Simple Channel Model Research Articles

Quantum communications for image transmission over error‐prone channels

AbstractThe introduction of quantum communications, enabled by advancements in quantum computing, is expected to play a significant role in the field of communications. Inherent properties of quantum objects, such as superposition and entanglement, have the potential to provide novel solutions to overcome the challenges encountered by classical communication systems in bandwidth‐intensive applications such as media transmission. This research explores the performance of a quantum communication system in image transmission using quantum superposition and investigates its performance using a simple quantum channel model. With an increase in channel noise, there are significant gains in the rate distortion performance of images transmitted over the quantum channel compared to an ideal classical channel. This novel attempt at constructing a quantum communication‐based image transmission system indicates the potential of the approach to be applied to satisfy the ever‐increasing demands of high‐quality media transmission applications.

Analisis Saluran Distribusi Rantai Pasokan Padi di Desa Mruwak Dagangan Kabupaten Madiun

Mruwak is one of the villages in Madiun Regency where the majority of residents work as farmers. The food commodity most widely grown in the village is rice, while the rest are corn, sweet potatoes and peanuts. With the existence of a supply chain system, it is hoped that it can help farmers determine rice marketing routes effectively and efficiently. The aim of this research is to determine the supply chain system for rice produced by farmers in Mruwak Village. This research is qualitative in nature and the sample was selected purposively and using snowball sampling. The research results show the rice process, starting from land processing, rice planting, to maintenance and harvest. The farmers in the village have experience in growing rice, on average they have been growing rice for more than 10 years so they understand how to grow rice properly, including selecting superior seeds. The supply chain distribution channel used is a simple distribution channel model (1 to 3 level distribution channel model). Determination of selling prices based on agreements between farmers and buyers through demand and supply mechanisms (market mechanisms).

Simplified channel model for indoor VLC systems.

The channel model of indoor visible light communication (VLC) is primarily influenced by the receiver's position and orientation. In this study, we decouple the effects of receiver position and orientation, treating them as independent random variables. This decoupling enables a clearer understanding of how each factor independently influences the channel model. Specifically, we focus on the impact of the azimuth angle, a critical component of orientation, on the VLC channel. We present a simplified channel model for indoor VLC systems based on partial orientation information. Theoretical analysis and simulation results collectively indicate that the accuracy of the simplified channel model is influenced by the field-of-view (FOV), with higher accuracy achieved at narrower FOVs. To evaluate practical performance, we compare our simplified model with the existing complete channel model in a two-user precoding system. Results show that our proposed simplified channel model maintains value, particularly in narrow-FOV scenarios. The research findings indicate that this simplified model is suitable for applications with high demands for channel accuracy in narrow-FOV indoor VLC systems, and can be extended to applications in wide-FOV systems where perfect channel information is not required.

A pressure-projection pre-conditioning multi-fractional-step method for Navier–Stokes Flow in Porous Media

A new pressure-projection pre-conditioning multi-fractional-step (PPP-MFS) method for incompressible Navier–Stokes flow in porous media is presented. This fractional step method is applied to decouple the pressure and the velocity; thereby, overcoming the computational costs and difficulty associated with the resolution of the nonlinear term in the Navier–Stokes equation for fine geometric models. Specifically, time evolution is decomposed into a sequence of multi-fractional solution steps. In the first step, an elliptic problem is solved for pressure (p) with a no-slip boundary condition. This gives the Stokes pressure and velocity fields. In the second step, the obtained pressure p is then projected onto the field p∗ and used to solve for velocity field (u) required for the pre-conditioning of the solution to the Navier–Stokes equation. The pressure and velocity fields are obtained from the solution of the Navier–Stokes equation in the third step. Numerical and geometric discretisation of porous samples were carried out using finite-element method. For flow in simple channel models represented by two- and three-dimensions and in systems with high conductivity, the Stokes and Navier–Stokes numerical solutions produced close pressure and velocity field approximations. For flow around a cylinder, computation time is consistently higher in the Navier–Stokes equation by a factor of 2 with a pronounced non-symmetric pressure field at high mesh refinements. This computation time is desirable given that Navier–Stokes computation without preconditioning can be orders of magnitude more expensive.

Equalizer Parameters’ Adjustment Based on an Oversampled Channel Model for OFDM Modulation Systems

A physical model of a wireless transmission channel in the time domain usually consists of the main propagation path and only a few reflections. The reasonable assumptions made about the channel model can improve its parameters’ estimation by a greedy OFDM (Orthogonal Frequency Division Multiplexing) equalizer. The equalizer works flawlessly if delays between propagation paths are in the sampling grid. Otherwise, the channel impulse response loses its compressible characteristic and the number of coefficients to find increases. It is possible to get back to the simple channel model by data oversampling. The paper describes how the above idea helps the OMP (Orthogonal Matching Pursuit) algorithm estimate channel coefficients. The authors analyze the oversampling algorithm on the one hand to assess the influence of filtering function and signal resolution on the quality of the channel impulse response reconstruction. On the other hand, the abilities of the OMP algorithm are analyzed to distinguish components of the oversampled signal. Based on these analyses, we proposed modifications to the compressible channel’s impulse response reconstruction algorithm to minimize the number of transmission errors. A distinction was made between the filters used in the OMP search and channel reconstruction stages before calculating equalizer coefficients. Additionally, the results of the search stage were considered as elements within the groups.

Channel characterization and modeling for wireless communication link planning and design in the tropics

<p>Channel model provides basis in the link budgetary for planning and design of low margin systems of communication links. This paper proposed a simple and efficient channel model for wireless channel path loss characterization and modeling. Free space channel was characterized and modelled to account for both static and dynamic changes of signal that propagates through it. Correction factors in the model account for the losses due to refraction effects, as a result of abnormal refractivity condition frequent in tropical regions. The model was validated via measurement data from experiment conducted in a tropical region and a high level of agreement was observed. The proposed model can be deployed during link planning and budgetary for ultra high frequency (UHF) wireless radio services.</p>

Modeling the Geometry of an Underwater Channel for Acoustic Communication

The achievement of efficient data transmissions via underwater acoustic channels, while dealing with large data packets and real-time data fed by underwater sensors, requires a~high data rate. However, diffraction, refraction, and reflection phenomena, as well as phase and amplitude variations, are common problems experienced in underwater acoustic (UWA) channels. These factors make it difficult to achieve high-speed and long-range underwater acoustic communications. Due to multipath interference caused by surface and ocean floor reflections, the process of modeling acoustic channels under the water's surface is of key importance. This work proposes a~simple geometry-based channel model for underwater communication. The impact that varying numbers of reflections, low water depth values, and distances between the transmitter and the receiver exert on channel impulse response and transmission loss is examined. The high degree of similarity between numerical simulations and actual results demonstrates that the proposed model is suitable for describing shallow underwater acoustic communication environments.

Electric arc I–V modeling and related plasma spectrometry issues

This article is devoted to studying the properties of an electric arc column as a development of the authors’ early pioneer experiments. The object of modeling is a free-burning electric arc between evaporating copper electrodes in atmospheric air as the basis for the functioning of many modern technologies. It includes the determination of fundamental characteristics, such as the radial structure, and the current–voltage characteristics of the electric arc column under the assumption of plasma equilibrium. The middle cross section between the electrodes of a spheroidal shape arc is considered in order to limit the problem to a one-dimensional cylindrical approximation. It is strictly solved from the Elenbaas–Heller energy equation, with no resorting to the simplified channel model. The radial structure of the electric arc column is carefully considered with known temperature functions of electric and heat conductivities. Convenient functional approximations depending on temperature are proposed for the mentioned coefficients of copper–air plasma. The boundary between the arc column and atmospheric air is strictly located taking into account the chemical processes in the plasma of the copper–air mixture. The paper also presents some bases features of high-speed plasma spectrometry, substantiating the reliability of the obtained experimental data. As shown, the non-monotonicity of the current–voltage characteristics can arise due to the non-monotonicity of thermal conductivity as a function of temperature. Also, the loss of energy with the so-called ionization energy diffusion is insignificant in the overall energy balance of the arc. The results of the numerical simulation are compared with the experimental data.

Pervasive Wireless Channel Modeling Theory and Applications to 6G GBSMs for All Frequency Bands and All Scenarios

In this paper, a pervasive wireless channel modeling theory is first proposed, which uses a unified channel modeling method and a unified equation of channel impulse response (CIR), and can integrate important channel characteristics at different frequency bands and scenarios. Then, we apply the proposed theory to a three dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) for the sixth generation (6G) wireless communication systems. The proposed 6G pervasive channel model (6GPCM) can characterize statistical properties of channels at all frequency bands from sub-6 GHz to visible light communication (VLC) bands and all scenarios such as unmanned aerial vehicle (UAV), maritime, (ultra-)massive multiple-input multiple-output (MIMO), reconfigurable intelligent surface (RIS), and industry Internet of things (IIoT) scenarios. By adjusting channel model parameters, the 6GPCM can be reduced to various simplified channel models for specific frequency bands and scenarios. Also, it includes standard fifth generation (5G) channel models as special cases. In addition, key statistical properties of the proposed 6GPCM are derived, simulated, and verified by various channel measurement results, which clearly demonstrates its accuracy, pervasiveness, and applicability.

Influence of wall roughness on two-phase displacement

In the development of oil and gas fields, water flooding is still the most important development method. The influence of the existence of rough channel on two-phase flow cannot be ignored. To study the flow of oil-water two-phase in rough channel, the method of solving Navier-Stokes (N-S) equation directly is used to simulate the displacement process of two-phase fluid in a single rough channel, and the phase field method is used to trace the interface of two-phase fluid in real time. The effects of a series of factors, such as relative roughness height, horizontal roughness unit clearance ratio, viscosity ratio, and so on, on the displacement velocity and the length of two-phase interface are studied by establishing a simple and regular rough channel model. By establishing a smooth channel model as a control experiment, the overall effect of wall roughness on two-phase displacement is studied. Finally, the complex rough channel model is established to verify the correctness of the law obtained by the simple model. The results show that the existence of wall roughness will greatly increase the two-phase flow resistance, promote the occurrence of fingering problem, and reduce the displacement efficiency.

A Statistical MIMO Channel Model for Reconfigurable Intelligent Surface Assisted Wireless Communications

Reconfigurable intelligent surface (RIS) consisting of a large number of programmable near-passive units has been a hot topic in wireless communications due to its capability in providing smart radio environments to enhance the communication performance. However, the existing research are mainly based on simplistic channel models, which will, in principle, lead to inaccurate analysis of the system performance. In this paper, we propose a general three-dimensional (3D) wideband non-stationary end-to-end channel model for RIS assisted multiple-input multiple-output (MIMO) communications, which takes into account the physical properties of RIS, such as unit numbers, unit sizes, array orientations and array configurations. By modeling the RIS by a virtual cluster, we describe the end-to-end channel by a superposition of virtual line-of-sight (V-LoS), single-bounced non-LoS (SB-NLoS), and double-bounced NLoS (DB-NLoS) components. We also derive an equivalent cascaded channel model and show the equivalence between end-to-end and cascaded modeling of RIS channels. Then, a sub-optimal solution with low complexity is used to derive the RIS reflection phases. The impact of physical properties of RIS, such as unit numbers, unit sizes, array orientations, array configurations and array relative locations, on channel statistical characteristics has been investigated and analyzed, the results demonstrate that the proposed model is helpful for characterizing the RIS-assisted communication channels.

Unfolded Hybrid Beamforming With GAN Compressed Ultra-Low Feedback Overhead

Optimizing a hybrid beamforming transmitter is a non-convex problem and requires channel state information, leading in most cases to nontrivial feedback overhead. We propose a methodology relying on the principles of deep generative models and unfolding to achieve near-optimal hybrid beamforming with reduced feedback and computational complexity. We first represent the channel as a low-dimensional manifold via a generative adversarial network (GAN) and search the optimum digital and analog precoders in this low-dimensional space. To decrease the search complexity, we find an iteration rule by formulating hybrid beamforming as a bi-level optimization problem and then unfold each iteration as a neural layer. This results in a novel model-based deep neural network that incorporates domain knowledge. Our results show that this method (i) approaches the capacity-achieving spectral efficiency, (ii) provides a superior energy and spectral efficiency tradeoff, (iii) decreases feedback overhead, and (iv) reduces the complexity significantly, by optimizing a single low-dimensional vector per channel coherence time, with the neural network itself trained offline. The achieved spectral efficiency is robust when tested with realistic 3GPP channel models, even if the offline training relies on a simple geometric channel model.

A General 3D Space-Time-Frequency Non-Stationary THz Channel Model for 6G Ultra-Massive MIMO Wireless Communication Systems

In this paper, a novel three-dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) is proposed for the sixth generation (6G) terahertz (THz) wireless communication systems. The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users. Also, the generality of the proposed channel model is demonstrated by the fact that it can easily be reduced to different simplified channel models to fit specific scenarios by properly adjusting model parameters. The proposed general channel model takes into consideration the non-stationarities in space, time, and frequency domains caused by ultra-massive MIMO, long traveling paths, and large bandwidths of THz communications, respectively. Statistical properties of the proposed general THz channel model are investigated. The accuracy and generality of the proposed channel model are verified by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding channel measurements.

Non-coherent massive MIMO-OFDM for communications in high mobility scenarios

Under scenarios of high mobility, the traditional coherent demodulation schemes (CDS) have a limited performance, due to the fact that reference signals cannot effectively track the variations of the channel with an affordable overhead. As an alternative solution, non coherent demodulation schemes (NCDS) based on differential modulation have been proposed. Even in the absence of reference signals, they are capable of outperforming the CDS with a reduced complexity. The literature on NCDS laid the theoretical foundations for simplified channel and signal models, often single carrier and spatially uncorrelated flat fading channels. In this work, the most recent results assuming orthogonal frequency division multiplexing (OFDM) signaling and realistic channel models are explained, and the impact of some hardware impairments such as the phase noise (PN) and the non linear high power amplifier (HPA) are also considered. Moreover, new potential research lines are also highlighted.

Performance Analysis and Cell Association Design for Drone-Assisted Heterogeneous Networks

Equipped with base station hardware, unmanned aerial vehicles (UAVs) can act as drone small cells (DSCs) to complement the traditional terrestrial small cells (TSCs) in providing ubiquitous connectivity for a wide range of applications. Although the performance of drone-assisted heterogeneous networks has been investigated, the state-of-the-art adopts simplified channel models that are not applicable to practical network scenarios. To make an accurate analysis on the network performance, and provide guidelines for the practical design of cell association and network deployment strategy, in this work, we consider a drone-assisted heterogeneous network that employs a flexible biased cell association policy and adopts a more general channel model that incorporates line-of-sight (LoS) and non-line-of-sight (NLoS) components for both air-to-ground (ATG) and ground-to-ground (GTG) transmissions. We develop a unifying analytical framework to evaluate the network performance in terms of coverage probability, area spectral efficiency (ASE) and user throughput. Our results show that 1) deploying DSCs to complement traditional TSCs can only deteriorate the network performance within the ultra-dense network region, 2) by optimally setting the bias factor for cell association policy, the network performance can be further enhanced.

An Underwater Acoustic Channel Modeling for Internet of Things Networks

Interest in wideband and medium distance underwater acoustic communication systems is increasing due to the practical application of underwater Internet of Things (IoT) networks. In this paper, we propose a simplified empirical channel model for the medium distance underwater acoustic channels based on real measurement results. The simple and tractable channel model is critical for the development of advanced underwater communications technology. The underwater channel measurements were performed at 20 m sea water depth, the transmitters (TXs) were located at 5 m and 15 m from the bottom, the receivers (RXs) were located at 4 m, 8 m, 12 m, and 16 m from the bottom, and the TX–RX distances were 21 m, 71 m, 127 m, and 273 m. We derived the path loss from the measured dataset, and modified the log-distance model to create a model suitable for an underwater IoT channel. Numerical results show that the proposed model is accurate and reliable enough to use in the development of advanced underwater communication technologies.

Adaptive Neural Signal Detection for Massive MIMO

Traditional symbol detection algorithms either perform poorly or are impractical to implement for Massive Multiple-Input Multiple-Output (MIMO) systems. Recently, several learning-based approaches have achieved promising results on simple channel models (e.g., i.i.d. Gaussian channel coefficients), but as we show, their performance degrades on real-world channels with spatial correlation. We propose MMNet, a deep learning MIMO detection scheme that significantly outperforms existing approaches on realistic channels with the same or lower computational complexity. MMNet's design builds on the theory of iterative soft-thresholding algorithms, and uses a novel training algorithm that leverages temporal and spectral correlation in real channels to accelerate training. These innovations make it practical to train MMNet online for every realization of the channel. On i.i.d. Gaussian channels, MMNet requires two orders of magnitude fewer operations than existing deep learning schemes but achieves near-optimal performance. On spatiallycorrelated channels, it achieves the same error rate as the next-best learning scheme (OAMPNet) at 2.5dB lower signalto-noise ratio (SNR), and with at least 10× less computational complexity. MMNet is also 4-8dB better overall than a classic linear scheme like the minimum mean square error (MMSE) detector.

Analytical and numerical study of wave interaction with a vertical slotted barrier

ABSTRACT In this study, the hydrodynamic performance of a partially submerged vertical slotted barrier was investigated on the basis of an analytical and numerical approach in regular waves. The matched eigenfunction expansion method (MEEM) was used to solve the boundary value problem including the porous boundary condition. Herein the drag coefficient representing the energy dissipation was estimated by the best possible fitting curve to CFD results for a 2D simplified channel model. A 3D implicit unsteady turbulent model based on incompressible Reynolds-averaged Navier–Stokes (RANS) equations was adopted to assess the analytical MEEM solutions. The analytical and numerical solutions show relatively good agreement with the experimental results. The presented analytical and numerical model provides a complementary approach to estimate the hydrodynamic performance and horizontal forces of the vertical slotted barrier effectively and practically.

Sparse Channel Estimation for Spatial Non-Stationary Massive MIMO Channels

In massive MIMO systems, the channel shows a spatial non-stationarity that has a significant impact on the design of sparse channel estimation scheme based on compressive sensing, due to the large size of antenna array. In this letter, the block matching pursuit (BMP)-based two-stage sparse channel estimation scheme is proposed for spatial non-stationary (SNS) channels. In this scheme, the BMP is introduced to search for the support blocks and the threshold-based denoising method is utilized to refine the supports of non-zero elements. In order to evaluate our scheme, we provide a simple SNS channel model modified from the 3GPP 3D channel model to capture the delay-domain special sparse characteristic. Simulation results demonstrate that the proposed scheme can recover channels effectively and achieve better performance than comparison methods.

Stacked Denoising Autoencoder Enhanced Polar Codes Over Rayleigh Fading Channels

Polar codes, with low encoding/decoding complexity and capacity-achieving potential, have drawn much attention recently. It is very critical to study the impact of fading on polar codes implementation into wireless communications. Existing research works on polar codes over the fading channel use simplified fading channel models while further reducing the frame error rate is highly needed. In this letter, using the structure of polar codes we propose the deep learning based scheme for polar codes over Rayleigh fading channels. Simulation results verify that our proposed scheme can significantly decrease the frame error rate for polar codes over Rayleigh fading channels.