Mean Square Error Research Articles

Energy-Efficient Resource Optimization for IRS-Assisted VLC-Enabled Offshore Communication System

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

Compressive strength prediction of Portland cement clinker using machine learning by XRD data

Accurately predicting the 28-day compressive strength of Portland cement clinkers is challenging due to their complex multiphase nature, especially when production processes and environmental conditions vary. To address this issue, the TransXRD model was introduced, which is a novel deep learning approach that combines the Transformer architecture with CNN design. 214 clinker samples were collected from 6 cement plants. The diffraction peak sequence of samples ranging from 10° to 55° was used as required input for the model. A combination of one-dimensional convolution is incorporated to extract local features, and self-attention mechanisms are leveraged to perform global feature integration. XRD data were directly utilized, bypassing additional processing to minimize signal loss. Compared to other DNN (deep neural network) models, the TransXRD model demonstrates promising performance, achieving a root mean square error (RMSE) of 1.15, a mean squared error (MSE) of 1.33, a mean absolute error (MAE) of 0.93, and a mean absolute percentage error (MAPE) of 1.76 on the test set. Notably, in the training set, 100% of the data exhibit prediction errors within 2 MPa, while within the test set, 90% of the prediction errors are within 2 MPa. The application potential of XRD data in predicting the compressive strength of cement clinker also offers novel insights into predicting the performance of other materials.

Predicting Bus Travel Time in Cheonan City Through Deep Learning Utilizing Digital Tachograph Data

Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

Optimization of process parameters of selective laser melted nickel-based superalloy for densification by random forest regression algorithm and response surface methodology

In this study, Random Forest Regression (RFR) and Response Surface Methodology (RSM) were employed to predict the optimized processing parameters to achieve the highest densification of a nickel-based superalloy Mar-M247LC fabricated by selective laser melting (SLM). The RFR model considered input processing parameters such as laser power, hatch distance, and scanning speed. A dataset of 223 samples, was used to train the RFR model. As a result, the RFR model exhibited accuracy of 99.57 %, R2 value of 0.976, Mean Square Error (MSE) of 0.402, and Mean Absolute Percentage Error (MAPE) of 0.426 % on testing set. In addition to the RFR model, this study also employed Central Composite Design (CCD) and RSM to optimize the processing parameter sets. Subsequently, this study conducted Box-Behnken Design (BBD) to experimentally validate the accuracy of this RFR model. In the end, a set of the optimal processing parameters was tested and resulted a sample densification of 99.959 %, outperformed that in the original database before building the RFR model, which was 99.734 %. In summary, the RFR models was able to predict densification with accuracy, and by coupling with RSM, the optimal processing parameter could be obtained, so better densification of the build could be achieved.

Unveiling a Novel Solvatomorphism of Anti-inflammatory Flufenamic Acid: X-ray Structure, Quantum Chemical, and In Silico Studies.

This paper delves into the polymorphism of 2-[3-(trifluoromethyl)anilino]benzoic acid, commonly referred to as flufenamic acid (FA), a pharmaceutical agent employed in treating inflammatory conditions. The central focus of the study is on a newly unearthed solvatomorphic structure of FA in methanol (FAM), and a thorough comparison is conducted with the commercially available standard structure. Employing a comprehensive approach, including X-ray crystallography, Hirshfeld surface analysis, density functional theory (DFT), molecular docking, and molecular dynamics (MD) simulations, the research aims to unravel the structural and functional implications of solvatomorphism. The X-ray crystal structure analysis brings to light notable differences between the standard FA and solvatomorphic FAM, showcasing variations in intermolecular interactions and crystal packing. Key features such as hydrogen bonding, π···π stacking, and C-H···π interactions are identified as influential factors shaping the stability and conformation of the compounds. Hirshfeld surface analysis further quantifies the nature and contribution of intermolecular interactions, providing a comprehensive perspective on molecular stability. Density functional theory offers valuable electronic structure insights, highlighting disparities in frontier molecular orbitals between FA and FAM. Molecular docking studies against prostaglandin D2 11-ketoreductase explore potential drug interactions, unveiling distinct binding modes and hydrogen bonding patterns that shed light on how the solvatomorphic structure may impact drug-target interactions. In-depth molecular dynamics simulations over 100 ns investigate the stability of the protein-ligand complex, with root mean square deviation and root mean square fluctuation analyses revealing minimal deviations and affirming the stability of FAM within the active site of the target protein.

Watermarking algorithm based on phase-only CGH in fractional Hartley domain for DICOM images

In this manuscript, we proposed a watermarking algorithm based on phase-only computer-generated holography (CGH) in the fractional Hartley domain for digital imaging and communications in medicine (DICOM) images. The proposed algorithm improves the security of the CGH-based algorithm. The cascaded use of fractional Hartley transform and attenuation factor β increases the keyspace of the proposed watermarking algorithm. The robustness and effectiveness of the proposed watermarking algorithm is validated using simulations on DICOM images. The effectiveness of the proposed watermarking algorithm is assessed using statistical tools in terms of mean-squared error, information entropy, correlation coefficient, histogram, and mesh plots. The robustness is evaluated by testing the proposed algorithm’s performance under real-time threats, including contamination and data loss attacks. Furthermore, the security of the proposed algorithm is also tested for existing cryptographic attacks, such as chosen-plaintext attacks, and known-plaintext attacks. The simulation results indicate that the proposed watermarking algorithm is robust and effective.

Supervised Stochastic Approach for computational analysis of convectively heated magnetized nanofluid flow with bioconvection aspects

Our study delves into the dynamics of a convective Magneto-Hydrodynamic Bioconvective Nanofluid model (MHD-BCNFM) flowing over a convectively heated stretched sheet. To accomplish this, we utilize the distinctive capabilities of the Supervised Stochastic Approach for Computational Analysis (SSACA). By integrating similarity transformations, we convert the partial differential equations (PDEs) governing the system into coupled ordinary differential equations (ODEs). We generate the dataset for our approach using the Adam numerical technique specifically tailored for the (MHD-BCNFM). Achieving this involves systematic modulation of parameters such as λ, bioconvection Péclet number Pe, Bioconvection constant σ, Brownian motion parameter Nb, and thermophoresis parameter Nt. "Moreover, we utilize a reference dataset to compute numerical values of various physical quantities in the (MHD-BCNFM) employing SSACA-based Artificial Intelligence methods. The effectiveness of our devised SSACA approach is demonstrated by a negligible mean squared error, ranging from approximately 10−8 to 10−10. Histograms exhibit a maximum error range of 10−5, closely aligning with optimal correlation/regression measures. Outstanding performance metrics in terms of Mean Squared Error (MSE) are attained at levels such as l 9.93E−12, 1.07E−11, 6.28E−10, 1.43E−11, 2.09E−09, 3.65E−11, 2.97E−11, and 2.80E−13 against 320, 430, 209, 85, 356, 295, 66, and 136 epochs. A comparative study between the proposed and reference datasets underscores the authenticity and precision of SSACA, supported by error analyses ranging from E-05 to E-09 across all scenarios.

Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures.

Developing reference-based correlations for temperature distribution in VVER reactor using gene expression programming and single-heated channel approach

Temperature distribution in different channels of a light-water nuclear reactor is essential for Multiphysics coupling approaches in core calculation. Machine learning algorithms trained by well-structured datasets can be applied for the prediction of temperature profiles in different components of the core including coolant, clad and fuel. In this study, gene expression programming (GEP) as an evolutionary machine learning technique is used for developing proper nondimensionalized correlations to estimate the temperature profiles in the VVER-1000 reactor. The single-heated channel approach based on solving mass, energy, and momentum equations is used as a mathematical model for generating datasets to train the GEP. To determine the most influential parameters on the temperature profiles a two-step sensitivity analysis including Pearson correlation coefficient and one-at-a-time sensitivity measure is performed. According to Wilks’ statistical technique, 59 samples are generated to uniformly distribute the uncertainties in the input parameters. By considering 80 % of the acquired data as a training dataset, three reference-based correlations for coolant, clad and center of fuel are developed. Mean Square Error (MSE) and coefficient of determination are used to measure the error of each approach. The high predictability (i.e., R2 > 0.95) presented in the temperature profiles indicates that the GEP could correlate input parameters successfully. The calculations for relative error show that at some axial directions, the maximum relative error can get to 5 %, while the average relative error is 0.21, 0.23, and 1.71 % respectively for coolant, clad, and fuel temperature profiles.

Proactive dam operation based on inflow prediction by modified long short-term memory for improving resilience

The operation of dams can have a significant downstream impact, causing major flood damage. For efficient dam operation, it is essential to accurately predict the inflow using deep learning such as a long short-term memory (LSTM). The modified LSTM (MLSTM) with new optimizers combined with a metaheuristic optimization algorithm was suggested. The MLSTM were trained using rainfall, water level, and discharge of the Yongdam Dam from 2010 to 2019 as input data and the inflow of the Daecheong Dam as output data. Normalization and time lagged cross correlation (TLCC) were applied as data preprocessing. The inflow of the Daecheong Dam in 2020 was predicted using rainfall, water level, and discharge of the Yongdam Dam. Compared to LSTM, MLSTM showed an accuracy improvement of about 63.85–83.34% based on the mean square error (MSE), which is a loss function. The results of proactive dam operation were calculated based on the predicted inflow considering TLCC. Compared to the existing operation, the peak water level of the reservoir was reduced from 77.54 m to 77.17 m, and the resilience was improved from 0.9225 to 0.9623. New dam operation can maintain low water levels of reservoir and improve resilience to prevent flood damage.

Precision in Insurance Forecasting: Enhancing Potential with Ensemble and Combination Models based on the Adaptive Neuro-Fuzzy Inference System in the Egyptian Insurance Industry

ABSTRACT Enhancing the precision of retention ratio predictions holds profound significance for insurance industry decision-makers and those vested in advancing insurance services. Precision helps insurance companies navigate inflationary pressures and evaluate underwriting profitability, enabling reliable prognoses of future underwriting gains. As far as we know, although there have been multiple attempts to construct a predictive model for retention ratio, none of these attempts have used combining models or studied the Egyptian market. Therefore, this study contributes significantly to this developing field by providing combining models, which combined statistical time series models such as Exponential Smoothing (ES), and Autoregressive Integrated Moving Average (ARIMA), with Adaptive Neuro-Fuzzy Inference System (ANFIS). Two different types of combinations are employed with these models. Furthermore, the study introduces three ensemble models designed for the purpose of predicting the retention ratio within the Egyptian insurance market. Dataset was carefully gathered from the EFSA’s annual reports, focused on the property-liability insurance sector within the Egyptian insurance market and covers the time period from 1989 to 2021. Next, the proposed models are assessed employing well-established statistical assessment metrics, namely, Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), R Square (R2), and Root Mean Square Error (RMSE). The results show that combining and ensemble methods improve predicted accuracy. A multi-linear regression-based ensemble model that combines ARIMA, ES, and ANFIS models outperforms both single and combined models in robustness. The article concludes that the insurance industry can greatly benefit from modern predictive methods to make sound decisions.

State of Health Estimation for Lithium-Ion Battery Based on Sample Transfer Learning under Current Pulse Test

Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods.

Development of improved estimators of finite population mean in simple random sampling with dual auxiliaries and its application to real world problems

In general, the incorporation of supplementary information reduces the Mean Square Error (MSE) and, consequently, enhances the precision of estimating a population parameter. This improvement relies on the appropriate application of a suitable function, with careful consideration. This study introduces two innovative families of estimators for the finite population mean, both of which exhibit superior performance in scenarios involving dual auxiliary information in simple random sampling. Expressions up to the first-order approximation, for bias, and Mean Square Error were derived, and the conditions under which these proposed families surpassed the existing estimators. Our evaluation involved the use of both real and simulated data to compute the Mean Square Error and Percent Relative Efficiency (PRE) of the estimators. A comparative analysis revealed that under the specified conditions, both proposed families yielded more precise results.

Optimizing energy storage plant discrete system dynamics analysis with graph convolutional networks

Addressing the challenges of suboptimal model performance and excessive parameters and operations in the optimization of energy storage power plants utilizing Graph Convolutional Network (GCN), this paper introduces a novel approach – the packet-switched graph convolutional network. Initially, a GCN extreme learning machine is established. Drawing inspiration from this solid foundation, we have innovatively crafted a group exchange graph convolution module. This module leverages group graph convolution techniques to amalgamate unique node feature information, tailored to diverse topology graph matrices based on various groupings. This innovative approach ensures that information flows freely and effectively among distinct groupings. Furthermore, we have designed a cutting-edge timing depth separation convolution module, comprising two innovative components. The first component introduces timing depth separation convolution, revolutionizing the original timing convolution module. The second component, the packet-switching graph convolutional network, revolutionizes the time sequence depth separation convolution process. It achieves this by employing 1 × 1 convolutional layers between different feature fusion packets, enabling seamless information exchange between distinct packets. Experimental results demonstrate the efficacy of the proposed model, with root mean square error (RMSE) metrics and root mean square error (MAE) metrics for single-step prediction reaching 46.08 and 26.22 at 60 min, respectively. In multi-step testing, the proposed model exhibits a 14.71 % reduction in RMSE error at the 15-min scale and a 9.29 % reduction at the 60-min scale compared to the benchmark model. This performance improvement enhances the operational efficiency and reliability of the energy storage plant, particularly under dynamic changes in the time series.

Comparative study of integer-order and fractional-order artificial neural networks: Application for mathematical function generation

This paper investigates the impact of fractional derivatives on the activation functions of an artificial neural network (ANN). Based on the results and analysis, a three-layer backpropagation neural network model with fractional and integer derivatives employed in the activation function and fractional gradient descent method backpropagation learning algorithm has been proposed. Specifically, three perceptrons have been proposed based on fractional and integer derivatives applied to activation functions and learning algorithms. They are fractional derivative activation function (FDAF) perceptron, integer derivative activation function (IDAF)-fractional derivative learning algorithm (FDLA) perceptron, and fractional derivative learning algorithm (FDLA) perceptron. The Riemann-Liouville (RL) fractional derivative, Grunwald-Letnikov (GL) derivative, Caputo-Fabrizio (CF) fractional derivative, Caputo (C) fractional derivative, and Atangana-Baleanu (AB) fractional derivative have been employed. The impact of these derivatives’ fractional-order (FO) is investigated in a wide range from 0.1−0.9 on testing mean square error (MSE) and time required to train the perceptron. FO-based and IO-based perceptrons are compared with the help of performance metrics such as testing MSE and the time required to train the models. The training and testing simulation results illustrate that Caputo-Fabrizio derivative-based perceptrons outperform other fractional derivatives. Also, the performance of the FO-based perceptron is better in terms of the least MSE.

A multitask incremental least mean square algorithm using orthonormal codes

A new multitask incremental least mean-square (MILMS) algorithm using orthonormal codes is developed. In the multitask topology, nodes belong to different clusters, with each cluster performing its own ILMS estimation. The closed-form expressions of both the theoretical transient and steady-state mean squared deviation (MSD) are derived. The proposed MILMS algorithm is further reinforced by two combination strategies, referred to here as the combine-then-adapt (CTA), and adapt-then-combine (ATC), giving rise to two new improved algorithms, the CTA-MILMS and ATC-MILMS that substantially improve the MSD. Two further variants of these two improved algorithms are also developed based on the combination of the variable step size (VSS) and discrete cosine transform (DCT) techniques, leading to the two combined algorithms, termed here as CTA-VSSMILMS and ATC-VSSMILMS, respectively. Analysis of the first and second moments of the step size, under the transient and steady-state conditions, is also included.The problem of agent localization is solved using the MILMS algorithm by deploying a fixed number of anchors with known positions that surround agents with unknown positions. Extensive experiments were carried out to show the excellent match between the theoretical transient and steady-state expressions and their corresponding empirical results in terms of MSD performance. Moreover, we show that both the ATC-VSSMILMS and CTA-VSSMILMS algorithms improve the accuracy and computational speed of the MSD results of the proposed MILMS algorithm, thanks to the VSS-induced reduction of the number of needed steps and the DCT-induced data compression effect.

Mixed-Input Residual Network for Air Temperature Forecasting by Low-Power Embedded Devices

Accuracy and model compactness are essential requirements for weather forecasting models designed for operation on low-power embedded devices. This study developed Mixed-Input Residual Network (MIRNet), a compact temperature-forecasting deep neural network model. MIRNet integrates stacked bidirectional long short-term memory layers using concatenated 1-dimensional and 2-dimensional convolutional layers to improve model accuracy. MIRNet was trained and tested on two datasets: one, IfeData, comprising historical weather data from Ile-Ife, Nigeria and the other a standard weather forecasting dataset called the Jena dataset. Training was carried out using 100 epochs of data partitioned in the standard 80:20 ratio, with an adaptable learning rate strategy. The model was tested for Nth-hour-ahead prediction for 1N24; where N are natural numbers, and performance quantified using metrics such as mean absolute percentage error (MAPE) and mean square error (MSE). The model was also implemented on a Raspberry Pi 4 device with a 1.8 GHz 64-bit quad-core ARM Cortex-A72 processor. The model achieved a MSE of 1.00 x 10-3 on the IfeData dataset, and 1.23x10-4 on the Jena dataset for 1-hour ahead forecasting. This is currently the best verifiable result achieved on the Jena dataset by any prediction model globally. For Nth hour ahead forecasting, MIRNet achieved an MSE generally below 2.0x10-3 for all values of N on the standard Jena dataset. The MSE of MIRNet for N-sequential 1-hour ahead and single Nth hour predictions using the Jena dataset reveal quadratic and linear relationships with N respectively. The model compares favourably with existing models for multi-hour predictions. The developed model is compact and has good forecasting properties.

Optimizing removal of antiretroviral drugs from tertiary wastewater using chlorination and AI-based prediction with response surface methodology

Chemical and pharmaceutical chemicals found in water sources create substantial risks to human health and the environment. The presence of pharmaceutical contaminants in water can cause antibiotic resistance development, toxicity to aquatic organisms, and endocrine disruption. Hence, the elimination of chemicals and other contaminants from wastewater prior to its release is a burgeoning concern in the domains of engineering and science. The use of treatment technologies in wastewater treatment plants can remove pharmaceutical contaminants through the oxidation process. However, many traditional wastewater treatment plants lack the advanced monitoring tools required to detect low concentrations of pharmaceuticals. Without the ability to detect these compounds, it's challenging to treat them effectively. The goal of this study was to use Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) algorithms to model and improve how Nevirapine and Efavirenz break down in different chlorination conditions. The RSM analysis revealed statistically significant models (F-values: Nevirapine, pH-t: 108.15, T-t: 76.55, ICC-t: 110.84), indicating a strong correlation between operational parameters (pH, temperature, and initial chlorine concentration) and degradation behavior. The ANN model accurately predicted the degradation of both Nevirapine and Efavirenz under various chlorination conditions, as confirmed by analyzing actual-predicted graphs, residual plots, and Mean Squared Error (MSE) values. The ANN model using ICC-t achieved the highest MOD value of 31.31 % for Nevirapine. The ANN model based on ICC-t yielded a maximum MOD value of 16.06 % for Efavirenz. These findings provide valuable insights into optimizing chlorination processes for better removal of these pharmaceutical contaminants from water.

Improved exponential type variance estimators for population utilizing supplementary information

This paper contributes to the existing literature on variance estimators by utilizing supplementary information. The variance estimation problem of a finite population is a significant matter as sometimes, it is tough to control the variation. For this purpose, an optimum family of exponential variance estimators is suggested under simple random sampling. Moreover, different specific members of the proposed estimators are identified by incorporating various known characteristics of the supplementary variable in the suggested generalized class of estimators. The derivations for the expressions of bias as well as mean square error (MSE) of the proposed estimators are conducted. The suggested family of estimators is studied in different situations by using sets of real data and simulation studies for their performance. To evaluate the efficiency of the suggested estimators, R software is used for the analysis. The study compares the performance of the proposed estimators against the traditional estimators. The theoretical and numerical comparisons show that the estimators suggested in the study are superior in efficiency as compared to the existing estimators.

Impact of Hardware Impairment on the Joint Reconfigurable Intelligent Surface and Robust Transceiver Design in MU-MIMO System

Reconfigurable intelligent surface (RIS) is a revolutionary passive radio technique to facilitate capacity enhancement beyond the current massive multiple-input multiple-output (MIMO) transmission. However, the potential hardware impairment (HWI) of the RIS usually causes inevitable performance degradation and the amplification of imperfect CSI. These impacts still lack full investigation in the RIS-assisted wireless network. This paper developed a robust joint RIS and transceiver design algorithm to minimize the worst-case mean square error (MSE) of the received signal under the HWI effect and imperfect channel state information (CSI) in the RIS-assisted multi-user MIMO (MU-MIMO) wireless network. Specifically, since the proposed robust joint RIS and transceiver design problem yields non-convex characteristics under severe HWI, an iterative three-step convex algorithm is developed to approach the optimality by relaxation and convex transformation. Compared with the state-of-the-art baselines that ignore the HWI, the proposed robust algorithm inhibits the destruction of HWI while raising the worst-case MSE effectively in several numerical simulations. Moreover, due to the properties of the HWI, the performance loss is notable under the magnification of the number of reflected elements in the RIS-assisted MU-MIMO wireless network.