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As the demand for high-speed data transmission grows with the expected emergence of 6G networks and the proliferation of wireless devices, more than traditional wireless infrastructure may be required. Small cell networks (ScNs) integrated with reconfigurable intelligent surfaces (RISs) and multiple-inputmultiple-output (MIMO) have emerged as promising solutions to address this issue. However, ScNs have resource allocation limitations, and traditional RISs can only reflect signals in a limited propagation space of 1800 with fixed reflection properties. This paper proposes a novel approach to overcome these challenges by introducing actively simultaneously transmitting and reflecting (ASTAR)-RISs. Unlike conventional RIS, ASTAR-RISs actively amplify and transmit signals, effectively mitigating the limited propagation challenge and improving signal strength, especially in dense ScNs. This approach enhances the quality of service in complex channel environments by amplifying, on top of reflection, from the macro base station (mBS), improving the overall signal strength, and providing 3600 flexible propagation space. Furthermore, ASTAR-RIS enables dynamic beam management, significantly improving signal coverage and interference management, which are crucial in dense deployments. In this work, we propose a network architecture where distributed ASTAR-RIS units are deployed to assist small cell mBSs by optimizing signal coverage and enhancing communication performance. ASTAR-RISs dynamically control signal reflection and amplification, complementing the functionality of traditional small-cell BSs in dense network environments. Using the MIMO technique, we design phase shifts for ASTAR elements and develop optimal hybrid beamforming for users at the mBS. We dynamically control the ON/OFF status of the ASTAR-RIS based on active or idle status. We propose an efficient model that ensures fairness of signal-to-noise ratio (SNR) for all users and minimizes overall power consumption while meeting user SNR and phase shift constraints. To this end, we integrate robust beamforming and power allocation strategies, ensuring the system maintains reliable performance even under imperfect channel state information (CSI). We formulate a max-min optimization problem that optimizes the SNR and power consumption, subject to the ON/OFF status, phase shift, and power budget of the ASTAR-RIS. Our proposed method uses an alternating optimization algorithm to optimize the phase shift matrix at the ASTAR-RIS and the hybrid beamforming at the mBS. The approach includes two transmission schemes, and the phase optimization problem is solved using a successive convex approximation method that offers a closed-form solution at each step. Additionally, we use the dual method to determine the optimal ON/OFF status of the ASTAR-RIS. Comprehensive simulations validate the robustness and scalability of our proposed solution, particularly under varying network densities and CSI uncertainties. provides significant performance improvements over 170% compared to traditional RIS schemes.

Abstract This paper introduces Deep Convolutional Generative Adversarial Networks (DCGAN) as a potential solution for wireless systems aiming to enhance the Block Error Rate (BLER). The DCGAN under consideration consists of a generator and a discriminator, which utilizes the same network to differentiate between true and synthetic messages while simultaneously reconstructing the transmitted message. The wireless network is modeled as a DCGAN, where the desirable communication link is modeled between the transmitter and receiver. In addition, the expression for the transmitter and receiver of DCGAN is analytically derived. Based on the simulation outcomes, the proposed DCGAN has investigated a significantly lower BLER than the other models, namely convolutional neural networks, deep learning, and conditional GAN.

Abstract The challenge of predicting wind speeds to facilitate site selection and the consistent operation of wind power plants in coastal regions is a global concern. The output of wind turbines is subject to fluctuations corresponding to changes in wind speed. The unpredictable characteristics of wind patterns introduce vulnerabilities to wind power facilities in wind power plants. To address this unpredictability, an effective strategy involves forecasting wind speeds at specific locations during wind power plant operations. While previous research has explored various machine learning algorithms to tackle these issues, satisfactory results have not been achieved, and Bangladesh faces challenges in this regard, especially in low-wind speed areas. This study aims to identify the most accurate machine learning-based algorithm to forecast the short-term wind speed of two areas (Kutubdia and Cox's Bazar) located on the eastern coast of Bangladesh. Wind speed data for a span of 21.5 years, ranging from January 2001 to June 2022, were sourced from two outlets: the Bangladesh Meteorological Department and the website of NASA. Wind speed has been forecasted using 14 different regression-based machine learning models with a comprehensive overview. The results of the experiment highlight the exceptional predictive performance of a boosting-based ensemble method known as categorical boosting, especially in the context of forecasting wind speed data obtained from NASA. Based on the testing data, the evaluation yields remarkable results, with coefficients of determination measuring 0.8621 and 0.8758 for wind speed in Kutubdia and Cox's Bazar, respectively. The study underscores the critical importance of prioritizing optimal turbine site selection in the context of wind power facilities in Bangladesh. This approach can yield benefits for stakeholders, including engineers and project owners associated with wind projects.

The use of the personal Internet of Things (PIoT) is rapidly expanding across a range of application areas. It is needed for real-time monitoring, wireless coverage, remote sensing, delivery, security, surveillance, and civil infrastructure inspection. Smart PIoT devices provide new opportunities in wearable, public, and home automation, making them the next significant development in PIoT technology. The study provides a comprehensive overview of PIoT systems, including their architecture, applications, technology, and future developments. This paper begins with a comprehensive overview of prior research about the PIoT network. Then, we present an overview of PIoT classification and a comprehensive explanation of the PIoT architecture. We also investigate the requirements and technology of the PIoT network. Additionally, we provide a thorough examination of several applications for PIoT networks. Moreover, we emphasize some significant challenges and ongoing problems confronting the PIoT network. Finally, we address recent research trends and outline potential future study directions.

Enhancing Internet of Things (IoT) communications through Reconfigurable Intelligent Surfaces (RIS) necessitates novel approaches that go beyond the conventional deployment of passive elements. This paper introduces an efficient method to enhance IoT system performance by combining active and passive functionality in RISs: an active and passive integrated approach. One identified enhancement area is efficiently managing signal throughput and exchanges in IoT systems, which is essential for supporting numerous devices. For this challenge, we propose a thorough process model that describes channel gains, develops a nonlinear energy harvesting, and identifies device-to-device communication schemes without violating information causality. The innovative component of our work is the utilization and strategic application of RIS panels that benefit from the advantages of active and passive components synergism to solve thermal noise issues and optimize signal reflection and transmission. An advanced optimization mechanism is developed based on mixed-integer nonlinear programming: an enabling approach between performance efficiency and maximum service utilization. Our simulation analysis show that developed RIS panels optimize IoT system performance and surpass existing performance indicators in conventional RIS-less systems.

Millimeter-wave beamspace massive multiple-input multiple-output system with a lens antenna array can minimize transceiver hardware complexity without compromising performance. However, the number of supported portable user terminals cannot exceed the number of radio frequency blocks accessible at the same time, frequency, and coding resources. In this paper, we propose the integration of rate-splitting multiple access and orthogonal random precoding into the downlink of beamspace massive multiple-input multiple-output system to support a larger number of portable user terminals than the number of available radio frequency blocks while minimizing both inter- and intra-beam interferences and extending the cell coverage percentage. Then, we formulate an optimization problem to optimize the system’s overall throughput while keeping the minimum needed throughput and power budget in consideration. The nonconvex optimization issue is then approximated into a convex optimization problem using the successive convex approximation approach. Following that, we offer an alternating method to solve the approximate optimization issue and select an optimal solution. Furthermore, we deduce an analytic expression for the downlink cell coverage percentage and evaluate the effectiveness of the suggested method in terms of total throughput, energy efficiency, and cell coverage percentage. Finally, we compare the proposed method with benchmark techniques for perfect and imperfect channel state information, and numerical results confirm the superior performance of the proposed method over benchmark techniques in terms of sum throughput, energy efficiency, and cell coverage percentage.

Abstract Bangladesh is one of the world’s most susceptible countries to climate change. Global warming has significantly increased surface temperatures worldwide, including in Bangladesh. According to meteorological observations, the average temperature of the world has risen approximately 1.2 °C to 1.3 °C over the last century. Researchers and decision-makers have recently paid attention into the climate change studies. Climate models are used extensively throughout the nation in studies on global climate change to determine future estimates and uncertainties. This paper outlines a perceptible stacking ensemble learning model to estimate the temperature of a tropical region—Cox’s Bazar, Bangladesh. The next day’s temperature, maximum temperature, and minimum temperature are estimated based on the daily weather database collected from the weather station of Cox’s Bazar for a period of 20 years between 2001 and 2021. Five machine learning (ML) models, namely linear regression (LR), ridge, support vector regression (SVR), random forest (RF), and light gradient boosting machine (LGBM) are selected out of twelve ML models and combined to integrate the outputs of each model to attain the desired predictive performance. Different statistical schemes based on time-lag values play a significant role in the feature engineering stage. Evaluation metrics like mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) are determined to compare the predictive performance of the models. The findings imply that the stacking approach presented in this paper prevails over the standalone models. Specifically, the study reached the highest attainable R2 values (0.925, 0.736, and 0.965) for forecasting temperature, maximum temperature, and minimum temperature. The statistical test and trend analysis provide additional evidence of the excellent performance of the suggested model.

The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.

To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes of data quickly. Different machine learning approaches to the classification and detection of lung and colon cancer have been proposed by multiple research studies. However, when it comes to self-learning classification and detection tasks, deep learning (DL) excels. This paper suggests a novel DL convolutional neural network (CNN) model for detecting lung and colon cancer. The proposed model is lightweight and multi-scale since it uses only 1.1 million parameters, making it appropriate for real-time applications as it provides an end-to-end solution. By incorporating features extracted at multiple scales, the model can effectively capture both local and global patterns within the input data. The explainability tools such as gradient-weighted class activation mapping and Shapley additive explanation can identify potential problems by highlighting the specific input data areas that have an impact on the model’s choice. The experimental findings demonstrate that for lung and colon cancer detection, the proposed model was outperformed by the competition and accuracy rates of 99.20% have been achieved for multi-class (containing five classes) predictions.

Road accidents are increasing worldwide and are causing millions of deaths each year. They impose significant financial and economic expenses on society. Existing research has mostly studied road accident prediction as a classification problem, which aims to predict whether a traffic accident may happen in the future or not without exploring the underneath relationships between the complicated factors contributing to road accidents. A number of research have been done to date to explore the importance of road accident contributing factors in relation to road accidents and their severity, however, only a few of those research have explored a subset of ensemble ML models and the New Zealand (NZ) road accident dataset. Therefore, in this paper, we have evaluated a set of machine learning (ML) models to predict road accident severity based on the most recent NZ road accident dataset. We have also analysed the predicted results and applied an explainable ML (XML) technique to evaluate the importance of road accident contributing factors. To predict road accidents with different injury severity, this work has considered different ensembles of ML models, like Random Forest (RF), Decision Jungle (DJ), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (L-GBM), and Categorical Boosting (CatBoost). New Zealand road accident data from 2016 through 2020 obtained from the New Zealand Ministry of Transport is used to perform this study. The comparison results show that RF is the best classifier with 81.45% accuracy, 81.68% precision, 81.42% recall, and 81.04% of F1-Score. Next, we have employed the Shapley value analysis as an XML technique to interpret the RF model performance at global and local levels. While the global level explanation provides the rank of the features’ contribution to severity classification, the local one is for exploring the use of features in the model. Furthermore, the Shapley Additive exPlanation (SHAP) dependence plot is used to investigate the relationship and interaction of the features towards the target variable prediction. Based on the findings, it can be said that the road category and number of vehicles involved in an accident significantly impact injury severity. The identified high-ranked features through SHAP analysis are used to retrain the ML models and measure their performance. The result shows 6%, 5%, and 8%, increase, respectively, in the performances of DJ, AdaBoost, and CatBoost models.

Unmanned aerial vehicle (UAV)-based communication system design has already attracted substantial interest due to UAV flexibility in deployment, cost effectiveness, and in-built line-of-sight air-to-ground channels. However, there is a persistent issue of security threats associated with the broadcast nature of UAV: physical layer security (PLS) can be introduced to enhance the secrecy performance in that regard. In the system proposed here, the combined effect of 3D fractional-order Liu chaotic system and 3D fractional-order Li chaotic system is introduced to enhance the PLS of the UAV-to-ground communication network. In addition, to reduce multi-user interference (MUI), efficient orthogonal variable spreading factor (OVSF) codes are integrated with a zero forcing (ZF) scheme. Moreover, to improve bit error rate (BER), a combined MUI signal discrimination and ZF signal detection scheme is introduced, strengthened by various channel coding techniques with multi-user beamforming weighting. The numerical outcomes establish the efficacy of the proposed system in terms of PLS and improvement of data rate with signal-to-interference-plus-noise ratio. This approach achieved an out-of-band emission reduction of 119 dB. Furthermore, an improved BER of $1 \times 10^{-4}$ is achieved in 16-QAM for a signal-to-noise ratio of 6 dB.

Terahertz (THz) band is one of the most promising aspects of wireless communication systems because of its potential to meet the growing demands for the envisioned next-generation of cellular communications. THz band connectivity can alleviate bandwidth shortages and transmit power constraints using multiple-input multiple-output (MIMO) systems. To obtain better throughput and enhanced spectral efficiency, non-orthogonal multiple access (NOMA) configuration can be incorporated into MIMO systems, as NOMA uses non-orthogonal resource allocation by assigning the same carrier frequency to multiple devices in the power domain. In this paper, we focus on 2 × 1 MIMO-NOMA and cooperative 2 × 1 MIMO-NOMA systems for THz spectrum in the downlink transmission. In order to evaluate the effectiveness of the proposed system architecture, we derive accurate data rate, transmission latency, and reliability expressions for 2 × 1 MIMO-NOMA system consists of critical and non-critical devices by leveraging finite block length theory. Moreover, we derive the closed-form expressions for power splitting coefficients, data rate, transmission latency, and reliability for the considered cooperative 2 × 1 MIMO-NOMA system. Extensive numerical results are presented to validate the feasibility of the proposed 2 × 1 MIMO-NOMA as well as the cooperative 2 × 1 MIMO-NOMA systems in the THz band.

Effective networking over wireless media has become extremely essential today as communication between massive Internet of things (IoT) devices is on an increase, thereby leading to a limited spectrum resource for utilisation. Specifically, for healthcare infrastructure in a remote or critical situation, providing uninterrupted communication between the macro base station and IoT devices or user nodes is imperative. However, owing to their limited spectral capacity, unmanned aerial vehicles (UAVs)-based networks can provide an efficient solution and utilise both licensed and unlicensed bands for communication among users or devices. In this paper, our focus is on cache-enabled cognitive networking for secondary users (SUs) that accredits precise communication delivery for critical healthcare systems that are performed by the cognitive UAV (CUAV). In addition, we develop a caching strategy wherein a CUAV is capable of caching relevant information from high-power (HP) and moderate-power (MP) devices in its local and cloud storage by applying a non-orthogonal multiple-access method. In the downlink scenario, the CUAV proactively transmits the requested HP and MP information to the designated SUs considering this entire model over two states, namely effectual state and interference state, which we can realise by any presence or absence of interference. To maximise this system’s energy efficiency, we formulate an optimisation problem to minimise the transmission power and satisfy the target performance in terms of throughput for SUs. We solve the optimisation issue using the Lagrangian approach and the Karush-Kuhn-Tucker conditions. In all simulations, the energy efficiency during the effectual state renders an average performance of approximately 400% better than that of the interference state.

The mobile relaying technique is a critical enhancing technology in wireless communications due to a higher chance of supporting the remote user from the base station (BS) with better service quality. This paper investigates energy-efficient (EE) mobile relaying networks mounted on the unmanned aerial vehicle (UAV) while the unknown adversaries try to intercept the legitimate link. We aim to optimize robust transmit power, both UAV and BS along, with relay hovering path, speed, and acceleration. The BS sends legitimate information, which is forwarded to the user by the relay. This procedure is defined as information-causality-constraint (ICC). We jointly optimize the worst-case secrecy rate (WCSR) and UAV propulsion energy consumption (PEC) for a finite time horizon. We construct the BS-UAV, the UAV-user, and the UAV-adversary channel models. We apply the UAV PEC considering UAV speed and acceleration. We derive EE UAV relay-user maximization problem in the adversarial wireless networks at last. While the problem is non-convex, we propose an iterative and sub-optimal algorithm to optimize EE UAV relay with constraints, such as ICC, trajectory, speed, acceleration, and transmit power. First, we optimize both BS and UAV transmit power and hovering speed for known UAV path planning and acceleration. Using the optimal transmit power and speed, we obtain the optimal trajectory and acceleration. We compare our algorithm with existing algorithms and demonstrate the improved EE UAV relaying communication for our model.

The integration of unmanned aerial vehicles (UAVs) into spectrum sensing cognitive communication networks can offer many benefits for massive connectivity services in 5G communications and beyond; hence, this work analyses the performance of non-orthogonal multiple access-based cognitive UAV-assisted ultra-reliable and low-latency communications (URLLCs) and massive machine-type communication (mMTC) services. An mMTC service requires better energy efficiency and connection probability, whereas a URLLC service requires minimising the latency. In particular, a cognitive UAV operates as an aerial secondary transmitter to a ground base station by sharing the unlicensed wireless spectrum. To address these issues, we derive the analytical expressions of throughput, energy efficiency, and latency for mMTC/URLLC-UAV device. We also formulate an optimisation problem of energy efficiency maximisation to satisfy the needs of URLLC latency and mMTC throughput and solve it using the Lagrangian method and the Karush-Kuhn-Tucker conditions. The algorithm is presented by jointly optimising the transmission powers of the mMTC and URLLC users. The derived expressions and algorithm are then used to evaluate the performance of the proposed system model. The numerical results show that the proposed algorithm improves the energy efficiency and satisfies the latency requirement of the mMTC/URLLC-UAV device.

The incorporation of user-assisted cooperative relaying into beamspace massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) system can extend the coverage area and improve the spectral and energy efficiency for millimeter wave (mmWave) communications when a dynamic cluster of mobile user terminals (MUTs) is formed within a beam. We propose threshold-based user-assisted cooperative relaying into a beamspace mMIMO NOMA system in a downlink scenario. Specifically, the intermediate MUTs between the next-generation base station (gNB) and the cell-edge MUT become relaying MUTs after the successful decoding of the signal of the cell-edge MUT only when they meet the predetermined signal-to-interference plus noise ratio (SINR) threshold. A zero forcing (ZF) precoder and iterative power allocation are used to minimize both inter- and intra-beam interferences to maximize the system sum rate. We then evaluate the performance of this system in a delay-intolerant cell-edge MUT scenario. Moreover, the outage probability of the cell-edge MUT of the proposed scheme is investigated and an analytic expression is derived. Simulation results confirm that the proposed threshold-based user-assisted cooperative relaying beamspace mMIMO NOMA system outperforms the user-assisted cooperative relaying in beamspace mMIMO NOMA, beamspace MIMO-NOMA, and beamspace MIMO orthogonal multiple access (OMA) systems in terms of spectrum efficiency, energy efficiency, and outage probability.

In this paper, a coordinated multipoint joint transmission (CoMP-JT) framework at mmWave for a cyclic prefix (CP)-free multiuser OFDM wireless communication system is developed and analyzed. The aim is to provide high-quality service to cell-edge users; otherwise, the cell-users would suffer from significant signal degradation due to undesired interference. The impact of complex Hadamard transform with block diagonalization channel precoding for multiuser interference reduction and designed subcarrier mapping for out-of-band (OOB) reduction are investigated. In addition, the paper studied the input back-off-aided high-power amplifier for peak-to-average power ratio (PAPR) reduction and forward error correction channel coding for improved bit error rate (BER) for cell-edge users at mmWave frequencies. Moreover, signal-to-interference-noise ratio and ergodic achievable rate are estimated both in the presence and absence of CoMP-JT-based transmission technique to verify their significance in terms of transmitted power. Numerical investigations showed an OOB reduction of 312 dB, PAPR reduction from 17.50 dB to 7.66 dB, and improved BER of 1×10−3 in 16-QAM for a signal-to-noise ratio of −6 dB. Hence, the simulation results demonstrated the effectiveness of the developed system.