the radial basis function (RBF), generalized regression (GR), Elman, and extreme learning machine (ELM) methods. The simulation results validate that the proposed CNN prediction approach can achieve a better prediction effect than RBF, Elman, GR, and ELM methods. For the CNN approach, it has a 44% increase in the prediction accuracy.

Performance Evaluation of a Tactical Data-Link System Based on MSK and 16QAM

Tactical data links are becoming more widely used in the military, as they can greatly improve the efficiency of various combat units. Such tactical data links must be able to transmit large volumes of data at high speed in complex communication environments while ensuring reliability. This paper studies the influence of 16-ary Quadrature Amplitude Modulation (16QAM) and Minimum Frequency-shift Keying(MSK) modulation on the transmission performance of data chain system. The simulation results show that 16QAM modulation can meet the requirements of tactical data link system for BER and data transmission rate, and the technical complexity is in the middle; the system BER of MSK modulation is lower, and a large number of data transmission can be realized at high speed. When the SNR reaches 50dB, the BER of MSK modulation can be reduced to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> order of magnitude, which is better than Quadrature Phase Shift Keying (QPSK) modulation, that is, the data chain system based on MSK modulation can meet the transmission requirements of data chain when the signal-to-noise ratio is large.

Realization of UAV Routing Protocol Evaluation System Based on Game Theory Comprehensive Weighting

Aiming at the issue of the selectivity of routing protocols between UAV groups, a comprehensive weighting evaluation system based on game theory is proposed. Taking network simulation data as an example, three protocols, AODV, DSDV, and OLSR, are selected as the research objects. The results show that the DSDV protocol is suitable for the simple communication environment between UAV groups, the AODV protocol is suitable for the complex communication environment between UAV groups. In addition, the evaluation system is compared with the two evaluation systems of the Covariance Analytic Hierarchy Process (Cov-AHP) and the entropy method to calculate the relative deviation. The comparison results show that the new evaluation system is more reasonable than the other two evaluation systems.

Machine Learning-Based Resource Allocation Strategy for Network Slicing in Vehicular Networks

The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce the total system delay. Specially, we first formulated the radio resource allocation problem as a convex optimization problem to minimize system delay. Secondly, to further reduce delay, we design a Convolutional LSTM- (ConvLSTM-) based traffic prediction to predict traffic of complex slice services in vehicular networks, which is used in the resource allocation processing. And three types of traffic are considered, that is, SMS, phone, and web traffic. Finally, based on the predicted results, i.e., the traffic of each slice and user load distribution, we exploit the primal-dual interior-point method to explore the optimal slice weight of resources. Numerical results show that the average error rates of predicted SMS, phone, and web traffic are 25.0%, 12.4%, and 12.2%, respectively, and the total delay is significantly reduced, which verifies the accuracy of the traffic prediction and the effectiveness of the proposed strategy.

Application of intelligent orienteering based on Internet of things

Orienteering has gradually changed from a professional sport to a civilian sport. Especially in recent years, orienteering has been widely popularized. Many colleges and universities in China have also set up this course. With the improvement of people’s living conditions, orienteering has really become a leisure sport in modern people’s life. The reduced difficulty of sports enables more people to participate, but it also exposes a series of problems. As the existing positioning technology is relatively backward, the progress in personnel tracking, emergency services, and other aspects is slow. To solve these problems, a new intelligent orienteering application system is developed based on the Internet of things. ZigBee network architecture is adopted in the system. ZigBee is the mainstream scheme in the current wireless sensor network technology, which has many advantages such as convenient carrying, low power consumption, and signal stability. Due to the complex communication environment in mobile signal, the collected information is processed by signal amplification and signal anti-interference technology. By adding anti-interference devices, video isolators and other devices, the signal is guaranteed to the maximum extent. In order to verify the actual effect of this system, through a number of experimental studies including the relationship between error and traffic radius and the relationship between coverage and the number of anchor nodes, the data shows that the scheme studied in this paper has a greater improvement in comprehensive performance than the traditional scheme, significantly improving the accuracy and coverage. Especially the coverage is close to 100% in the simulation experiment. This research has achieved good results and can be widely used in orienteering training and competition.

Fuzzy Rule-Based Signal Restoration for Wireless Optical Communication of Multi-autonomous Underwater Vehicles

As a complex process, wireless optical communication of autonomous underwater vehicles (AUVs) is particularly challenging in the complex communication environment. Therefore, the existing control or denoise systems show some limitations for communication system of multi-AUV. It is still difficult to balance between the complexity of underwater environment and the performance of reality or estimation. This paper is about to investigate the possibility of combining this realistic communication environment for AUVs with existing strategies in fuzzy control framework. More specifically, first, a model for underwater wireless optical communication (UWOC) is proposed, then, signal restore model for UWOC based on fuzzy control is developed for simulation and analysis purposes. To illustrate the superiority of the proposed methods, several common denoising methods are experimented and compared together. It is also shown that the fuzzy control for signal restore model can well adapt to the real communication environment, and ensure more efficiency and stabilization.

Robust Power Control for 5G Small Cell Networks with Sleep Strategy

In this paper, the resource scheduling and power control strategy is studied for a two-tier 5G network system, which is comprised of a central macrocell and several small cells. Because cellular deployment is ultra dense in the 5G network, a sleep strategy for small cells is proposed to reduce energy consumption caused by the cellular overlapping coverage. Specifically, the imperfect channel state information (CSI) is considered due to the complex and dynamic communication environment in practice. This work attempts to maximize the resource utilization and enhance the robustness of the power control strategy with imperfect CSI. An optimization problem with probability constraints is formulated, and the high rate requirements of 5G network users are included. A novel method is provided to convert probability constraints into deterministic ones. An iterative algorithm of power control to enhance the network efficiency is proposed further. Finally, numerical results are given to illustrate the effectiveness of the joint resource scheduling scheme.

Monitoring scheme against local oscillator attacks for practical continuous-variable quantum-key-distribution systems in complex communication environments

In a practical continuous-variable quantum-key-distribution (CVQKD) system, a strong local oscillator (LO) signal in a quantum channel may be manipulated by eavesdropper Eve to steal information about secret keys through general attacks (e.g., entangling cloner attack or intercept-resend attack) without being detected, which is an effective quantum hacking strategy, i.e., LO attacks. To guarantee the practical security of the system, a general monitoring scheme has been previously demonstrated to resist this quantum hacking attack, where the channel transmittance was regarded as a fixed value related to transmission distance. However, practical communication environments are complex, which may result in the time-varying transmittance. This deviation may affect the effectiveness of this monitoring scheme. In this paper, we investigate the monitoring of CVQKD systems running in complex communication environments. We first model the LO attacks on practical CVQKD systems in complex environments, where the channel transmittance is assumed to obey a fixed distribution. Then, the low bound of intensity disturbance of the LO signal for Eve successfully concealing herself is obtained based on this model, where we consider all noise that can be used by Eve in complex communication environments. Simultaneously, we obtain an optimal monitoring condition to resist the LO attacks. Our numerical analysis confirms that the proposed monitoring scheme can effectively resist the LO attacks on practical CVQKD systems in complex environments. Subsequently, the feasibility of this way is again demonstrated by using a quantitative example. It is important that this monitoring scheme can also be extended to resist other quantum hacking attacks.

Optimisation of delay tolerance in wireless sensor networks based on unscented Kalman filter estimation

Delay tolerance in wireless sensor network can be applied to complex and harsh communication environment. In order to improve the communication performance of delay tolerance in wireless sensor network, the optimisation of delay tolerance in wireless sensor network based on Unscented Kalman filter estimation is studied. Firstly, the model of delay tolerance in wireless sensor network is established according to the theory of dynamics and position constraints, and then an unscented Kalman filter estimation algorithm is used. Through analysing the basic state and measurement equation of delay tolerance in wireless sensor network model, the measurement equation of round-trip delay model is obtained. The pre handover time estimated by unscented Kalman filter is set, to effectively reduce the pre handover time and energy consumption, and optimise the delay wireless sensor network. The experimental results show that the delay tolerance of wireless sensor network optimised by the proposed method is within 30 ms under different observation noises, and the packet loss rate and energy consumption are low.

Contemporary criminal defence practice: importance of active involvement at the investigative stage and related training requirements

ABSTRACTThe shifting focus of criminal proceedings from the trial to the pre-trial stages leads to a changing role of criminal defence practitioners across Europe. European criminal defence lawyers are now expected to enter the proceedings earlier and exercise “active” and “participatory” defence as early as the investigative stage. Criminal lawyers, trained in the traditional trial-centred paradigm, are ill-prepared for this role, which results in an important skills gap. Legal representation at the investigative stage presents unique challenges, such as shortage of information, time pressures and the closed nature of pre-trial proceedings. It requires lawyers to operate in a more complex communication environment, than the one to which they have been accustomed. This article sets out the main elements of a professional training programme aiming to fill in the emerging skills gap. The training programme (SUPRALAT) was successfully implemented in Belgium, Hungary, Ireland and the Netherlands, and is being expanded further. The training focuses on effective communication skills, experiential learning and the development of reflective skills. It includes elements of interprofessional training and encourages the development of “communities of practice”.

SHVC CU Processing Aided by a Feedforward Neural Network

The development of multimedia and hardware technologies has led to a great number of industrial video applications, such as virtual reality, high-definition video surveillance, and remote monitoring. As complex communication environments and heterogeneous networks are common in industrial applications, industrial videos are required to support a diverse range of display resolutions and transmission channel capacities. Scalable high-efficiency video coding (SHVC) standards provide the tools to meet this requirement. However, it is highly computationally expensive. Coding complexity has a great impact on SHVC performance in industrial applications. Many of these applications are sensitive to time delay and have limited power. Thus, improvements are required to ensure the practical usability of SHVC encoders. In SHVC encoders, intra/interprediction of variable coding unit (CU) sizes is independently performed for the base and enhancement layers (ELs). There are many interlayer similarities that can be exploited to speed up the procedure for EL coding. In this paper, we propose a feedforward neural network aided model for CU size and mode decisions for SHVC, which utilizes base layer coding information and the coding data of spatiotemporal neighboring CUs to decide which CU sizes or prediction modes can be bypassed for certain EL CUs. Two feedforward neural network based learning models are built for CU classification, which are introduced in the procedures for CU size and mode decisions, respectively. According to the analysis from a large number of video sequences, the representative features are directly extracted from the coding information of previously coded neighboring CUs to avoid computational overheads. After the training is finished, these two models are designed and integrated to build classifiers. Then, two online classification approaches are designed for the CU size and mode decision procedures to classify each CU's type. Finally, different candidate CU sizes and prediction modes are adaptively assigned for each type of CU. This approach outperforms the state-of-the-art fast SHVC/high-efficiency video coding (HEVC) algorithms with approximately 19–42% coding time savings or better compression efficiency, which will be beneficial for the realization of real-time scalable video coding.

Critical discourse analysis in political communication research: a case study of right-wing populist discourse in Australia

ABSTRACTThe purpose of this paper is to showcase critical discourse analysis (CDA) in political communication research through an empirical case study of right-wing populist discourse in Australia. It is argued here that critical discourse analysis is a valuable resource for the field of political communication for understanding an increasingly complex media and communication environment. The article serves as a worked example for communication scholars looking to apply critical discourse analysis in a political context. As such, it provides a detailed overview of the theoretical underpinnings of critical discourse analysis, its significant approaches and their shared tenets. The case study of Australian Senator Pauline Hanson’s 2016 maiden speech provides a step-by-step account of the processes involved in conducting a critical discourse analysis, including the strengths and limitations of this approach. The paper contributes to the growing calls within the field for a deeper engagement with qualitative and critical approaches.

Efficient target detection in maritime search and rescue wireless sensor network using data fusion

Maritime search and rescue wireless sensor network (MSR-WSN) has been a bedrock to discover the floating target after the shipwreck. In this paper, we first define a sea region of target detection and formulate a clustered topology of MSR-WSN. Second, we employ the sensor nodes of MSR-WSN to track the collective radio signal emitted by the mobile target. Each node firstly transmits the preprocessed perceived data to the cluster head node. Next, the data fusion center (DFC) collects a local decision of cluster head node through a binary hypothesis test and works out an accurate global decision. This paper emphasizes at designing both local and global data fusion rules based on the likelihood of ratio test statistics using a Neyman–Pearson lemma and Bayesian approach. One major stumbling block in the ocean lies in a complex and changing communication environment. There is a need for the DFC to develop a fusion rule of carrying out a dependable target detection to screen out the side effect of wave shadow. To address the concern, we propose a novel mobile target detection algorithm (NMTDA) based on information theory. The main idea is to dynamically calculate an adaptive decision threshold using both Kullback–Leibler divergence (KLD) and a global optimal decision statistics to enforce the accuracy of target detection. In addition, KLD is adopted to quantify the strength of wave shadow effect and tune Correct Detection/Flase Alarm probabilities of target detection. To conserve the overall MSR-WSN energy, DFC selects clusters with the maximum predictive information gain for MSR before next round search. Extensive simulation results demonstrate that our proposed mobile target detection algorithm works well in maritime search and rescue scenario.

USA dental health providers’ role in HPV vaccine communication and HPV-OPC protection: a systematic review

ABSTRACT Background. Improving human papilloma virus (HPV) vaccination coverage in the US will require healthcare providers to recommend the vaccine more effectively. To inform quality improvement efforts, we systematically reviewed studies of dental provider communication about HPV vaccination. Methods. We searched MEDLINE, CINAHL, ScienceDirect, PsycINFO and JSTOR in August 2018 to identify studies of dental provider knowledge, perceived role and communication about HPV, HPV vaccination and HPV-associated oropharyngeal cancer (HPV-OPC). Results. We identified 10 qualitative and quantitative studies. Results of the primarily descriptive studies showed that although there were some deficiencies in knowledge about HPV-related outcomes and its effect on the male population, most providers understand HPV as a sexually transmitted infection and know the HPV vaccine is available, yet many are not discussing the HPV-OPC link or recommending vaccination. Providers were less often to recommend HPV vaccination if they were uncomfortable discussing sex, perceived parents as hesitant, or believed patients to be low risk. Studies reported mixed results on providers’ perceived role in expanded HPV vaccination and HPV-OPC education, but indicated support for the role of professional organizations in promoting awareness. Conclusion. Interventions are needed to help dental providers perceive their role to deliver effective recommendations within the complex communication environment surrounding HPV vaccination and HPV-OPC education.

A Novel Acquisition Algorithm Based on PMF-apFFT for BOC Modulated Signals

Large Doppler shifts are inherent to high-speed relative motion between receiver and satellite and are likely to cause major problems during the code phase acquisition due to the introduced frequency ambiguity. In order to solve the problem of acquisition for the binary offset carrier (BOC) modulated the signal with large Doppler shift, a novel acquisition algorithm based on partial matched filter-all phase fast Fourier transform (PMF-apFFT) is proposed in this paper. In this method, all phase processing was adopted to weaken the effect of spectrum leakage after partial correlation, and two dimensional parallel searching for Doppler frequency on different code phases was completed by the maximum value after the cumulative average exceeding the threshold. The theoretical analysis and simulation results show that the correct code phase search and high precision estimation of Doppler frequency can be completed by the proposed algorithm at the same time. Compared with the traditional PMF-FFT acquisition algorithm, the proposed algorithm has stronger anti-noise performance and a better detection probability, and it is suitable for the complex communication environment of strong noise and large Doppler shift.