State Of Communication Channel Research Articles

Development of a simulation model and adjustment of hyperparameters of a neural network for predicting possible states of a network of small space carriages

The purpose of the research is to develop a simulation model and tune the hyperparameters of a neural network to predict possible states of a network of small spacecraft.The research methods are based on the concepts of the theory of artificial intelligence to control a group of small spacecraft (SSV) – the use of adaptive methods and tools to make decisions, similar to the mechanisms of human thinking. In relation to space communication systems with a heterogeneous structure, artificial intelligence methods and technologies are aimed at predicting the state of communication channels between network nodes and automatically reconfiguring a network of devices based on neural network learning processes. One of the most important functions of network software for the application of cognitive algorithms is to predict the quality of communication between pairs of SSVs.Results. A method has been developed for using the Transformer architecture neural network to predict possible states of the SSV network, which provides aggregation and time synchronization of data on the state of the SSV network, their use for training the neural network, as well as using the neural network to predict the quality of communication. The data format for the training sample has been created, based on the representation of the state of the SSV network, which ensures the generation of the initial state of the network, modeling the proactive mode of its operation, collecting SSV network state markers to generate training data sets in the form of chronological sequences grouped into frames, and allowing to reduce the amount of data transferred between SSVs when creating a training set. A simulation model of the SSV network has been developed, which provides generation of the initial state of the network, modeling of the proactive mode of its operation, and collection of information about the state of the SSV network to generate sets of synthetic training data.Conclusion. The article develops a simulation model of the SSV network for generating synthetic training data and predicting possible states of the SSV network, as well as a method for using a neural network to predict possible states of the SSV network.

ESTIMATION OF PROBABILISTIC AND STATISTICAL CHARACTERISTICS OF THE INFORMATIONAL STATE OF A COMMUNICATION CHANNEL

The methods for determining the informational state of a communication channel based on the assessment of probabilistic and statistical characteristics of errors at the channel output are presented. Adaptive information protection methods in digital communication systems, where encoder parameters change depending on the channel state, require the use of network quality control methods based on determining the statistical characteristics of the channel. It is shown that the bit error probability in the channel is constant for the presented error generation process and is independent of the method of dividing information into code blocks. The methods for assessing the statistical characteristics of the channel are based on the topological representation of error generation processes over the length of code blocks. It is demonstrated that, with a fixed code sequence length and a constant bit error probability at the output of the information channel, different error distributions correspond to sets of n-sequences. With a known number of states in the likelihood transition matrix, calculation of observation intervals for counting erroneous blocks is proposed. The presented method of researching statistical characteristics, based on the assumption of error independence, allows for the development of algorithms and procedures for assessing the state of discrete communication channels. The evaluation of potential bounds and statistical characteristics for the decoding error probability enables establishing a connection between the topological characteristics of error sequence partitioning and code parameters. The work confirms that the proposed method allows the synthesis of algorithms for determining code parameters, whose numerical values are determined based on minimizing potential decoding error probabilities.

Robust Beamforming for Sensing Assisted Integrated Communication and Jamming Systems

This paper investigates the robust beamforming design problem in a sensing assisted integrated communication and jamming (ICAJ) system, which aims to minimize the transmission energy consumption in a time duration while ensuring the communication requirements for multiple legitimate users and jamming demands for the suspicious network with multiple communication groups. To achieve these goals, we first design a sensing-communication and jamming (SCJ) protocol. Then, a robust beamforming design problem with both communication and jamming channel state information (CSI) estimation error is formulated as a non-convex optimization. To solve it, a joint optimization (JO) algorithm is developed to jointly optimize the beamforming matrices of communication and jamming, by leveraging the General S-procedure, Schur' complement and General Sign-definiteness techniques. Simulation results show that the proposed SCJ protocol with JO algorithm has superior performance, as compared to benchmark methods.

Robust Beamforming Design for Dual-Function Radar-Communication System

In this paper, we investigate the problem of robust beamforming for multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, when there exist radar look direction mismatch and communication channel state information (CSI) error. To address these system imperfections, a region-of-interest (ROI) rather than a specific angle is considered for radar sensing, and the communication CSI is assumed to be available with a bounded unknown error. With these considerations, we design a robust dual-function beamformer through optimizing the radar sensing performance in terms of the energy radiated towards the ROI, while guaranteeing the communication quality-of-service (QoS) measured by the per-user signal-to-interference-plus-noise ratio (SINR) as required. For this robust design, a max-min optimization problem is formulated and a semidefinite programming (SDP) reformulation based on the S-procedure is presented. The popular semidefinite relaxation (SDR) technique is employed to solve the resulting non-convex problem, and the tightness of SDR is discussed. Further, we propose to incorporate an extra constraint of main beam control into the problem to achieve a flat magnitude response of the beampattern over the ROI. In addition, it is shown that the proposed approach is straightforwardly applicable to the scenario of multiple targets. Numerical examples are carried out to show the tightness, and demonstrate the effectiveness and robustness of the proposed MIMO-DFRC beamforming approach.

Research on Vehicle Re-identification for Vehicle Road Collaboration

Vehicles and roads cooperate to perceive traffic targets, which can reduce the perception blind spots of vehicles and improve driving safety. In this paper, we proposes a vehicle re-identification method oriented to vehicle-road coordination. This method first designs a lightweight vehicle re-identification network based on ShufflenetV2 to solve the computational efficiency problem of vehicle-road coordination scenarios, which can efficiently complete vehicle feature extraction; then, due to the real-time requirements of scenario communication, an adaptive feature conversion mechanism is designed in combination with the LSH algorithm, which can make the re-identification module to dynamically perform binary bit feature conversion and adjust the dimension according to the communication channel state; finally, a loss function for the conversion of vehicle re-identification features is designed, which can greatly reduce the accuracy loss rate of converting floating-point features to bit features. Experiments show that our method can efficiently complete the information extraction and comparison of vehicle re-identification features in the vehicle-road coordination scenario, and can improve the perception efficiency of vehicle-road coordination while taking into account performance and bandwidth.

Distributed Task Scheduling in Serverless Edge Computing Networks for the Internet of Things: A Learning Approach

By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. Task scheduling is a critical issue in serverless edge computing as it significantly impacts the quality of user experience. In contrast to the centralized scheduling in the cloud center, serverless edge task scheduling is more challenging due to the heterogeneous and resource-constrained nature of edge resources. This article aims to study the distributed task scheduling for the IoT in serverless edge computing networks, in which heterogeneous serverless edge computing nodes are rational individuals with interests to optimize their own scheduling utility while the nodes only have access to local observations. The task scheduling competition process is formulated as a partially observable stochastic game (POSG) to enable serverless edge computing nodes to noncooperatively schedule tasks and allocate computing resources depending on their locally observed system state, which takes into account the associated task generation state, data queue state, communication channel state, and previous computing resource allocation state. To solve the proposed POSG and deal with the partial observability, a multiagent task scheduling algorithm based on the dueling double deep recurrent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (D3RQN) method is developed to approximate the optimal task scheduling and resource allocation solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

UAV Detection and Localization Based on Multi-Dimensional Signal Features

In recent years, unmanned aerial vehicles (UAVs) have received growing attention due to security threats issues. Though UAV detection and positioning systems are commonly used in various scenarios, most of the available systems are still suffering from low accuracy and susceptibility to the environment. Therefore, it is still necessary to design a highly accurate, versatile UAV detection and positioning system. In this paper, a UAV detection and positioning system based on multi-dimensional signal features is proposed. The first step of the system is to monitor the communication signal and channel state information (CSI) between the UAV and the controller. Subsequently the signal frequency spectrum (SFS), the wavelet energy entropy (WEE), and the power spectral entropy (PSE) are extracted as features. At the same time, machine learning algorithms combining the above features are applied to detect UAVs. After the UAV is successfully detected, the spatial features such as angle of azimuth (AOA) and angle of elevation (AOE) were extracted for UAV localization based on a super-resolution estimation algorithm. In conclusion, the Wireless Insite (WI) software was employed for long-distance positioning verification while the software-defined radio (SDR) was used for small-scale testing and verification. The experimental results show that the average detection rate of combining multiple features in the test environment is 95.58%, the median accuracy of 2D positioning is 0.76 m, and the median accuracy of 3D positioning is 1.2 m. In the WI simulation environment, the median accuracy of 2D positioning is 1.1 m, and the median accuracy of 3D positioning is 2.35 m.

Nonlinear iterative precoding algorithm for MIMO multiuser systems

A modification of the known linear precoding algorithm CLTD (closed-loop transmit diversity) intended for multiuser radio communication systems and using theMIMOtechnology has been proposed. The efficiency of the original precoding algorithm CLTD possessing a number of advantages as compared to earlier precoding methods based on complete information about the communication channel state is enhanced at the expense of introducing nonlinearity. The use of nonlinear procedure of ordered successive interference cancellation (OSIC) in forming the precoding matrix makes it possible to significantly increase the noise immunity of the entire system as a whole at the expense of a slight rise of algorithm computational complexity. In addition, the results of computer simulation are presented reflecting the efficiency of the proposed modification of the algorithm for forming a precoding matrix as compared to the original CLTD algorithm.

Моделирование систем передачи данных с адаптацией к состоянию канала связи

Моделирование систем передачи данных с адаптацией к состоянию канала связи

Channel estimation techniques for MIMO-OFDM system

In the wireless communications, channel estimation has an important role, because channel state information (CSI) is obtained base on this estimation. CSI allows the adaptation of transmitting signal to the actual state of communication channel, which is crucial in order to achieve fast and reliable communication on wireless channel. In this paper we give a brief overview of channel estimation techniques for MIMO-OFDM system.

On a two-pass scheme without a faraday mirror for free-space relativistic quantum cryptography

The stability of destructive interference independent of the input polarization and the state of a quantum communication channel in fiber optic systems used in quantum cryptography plays a principal role in providing the security of communicated keys. A novel optical scheme is proposed that can be used both in relativistic quantum cryptography for communicating keys in open space and for communicating them over fiber optic lines. The scheme ensures stability of destructive interference and admits simple automatic balancing of a fiber interferometer.

Improving rival penalized competitive learning using density‐evaluated mechanism

Rival penalized competitive learning (RPCL) and its variants have provided attractive ways to perform clustering without knowing the exact cluster number. However, they are always accompanied by problems of falling in local optima and slow learning speed. Thus we investigate the RPCL and propose a mechanism to directly prune the RPCL's structure by evaluating the data density of each unit. We call the new strategy density‐evaluated RPCL (DERPCL). The communication channel state is estimated by the DERPCL in the simulations, and comprehensive comparisons are made with other RPCLs. Results show that the DERPCL is superior in terms of convergence accuracy and speed.

Control of Spatially Interconnected Systems with Random Communication Losses

This paper deals with analysis and synthesis problems of spatially interconnected systems where communicated information may get lost between subsystems. Spatial shift operator and temporal forward shift operator are introduced to model the interconnected systems as discrete time-space multidimensional linear systems with Markovian jumping parameters which reflect the state of communication channels. To ensure the whole system s well-posedness and mean square stability for a given packet loss rate, a condition is derived through analysis. Then a procedure of designing distributed dynamic output feedback controllers is proposed. The controllers have the same structure as the plants and are solved within the linear matrix inequality (LMI) framework. Finally, we apply these results to study the effect of communication losses on the multiple vehicle platoon control system, which further illustrates the effectiveness of the proposed model and method.

User effect analysis on polarisation in multi-input multi-output systems

In this study the authors present the results of investigating the human head effects on the performance of a two-antenna system as applied to the polarisation of these antennas. For this, we define a 2×2 multi-input multi-output (MIMO) model constructed of two dipole transmitting antennas and two dipole receiving antennas. The user head effects are studied at three different frequencies: 700, 950 and 1880 MHz. The investigation is performed via full-wave electromagnetic simulations and lab experiments. Two antenna polarisation scenarios are investigated: vertically co-polarised (V-polarised) dipoles and cross-polarised (X-polarised) dipoles. The channel matrix that includes the antenna system on both sides of the communication link and the user&apos;s head is constructed and characterised. The channel power factor, the sub-channel weights and the channel capacity are all measured and analysed. The investigation showed that the presence of the human head decreases the channel power at all the examined frequencies and this effect becomes more sever as the frequency becomes higher. Also, the user head helped to improve the communication channel state in the V-polarised scenario, whereas it degraded the channel state in the X-polarised scenario. Therefore in the presence of the user, V-polarised antenna configurations would be the better choice if an increase in the channel capacity is the multi-antenna design objective. While X-polarised antenna configurations should be considered if an improvement in the signal quality and hence in the communication link margin is the MIMO design objective.

Multi‐channel communication and consumer choice in the household furniture buying process

PurposeFurniture is among the important personal consumption expenditures for durable goods in the USA. Retailers and manufacturers offer different communication channels to assist consumers all through the process of acquiring furniture. The objective of the present study is to evaluate US consumers' channel use at different steps of the residential furniture‐buying process.Design/methodology/approachA cross‐section study was conducted by taking advantage of the structured nature of quantitative methods using a questionnaire for data collection and a socio‐demographic representative sample of US citizens. Consumers' use of six communication channels through five buying decision steps was assessed.FindingsResults showed that the furniture retail store is the most important communication channel at each of the five considered buying process stages. Overall score of that channel was higher for females than males, indicating that women care more about communication when buying furniture. The internet was not of significant importance when buying furniture. Advertising was perceived as a significant means to gather information.Practical implicationsThe study will help to orient companies' marketing strategies by making proper use of communication channels. It also shows marketing students the present state of consumers' communication channel preferences.Originality/valueThis paper gives unique insights into consumers' buying behavior that will help to design communication channels properly.

Method of Monitoring the State of Communication Channels During Information Transmission

Method of Monitoring the State of Communication Channels During Information Transmission

The State of Communication Channels in Hungary

Serials: The Journal for the Serials Community has been digitised and can be accessed in full on this website. All content is freely available on an open-access basis. Serials was published between 1988 and 2011. In 2012, the journal was retitled and is now published as Insights: the UKSG journal.

Estimation of functional state of intercellular communication channels during rat liver carcinogenesis

Estimation of functional state of intercellular communication channels during rat liver carcinogenesis