Model Of Communication System Research Articles

Beamforming design based on particle swarm algorithm in IRS-assisted communication system

5G and the next generation of wireless technologies are designed to meet the needs of vertical industrial applications like Smart Cities, Industry 4.0, and Smart Grid. These requirements necessitate increased network capacity in order to provide a ubiquitous wireless connection for a large number of devices. Many technologies, such as mm-wave (MMW) communications, massive MIMO systems, and ultra-dense networks (UDN), have been developed in recent years to achieve these goals. However, these technologies have high implementation costs or consume a lot of energy. Intelligent Reflective Surface (IRS) is a low-cost, energy-efficient meta-surface made up of several low-cost passive components. When controlled by the controller, the IRS components can reflect an occurrence signal with a predetermined phase shift for beamforming. This not only improves system performance, but it also addresses the millimeter wave penetration performance issue (MMW). In this paper, a mathematical model of a wireless communication system with IRS in the line-of-sight (LoS) case is developed, as well as a channel model between the base station, the smart reflecting surface, and the user, and a millimeter-wave large-scale fading model. It is investigated the beamforming matrix optimization problem on the base station surface and the intelligent reflecting surface.

Application of visual communication in digital animation advertising design using convolutional neural networks and big data.

In the age of big data, visual communication has emerged as a critical means of engaging with customers. Among multiple modes of visual communication, digital animation advertising is an exceptionally potent tool. Advertisers can create lively and compelling ads by harnessing the power of digital animation technology. This article proposes a multimodal visual communication system (MVCS) model based on multimodal video emotion analysis. This model automatically adjusts video content and playback mode according to users' emotions and interests, achieving more personalized video communication. The MVCS model analyses videos from multiple dimensions, such as vision, sound, and text, by training on a large-scale video dataset. We employ convolutional neural networks to extract the visual features of videos, while the audio and text features are extracted and analyzed for emotions using recurrent neural networks. By integrating feature information, the MVCS model can dynamically adjust the video's playback mode based on users' emotions and interaction behaviours, which increases its playback volume. We conducted a satisfaction survey on 106 digitally corrected ads created using the MVCS method to evaluate our approach's effectiveness. Results showed that 92.6% of users expressed satisfaction with the adjusted ads, indicating the MVCS model's efficacy in enhancing digital ad design effectiveness.

Intelligent-Reflecting-Surface-Assisted GFDM Communication Systems

Demands for higher data rates is an inevitable fact. Therefore, the effect of the frequency selectivity and time variation of communication channels will be more significant. One effective technique to combat the doubly dispersive channels is generalized frequency division multiplexing (GFDM) scheme. Nevertheless, nonorthogonality of the used prototype filter can deteriorate the GFDM performance. On the other hand, intelligent reflecting surface (IRS) is a new revolutionary technology for future wireless networks. IRS can smartly reconfigure the propagation environment in order to improve the efficiency of the communication systems. In this article, IRS is applied in a GFDM communication system to enhance the system's performance. In this way, an IRS-assisted GFDM communication system model is proposed. The goal is to design the reflecting coefficients of the IRS such that the total noise power at a GFDM block is minimized. In order to obtain an effective solution, the optimization problem is converted to a quadratically constrained quadratic program (QCQP). The developed QCQP problem is convex, which can be solved by using an iterative algorithm. An analytical derivation of the maximum achievable rate (MAR) and bit error rate (BER) of the proposed IRS-assisted GFDM system is provided. Also, in simulations, the performance of the proposed method is compared with the conventional GFDM system in terms of MAR and BER. Simulation results confirm that using IRS by employing the proposed method dramatically improves the performance of a GFDM system.

Robust Deep Learning Models for OFDM-Based Image Communication Systems in Intelligent Transportation Systems (ITS) for Smart Cities

Internet of Things (IoT) ecosystem in smart cities demands fast, reliable, and efficient image data transmission to enable real-time Computer Vision (CV) applications. To fulfill these demands, an Orthogonal Frequency Division Multiplexing (OFDM)-based communication system has been widely utilized due to its higher spectral efficiency and data rate. When adapting such a system to achieve fast and reliable image transmission over fading channels, noise is introduced in the signal which heavily distorts the recovered image. This noise independently corrupts pixel values, however, certain intrinsic properties of the image, such as spatial information, may remain intact, which can be extracted as multidimensional features (in the convolution layers) and interpreted (in the top layers) by a Deep Learning (DL) model. Therefore, the current study analyzes the robustness of such DL models utilizing various OFDM-based image communication systems for CV applications in an Intelligent Transportation Systems (ITS) environment. Our analysis has shown that the EfficientNetV2-based model achieved a range of 70–90% accuracy across different OFDM-based image communication systems over the Rayleigh Fading channel. In addition, leveraging different data augmentation techniques further improves accuracy up to 18%.

Path loss modeling and performance evaluation of double IRSs-aided wireless communication systems based on spatial scattering channel model

Intelligent reflecting surface (IRS) is a key enabling technology to reshape the electromagnetic propagation environment and enhance the communication performance. Current single IRS-aided or multiple distributed IRSs-aided wireless communication systems leave inter-IRSs collaboration out of consideration, and as a result, the system performance may be severely restricted. For cooperative double IRSs-aided wireless communication systems, dyadic backscatter channel model is widely used in the performance analysis and optimization. However, the impact of factors such as the size and gain of IRS elements is omitted. As a result, the performance quantification and evaluation are inaccurate. In order to avoid the above limitations, spatial scattering channel model is leveraged to quantify the path loss of the double reflection link in typical application scenarios of double IRSs-aided wireless communication systems. When the near-field condition is satisfied, the electromagnetic wave signal transmitted between IRSs is a spherical wave, which leads to high-rank channel and a lower signal to noise ratio. This paper considers the rank-1 inter-IRSs equivalent channel and derives the closed-form received signal power which reveals its relationship with the deployment of IRSs and the physical and electromagnetic properties of IRSs. Taking the impact of near/far-field effects of IRS on signal propagation further into consideration, the network configurations under which double cooperative IRSs can enhance the system performance are recognized. Simulation results show that whether double IRSs should be selected to assist in the communication between the transmitter and the receiver depends on practical network configurations, and the same number of elements should be assigned to the two IRSs to maximize the system performance if they are adopted.

Waveform-to-Waveform End-to-End Learning Framework in a Seamless Fiber-Terahertz Integrated Communication System

Seamless fiber-terahertz integrated communication has emerged as a promising technology in the special field of 6G, including mobile fronthaul and wireless bridges. Electronic terahertz communication systems have the advantages of high-level integration, small form factors, and potentially low costs, but their drawbacks are their small bandwidths and high harmonic interference levels. In this paper, an end-to-end learning-based waveform-to-waveform automatic equalization framework (W2WAEF) is proposed to overcome the above shortcomings in a seamless fiber-terahertz integrated communication system. An attention-based three-tributary heterogeneous neural network (ATTH) is designed to simulate the channel model of a seamless fiber-wireless communication system, taking fiber optics, optical-to-electrical conversion, and electrogenerated terahertz waves into account. With the proposed method, a data rate of 80.78 Gbps is experimentally demonstrated for discrete multitone modulation (DMT) signals over 5 km of fiber and 1 m of 209-GHz terahertz signals under a 20% soft decision-forward error correction (SD-FEC) threshold of 2.4 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> . Compared with an approach without preprocessing, a receiver sensitivity gain exceeding 1.3 dB is successfully achieved at a data rate of 60 Gbps. The proposed method is a promising scheme for meeting the high speed and low-cost demands of future seamless fiber-terahertz integrated communication systems.

A Novel Beam Domain Channel Model for B5G Massive MIMO Wireless Communication Systems

In this paper, a novel beam domain channel model (BDCM) is proposed for beyond fifth generation (B5G) massive multiple-input multiple-output (MIMO) wireless communication systems. Different from conventional massive MIMO BDCMs which assumed the far-field plane wavefront effect, the proposed BDCM considers more realistic spherical wavefront caused by near-field effect. We transform a massive MIMO geometry-based stochastic model (GBSM) from the antenna domain to the beam domain through specific algorithms to obtain the novel BDCM. The space-time-frequency correlations of both the GBSM and BDCM are studied, and the correlations for both models at the cluster level are similar. We also compare the quasi-stationary distance (QSD), computational complexity, and channel capacity for both models. Results show that in comparison to the GBSM, the novel BDCM has lower complexity and similar accuracy if the number of beams is sufficiently large. Furthermore, we compare the singular value spreads (SVSs) of both channel models with channel measurements under the same conditions. Both the novel BDCM and GBSM are close to the measurement. Through the above analysis, the novel BDCM is proved to be more convenient for information theory and signal processing researches than the conventional GBSMs.

Developing novel channel estimation and hybrid precoding in millimeter-wave communication system using heuristic-based deep learning

The digital or analog precoders are not carried the optimal energy efficiency in the mm-wave massive MIMO and so, every antenna is needed with one radio frequency chain in the system. Based on this view, a cost-efficient technique is developed for hybrid precoding. From this technique, the short dimensional precoding is obtained from the high dimensional beam-formers in the digital domain for steering antenna elements. Therefore, the main intention of this paper is to develop a channel estimation and hybrid pre-coding model for the mm-wave massive MIMO communication system. The channel estimation phase is performed by the Adaptive Deep Convolutional Neural Network (A-Deep CNN), which covers both channel estimation and channel reconstruction. The introduction of a hybrid meta-heuristic algorithm with Forest-Tunicate Swarm Algorithm (F-TSA) is used for enhancing A-Deep CNN that is adaptable for efficient channel estimation. Once the channel estimation is done, the deployment of Optimized Recurrent Neural Networks (O-RNN) is used for hybrid precoding. Simulation results demonstrate that the proposed A-Deep CNN-based channel estimation scheme outperforms the existing schemes in terms of the Normalized Mean-Squared Error (NMSE) and spectral efficiency, while the O-RNN-hybrid precoder design method has better spectral efficiency performance than other methods.

Performance analysis of a dual-hop parallel relayed mixed FSO-UWOC system

Abstract In this paper, we propose a dual-hop parallel relayed mixed free space optical-underwater optical communication (FSO-UWOC) system model to setup a high-speed communication link connecting a distant sender located at the lighthouse tower and the receiver inside the deep ocean to transmit and receive the data for the applications such as deep sea exploration, marine life study etc. The proposed model is the setup of multiple parallel cooperative dual hop communication link for the betterment of the overall system performance under the worst case scenarios. The receiver examines the end-to-end signal to noise ratio and a relay is selected which has the maximum SNR. The selected relay then decodes the incoming optical signal through FSO link into its corresponding visible light signal and further transmits it to the underwater exploration devices such as underwater drone via VLC link. The FSO link is modelled by the Gamma-Gamma fading distribution and the VLC link is modelled by Exponential Generalized Gamma distribution. The end-to-end SNR of the system is utilized to derived the outage probability of the FSO-UWOC system and the impact of the individual link parameters is studied and presented for the proposed system model.

AWOA: An Advanced Whale Optimization Algorithm for Signal Detection in Underwater Magnetic Induction Multi-Input–Multi-Output Systems

With the increasing exploitation and use of marine resources, the limitations of acoustic, optical, and radio frequency technologies for underwater communications have become increasingly apparent. Magnetic induction (MI) is a new communication technology that enables wireless data transmission via magnetic field coupling between transmitting and receiving coils. MI offers advantages such as channel stability, small antenna size, and no multi-path loss. Multi-input–multi-output (MIMO) is a multi-antenna technology that significantly increases system capacity and spectrum utilization without increasing bandwidth. The whale optimization algorithm (WOA) is a well-known bio-inspired algorithm that mimics the hunting behavior of whales to optimize swarm intelligence. This paper proposes a model for an underwater MIMO communication system based on magnetic induction. We then construct a signal detection algorithm for MI-MIMO systems using the advanced whale optimization algorithm (AWOA) and conduct simulation experiments to compare the performance and complexity of three standard signal detection algorithms: zero-forcing (ZF), minimum mean square error (MMSE), and maximum likelihood (ML). The experimental results show that AWOA achieves suboptimal results, as its bit error rate (BER) is close to that of the ML algorithm. Furthermore, the complexity of AWOA is comparable to that of the MMSE strategy. This work supports the development of a high-performance MI-based underwater communication system.

Enabling OTFS-TSMA for Smart Railways mMTC Over LEO Satellite: A Differential Doppler Shift Perspective

Recently, grant-free orthogonal time–frequency space-based tandem spreading multiple access (OTFS-TSMA) is proposed for machine-type communications (mMTCs) in smart railways environmental sensing. To achieve massive connections with scarce radio resources, OTFS-TSMA combines the advantages of OTFS and TSMA. It shows high connectivity and reliability under time–frequency-selective fading channels. Meanwhile, smart railways require over-horizon and all-weather environmental sensing based on mMTC, and the implementation of both would cost a lot in terrestrial networks. With the development of low-Earth orbit (LEO) satellites, enabling smart railways mMTC over LEO satellite is a potential diagram. However, in this scenario, due to the larger transmission delay and Doppler frequency shift, the time–frequency resource requirements of the OTFS modulation-based system increase significantly and are difficult to meet. To this end, OTFS-TSMA based on differential Doppler shift is proposed in this article. Specifically, in this article, the satellite-to-ground communication system model consisting of three sections is introduced, and the Doppler shift and differential Doppler shift characteristics of access points (APs) are investigated. Next, it is proven that designing OTFS-based multiple access schemes over the LEO satellite based on differential Doppler shift is not only resource-friendly but also has the advantages of service continuity and controllable multiuser interference. Then, the transceiver of differential-Doppler-shift-based OTFS-TSMA and its improved designs are proposed. Finally, the simulation results demonstrate that the proposed transceiver realizes high resource efficiency, collision resolution capability, and reliability for smart railways mMTC over the LEO satellite.

A Novel 3D Beam Domain Channel Model for Massive MIMO Communication Systems

Massive multiple-input multiple-output (MIMO) channels are distinctly characterized by their array non-stationarity, which has not been considered in the existing beam domain channel models (BDCMs). In this paper, the array non-stationarity of massive MIMO channels is modeled by the spatially consistent visibility regions (VRs) over a large uniform planar array (UPA) in terms of individual multipath components (MPCs). Based on this, a novel three-dimensional (3D) BDCM incorporating the effects of array non-stationarity is proposed. Statistical properties of the proposed BDCM including channel power, power leakage, space-time-frequency correlation function (STF-CF), and beam spread are derived. The ergodic and outage capacities are evaluated. The impacts of array non-stationarity on those statistics and channel capacity are analyzed. Results suggest that the beamwidths or spatial resolutions of the BDCM for different directions are not equal due to the array non-stationarity. This in turn increases the power leakage and correlation between channel elements and reduces the beam domain channel capacity.

Resource Allocation for D2D Communication Underlaying Cellular Networks: A Distance-Based Grouping Strategy

Device-to-device (D2D) communication with direct terminal connection is a promising candidate for 5G communication, which increases the capacity of cellular networks and spectral efficiency. Introducing D2D communication to cellular users (CUs) will increase system capacity, and CUs will provide reusable channel resources for D2D users (DUs). However, the sharing of channel resources between CUs and DUs will lead to cofrequency interference and affect the communication quality of user terminals. As a means of improving spectrum utilization and solving cofrequency interference problems, a one-to-many D2D communication system model is established in cellular networks. Through model analysis, the interference between CUs and DUs is correlated with their distance from one another. Considering the different interference of CUs to DUs at different distances, an algorithm for resource allocation based on distance grouping is proposed. With this algorithm, DUs will reuse channel resources of CUs within a reasonable distance in the group, and interference between DUs and CUs will be minimized. The improved particle swarm optimization algorithm is used to solve the optimal power, to achieve the maximum transmission rate of the system. Simulated results show that the algorithm will significantly improve system throughput and performance while also lowering the computational complexity of the algorithm, enabling the whole system to have better communication quality.

A 4-Term Exponential-Quadratic Approximation for Gaussian Q or Error Functions Accurate to $1.65\times 10^{-4}$

Integrals on [0, ∞) where the integrand is of the form Qn(a√x) p(x), where Q is the Gaussian Q function, p(·) a Gamma PDF, n a positive integer and a &gt; 0; or of the form erfn(ax + b) xr exp(-c2x2 + 2dx), where erf(x) is the error function, with integers r ≥ 0, n &gt; 0, arise in performance modelling of communication and machine learning systems. Such integrals cannot be evaluated analytically in general, but they are reducible to a set of key integrals whose integrand is erfn(ax + b) N(x; m, s) where N() is a Gaussian PDF with mean m and variance s. Seeking an efficient and accurate evaluation method, we develop a new 4-term exponential quadratic approximator (EQA) for the error function that includes both linear and quadratic terms in its exponents. The EQA minimises a sum-of-squares cost function with two “spline-type” constraints, i.e., constraints on the function value and its first derivative. This constrained optimisation problem is reduced to an unconstrained one by inverting a 4-D linear system, then solved by gradient descent. The resulting approximator has a maximum absolute error of 1.65 × 10-4 on the real line, and outperforms many other exponential sum approximators for erf(x) on x ∈ [0, 1.5] and for Q(x) on x ∈ [0, 2]. Moreover, due to its functional form, the EQA leads to an analytical formula for the set of key integrals, which, in the n = 1 case, is accurate to 3 to 4 significant figures while being orders of magnitude more efficient than Monte Carlo integration. The EQA can equally be used to obtain closed forms for the average symbol error probability of various modulation schemes on Rayleigh fading channels.

Achievable Rate of Near-Field Communications Based on Physically Consistent Models

This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-input-single-output (SISO) near-field communications (NFC). By incorporating the finite antenna size constraint using Chu’s theory and under the assumption of canonical-minimum scattering (CMS), we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we first establish its input-output relationship where the noise depends on the self/mutual impedances of the antennas, unlike the conventional assumption of independent additive white Gaussian noise. We then characterise the achievable data rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.

Nonlinear bridge deflection monitoring and prediction system based on network communication

Abstract In order to study the bridge deflection monitoring and prediction system based on network communication, first, the development status of the bridge deflection monitoring system, overall demand of the system, hardware composition of the system, realization of the system software, and the timely processing and analysis of monitoring data are discussed. Then, the dynamic prediction of the change trend of the external load and the decay information of the structural resistance contained in the deflection monitoring data is carried out, and the prediction function of the external effect of the structure is established at the same time. Finally, a rapid monitoring system specially designed to deal with accidental bridge disasters was developed, and it was installed and applied in the engineering experiment of Chongqing Gaojia Garden Bridge. The deflection data analysis of Gaojia Huayuan Bridge was carried out, and suggestions for the operation and maintenance of the bridge were put forward, and the safety status assessment of the bridge was realized. The results show that the system has strong practicability, real-time monitoring and accuracy. It provides a convenient and accurate way for bridge managers to supervise bridges and formulate specific bridge maintenance plans in a timely manner. During the radio frequency modulation of communication data, harmonic oscillations occur due to the nonlinear characteristics of oscillating data, so it is difficult to improve the wireless ability to modulate and demodulate transmitted data in communications. The traditional method uses neural network fuzzy control distribution estimation harmonic balance algorithm and nonlinear rolling. The performance of dynamic predictive control is poor in quality, harmonic balance, and stability control. An improved harmonic-based communication network is proposed to balance the stability control model of nonlinear communication system, construct nonlinear communication system model, and extract signal and channel characteristics of the communication system. The channel model is designed and the communication network control method is adopted to improve the control algorithm. The simulation results show that the proposed algorithm can be used to improve the stability of nonlinear communication system, reduce the bit error rate, overcome the interference of coherent component in sidelobe, and autocorrelated the impulse response of receiver. The stability of the cumulative output is good, which can overcome the communication error caused by the harmonic oscillation due to the nonlinear characteristics of the oscillating data, and improve the communication quality.

A Contention-Free Cooperative MAC Protocol for Eliminating Heterogenous Collisions in Vehicular Ad Hoc Networks

In vehicular ad hoc networks (VANETs), efficient data dissemination to a specified number of vehicles with minimum collisions and limited access delay is critical for accident prevention in road safety. However, packet collisions have a significant impact on access delay, and they may lead to unanticipated link failure when a range of diversified collisions are combined due to complex traffic conditions and rapid changes in network topology. In this paper, we propose a distributed contention-free cooperative medium access control (CFC-MAC) protocol to reduce heterogenous collisions and unintended access delay in stochastic traffic scenarios. Firstly, we develop a cooperative communication system model and cooperative forwarding mechanism to explore the optimum road path between the source and destination by identifying the potential cooperative vehicles. Secondly, we propose a vectorized trajectory estimation mechanism to suppress merging collisions by identifying the relative velocity of vehicles with different speeds in a specific time interval. Based on the case study, typical heterogeneous collisions and aggregated heterogeneous collisions at dissociated positions and associated positions are investigated. In both cases, we propose the corresponding collision-resolving mechanisms by methodically recapturing the colliding time slot or acquiring the available free time slots after identifying the access vehicles and comparing the received signal strengths. Performance analysis for collision probability and access delay is conducted. Finally, the simulation results show that the proposed protocol can achieve deterministic access delay and a minimal collision rate, substantially outperforming the existing solutions.

Research on Multi-Agent D2D Communication Resource Allocation Algorithm Based on A2C

Device to device (D2D) communication technology is the main component of future communication, which greatly improves the utilization of spectrum resources. However, in the D2D subscriber multiplex communication network, the interference between communication links is serious and the system performance is degraded. Traditional resource allocation schemes need a lot of channel information when dealing with interference problems in the system, and have the problems of weak dynamic resource allocation capability and low system throughput. Aiming at this challenge, this paper proposes a multi-agent D2D communication resource allocation algorithm based on Advantage Actor Critic (A2C). First, a multi-D2D cellular communication system model based on A2C Critic is established, then the parameters of the actor network and the critic network in the system are updated, and finally the resource allocation scheme of D2D users is dynamically and adaptively output. The simulation results show that compared with DQN (deep Q-network) and MAAC (multi-agent actor–critic), the average throughput of the system is improved by 26% and 12.5%, respectively.

Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

Unmanned aerial vehicles (UAVs) are increasingly used in various applications, including infrastructure inspection, traffic monitoring, remote sensing, mapping, and rescue. However, many applications have required UAVs to function autonomously, without human intervention to improve system performance. In this study, we propose a new approach to environmental monitoring using a group of UAVs equipped with sensors under the support of reinforcement learning. Regarding the communication system model, we assume that UAVs can cooperate with each other to learn and share information about the environment, and then relocate to an optimal position while managing connectivity and coverage. After that, we exploit reinforcement learning with a deep deterministic policy gradient (DDPG) algorithm to optimize environmental monitoring with the proposed algorithm. Specifically, the proposed algorithm aims to simulate an environmental monitoring system using UAVs with basic parameters. We further apply the proposed algorithm to evaluate network performance under different parameter settings. Numerical results validate the effectiveness of the proposed learning-based framework in monitoring and sensing data.

Matematičko modelovanje i simulacija primopredajnika pravougaonih impulsa za rad u diskretnom vremenu

Introduction/purpose: The paper presents the theory and design issues of a discrete-time communication system used for discrete-time pulse transmission with and without filtering. Signals are analyzed in both the time domain and the frequency domain. Methods: The system is theoretically analyzed using block schematics expressed in terms of mathematic operators and the system simulation is performed to confirm the theoretical findings. Results: Discrete-time signals are presented in the time domain and the frequency domain as well as confirmed by a simulation designed in Matlab. Conclusion: The results of the paper contribute to the theoretical modeling and design of modern discrete communication systems.