This paper presents the design and development of an intelligent, long range wide area network (LoRaWAN)-based smart home energy management system that integrates real-time analytics and artificial intelligence (AI) techniques. Utilizing a publicly available, appliance-level energy consumption dataset, the system simulates realistic household usage patterns across multiple devices. A MATLAB-based LoRaWAN transmission model, incorporating packet loss and signal attenuation, emulates low-power wide-area network (LPWAN) behavior under realistic wireless communication conditions. Key system functionalities include anomaly detection via statistical thresholding and moving averages, energy consumption forecasting through linear regression and decision tree models, i.e., fitrtree, idle device detection, and daily cost prediction in United Arab Emirates Dirhams (AED). The system demonstrates moderate predictive accuracy, with mean$R^{2}$values of approximately 0.55 per appliance. An interactive command-line interface and an artificial general intelligence (AGI)-inspired natural language chat module enhance usability, enabling non-technical users to query energy data and cost insights effectively. Visualization tools support real-time energy pattern recognition and future consumption forecasting, facilitating informed user decisions. Despite simulated packet loss and missing data, the system maintains robust performance through data interpolation and resilient model training. The proposed framework lays the foundation for scalable, intelligent home energy systems and offers pathways toward deeper learning integration, dynamic pricing models, and edge deployment for real-time autonomous energy management.

This study introduces a fully automated collision detection and notification system specifically engineered to safeguard parked vehicles against accidental impacts in densely populated areas such as commercial parking lots and urban streets. The system employs an integrated network of front and rear cameras, proximity sensors, and vibration sensors to provide continuous environmental monitoring around a stationary vehicle. When a foreign object or vehicle encroaches within a predefined proximity, the system initiates real-time surveillance by activating on-board cameras. Simultaneously, visual alert mechanisms, such as high-intensity flashing lights, are triggered to attract the attention of nearby drivers and prevent potential collisions. In the event of physical contact, the system immediately begins continuous video recording, capturing high-resolution footage of the incident. This evidence is securely transmitted to the vehicle owner's mobile device via a dedicated application, delivering instant notification and remote access to the recorded material. The design emphasizes affordability and accessibility, ensuring that advanced vehicle protection is available to a broad user base. By combining proactive collision deterrence with post-incident documentation and real-time communication, the proposed system offers a comprehensive and practical solution to mitigate the risk and consequences of parked vehicle collisions. Experimental validation confirms the system's reliability, responsiveness, and effectiveness in real-world parking scenarios, demonstrating its value as a robust enhancement to vehicular safety infrastructure.

The rapid expansion of highways in desert regions has resulted in an increase in camel-vehicle collisions, leading to substantial human, economic, and animal welfare impacts. Despite the success of virtual fencing in managing livestock such as cattle, goats, and sheep, its application for camels remains largely unexplored. This paper introduces an innovative global positioning system (GPS)-enabled virtual fencing prototype that leverages non-invasive auditory cues and real-time monitoring to control camel movements. The system integrates advanced geofencing algorithms, a random forest machine learning classifier trained on accelerometer data with an accuracy of 92% for activity recognition, and long range (LoRa) communication for reliable long-range data transmission. Field tests on camels demonstrate that auditory signals at 2000 Hz effectively deter the camels from crossing virtual boundaries after repeated interactions. The system maintains robust performance, achieving GPS positional accuracy of 8.08 meters and ensuring effective communication over distances up to 1.5 km. This study offers a significant contribution to the fields of wireless communication and Internet of Things (IoT)-based animal management systems.

Integrated sensing and communications (ISAC) has emerged as a promising solution for addressing spectrum congestion in sixth-generation communication systems. In this work, we consider the deployment of ISAC in a full-duplex cell-free (FD-CF) multi-input multi-output (MIMO) system that is aided by a reconfigurable intelligent surface (RIS). To overcome the performance limitations of a single ISAC base station (BS), we consider multiple FD access points (APs) that simultaneously perform target detection and multi-user uplink (UL) communication, assisted by a RIS. We aim to maximize the weighted sum of the output radar and communication signal-to-interference-plus-noise ratios (SINRs) by jointly designing the radar and communication receive beamformers, UL transmission powers, joint downlink (DL) sensing beamformers, and RIS reflection coefficients. The total UL, DL power budgets, and the RIS phase shift unit-modulus constraints are considered to guarantee the balance between sensing and communication requirements. The resulting problem is non-convex and rather formidable to solve. Nonetheless, an efficient solution to this problem is developed based on alternating optimization, which utilizes majorization-minimization (MM), fractional programming (FP), and the penalty method. Simulations demonstrate the effectiveness of the proposed solution and the advantages of deploying RIS to assist integrated cooperative sensing and communication (ICSAC) in FD-CF MIMO systems.

The evolution of communication networks towards multi-functionality has paved the way for integrated sensing, communication, and power transfer (ISCAPT) systems, enabling efficient data transmission, environmental sensing, and wireless energy transfer. However, conventional ISCAPT architectures face inherent trade-offs between high-rate communication, precise sensing, and efficient energy transfer, exacerbated by interference and dynamic channel conditions. To address these limitations, rate-splitting multiple access (RSMA) and reconfigurable intelligent surfaces (RIS) are integrated into ISCAPT systems. Hence, in this paper, we consider active RIS-aided RSMA ISCAPT to maximize the sum communication rate while ensuring the sensing performance, harvesting adequate energy, and satisfying the transmit power budgets of the BS and RIS. To achieve this goal, the transmit beamforming matrix, RIS reflection matrix, power splitting factor, radar receive beamforming, and common rate allocation are jointly designed. However, the formulated maximization problem is challenging due to its non-convex nature and the coupling among the optimization variables. To efficiently tackle this issue, the formulated problem is decomposed into three sub-problems, which are reformulated into quadratic-constrained-quadratic programs (QCQPs). Then, an alternating optimization (AO)-aided majorization-minimization (MM) and successive convex approximation (SCA) algorithm is proposed to iteratively optimize these sub-problems. Simulation studies are conducted to demonstrate the effectiveness of the proposed framework and illustrate trade-offs compared to established benchmarks.

This research explores the latest advancements in channel estimation, precoding, and detection techniques within massive multiple-input multiple-output (MIMO) systems, which are crucial for the evolution of fifth-generation (5G) and beyond. As global data traffic surges, traditional methodologies face significant limitations, necessitating innovative approaches to enhance performance. This paper critically examines how these advanced techniques effectively address challenges such as increased spectral efficiency and reduced latency while significantly improving overall signal processing efficiency. This work presents practical applications of these methodologies, showcasing a detailed analysis of novel signal detection algorithms designed to maximize system performance in real-world scenarios. By leveraging state-of-the-art signal processing frameworks, it is demonstrated how these techniques enhance detection accuracy and optimize resource allocation, ensuring robust communication in dense user environments. Ultimately, this study underscores the transformative potential of massive MIMO in revolutionizing wireless communications. The findings offer critical insights and practical guidelines that contribute to advancing telecommunications infrastructure, equipping stakeholders to meet the dynamic demands of next-generation wireless networks. This research aims to inspire further exploration and development in this rapidly evolving field, establishing massive MIMO as a cornerstone of future connectivity solutions.

The advent of fifth-generation (5G) wireless communication systems is reshaping the connectivity landscape, enabling unprecedented speed, reliability, and capacity. Central to this transformation is multiple-input multiple-output (MIMO) technology, which plays a critical role in harnessing the full potential of 5G networks. This research paper offers an in-depth exploration of emerging MIMO technologies, focusing mainly on massive MIMO (MMIMO) as a pivotal case study. This study delves into the fundamental principles that underpin these technologies, examining their substantial benefits, inherent challenges, and diverse applications across various sectors, from smart cities to autonomous systems. Through a thorough review of the existing literature and technical specifications, this paper illuminates recent advancements and highlights key open research questions. Additionally, this work proposes future directions for MIMO technologies, envisioning their vital role in the evolution of 5G and beyond. By addressing these dimensions, this paper aims to contribute to the ongoing discourse in wireless communications and inspire further innovation in the field.

In recent years, the proliferation of data and sensor measurements in various scientific fields, particularly within the realm of the Internet of Things, has opened new avenues for knowledge extraction through advanced data analysis techniques. However, the presence of outliers and anomalies poses significant challenges, leading to inaccuracies that can compromise analytical outcomes. Outliers are defined as data points that deviate markedly from other observations, often resulting from measurement errors or inconsistencies within the dataset. Their detection and removal during the data cleaning process are crucial for enhancing data quality and ensuring robust analysis. This study systematically investigates the impact of outliers and their detection on the accuracy and performance of various machine learning algorithms and statistical models in regression and classification tasks. A series of MATLAB simulations is conducted on standard datasets to evaluate the effects of outliers and validate the performance of different methodologies. The findings highlight the critical importance of effective outlier detection, demonstrating a marked improvement in the accuracy and reliability of analytical results.

In most communication systems, when transmitting or receiving a signal, some unwanted signals may get introduced into the communications, making the quality of the communication poor. This disturbance, referred to as noise, can affect the reliability and quality of a digital communication system by corrupting or getting a message scrambled whenever it is transmitted. To prevent these errors, error-correction codes help detect and correct these errors arising during data transmission within digital communication systems. In communication systems, such error-correcting codes are essential for ensuring the efficient and reliable exchange of information over noisy channels like satellite links, optical fibers, and wireless networks. As such, the work presented in this paper delves into the issue of noisy data in communication systems and how error-correcting codes can be used to solve the problem. To achieve this, a review of different relevant literature by various scholars is done to present authentic and verifiable data on error-correcting codes for communication systems. On top of that, this paper introduces a novel approach by integrating advanced machine learning techniques, particularly artificial neural networks, to enhance the effectiveness and adaptability of error-correcting codes in diverse and dynamically changing communication environments, significantly surpassing traditional error correction methods.

Medication non-adherence is a prevalent concern, particularly among individuals managing chronic illnesses who rely on consistent pill consumption. This study addresses forgetfulness and non-compliance in medication intake by proposing a system that ensures accurate administration of prescribed medications at designated times. This paper investigates medication adherence challenges, primarily focusing on chronic condition management. Leveraging mobile phones, our innovative approach aims to mitigate these challenges. This paper proposes a system that delivers timely reminders to patients via mobile devices, fostering responsibility toward adhering to medication regimens. Central to the proposed solution is a patient-to-hospital communication framework, enabling caregivers to curate medication schedules. Caregivers have control over medications, timings, and dosages. This empowers short-term and long-term medication users to monitor regimens, alleviating concerns of omissions or deviations. Implications of the proposed framework are far-reaching. Consistent medication adherence can enhance therapeutic interventions, potentially reducing morbidity and mortality. The presented technology-healthcare convergence underscores the positive impact of technological interventions in medical contexts.

Throughout recent years, the field of wireless communication has experienced exponential growth. This expansion has been propelled by the continual innovation of diverse wireless standards and the evolution of high-speed applications, resulting in a mounting scarcity of spectrum and an intensified demand for bandwidth. Regrettably, existing studies substantiate an inefficient utilization of available frequency bands. Channel bandwidth and effective spectrum utilization persist as formidable challenges in the realm of wireless communication. Addressing these challenges, cognitive radio stands as a pivotal solution, enabling the efficient sharing of available spectrum among primary/secondary or licensed/unlicensed users. The successful implementation of cognitive radio relies significantly on accurate spectrum sensing and prediction to avert interference or collisions among users. This work introduces a neural network-based model augmented and fine-tuned by a genetic algorithm, exemplifying state-of-the-art effectiveness in spectrum prediction. To expand the horizon, this paper investigates a novel approach based on the radial basis function network, further enriching the exploration. The proposed model demonstrates exceptional performance as validated through rigorous MATLAB simulations. The comparative analysis of these simulations serves as a robust benchmark, illuminating the superior efficacy and practicality of the model in real-world scenarios.

In this paper, we investigate the bit-error-rate (BER) performance of linear receivers, zero-forcing (ZF) and maximum ratio combining (MRC); and iterative ZF-enabled vertical Bell laboratories layered space-time (V-BLAST) under the context of multiple-input-multiple-output (MIMO) molecular communications (MC). We show the detection techniques depend on pilot sequences while estimating the channels. Furthermore, we leverage these results to investigate the performance of MC under V-BLAST and ZF detection to assure that they displayed significantly outperformed MRC in which BER remained constant regardless of the number of pilot symbols used.

Abstract With the advent of fifth generation (5G) systems and the Internet‐of‐Things (IoT), the number of interconnected wireless devices is increasing significantly. Protocols that allow these deceives to interconnect peer‐to‐peer through wireless links are becoming of interest. The major challenge is the inevitable interference among the simultaneously activated wireless links. Given a set of wireless links, this article addresses the non‐deterministic polynomial‐time (NP) hard problem of selecting the maximum subset of links that can be simultaneously activated at their respective signal‐to‐interference‐plus‐noise‐ratio (SINR) targets. The contribution of this article is two‐fold. First, we introduce a new genetic algorithm (GA) constraint‐handling mechanism, and prove analytically that finding optimal link schedules is guaranteed. Second, we develop a novel parallelized GA to solve the problem. Through serial algorithm analysis, we utilize data decomposition as well as exploratory decomposition in order to achieve significant running time speedup, which scales well with problem size. Our numerical results for openMP parallelization illustrate 6.5 and 5.4 reduction in computation time as compared to the serial versions of the GA and hybrid genetic algorithm (HGA), respectively. Moreover, the parallelization of the GA and HGA result in a speedup of 10.4 and 5.4 , respectively, using master‐slave multithreading.

In this paper, we present an energy efficient two-stage hybrid beamforming for the multiuser massive multiple-input multiple-output (MIMO) downlink system. The millimeter wave (mmWave) technology provides high bandwidth but at the same time suffers from high path-, penetration-, and absorption-losses. In massive MIMO system, the large number of antennas at base-station side compensates the mmWave losses but causes high power consumption in the large number of radio frequency (RF) chains. In this paper, we propose a low complexity orthogonal hybrid beamforming (OHBF) design. We use the Householder reflectors generate the orthogonal analog precoding matrix. It reduces the dimension of the digital precoder as well as the inter-user interference in the beam domain. The proposed Householder based OHBF (HOHBF) scheme provides better energy-efficiency (EE) performance than the Gram-Schmidt based hybrid beamforming design in the real-World ill-conditioned massive MIMO channel. It has been shown that the beam domain orthogonality error remains less than 0.5 with 0.98 probability and the proposed OHBF provides 19.5% improvement in EE at 5 dB SNR as compared to the Gram Schmidt based hybrid beamforming.

Abstract This paper considers the problem of joint timing‐offset and channel estimation for physical‐layer network coding systems operating in frequency‐selective environments. Three different algorithms are investigated for the joint estimation of the channel coefficients and the fractional timing offset. The first algorithm is based on the maximum‐likelihood (ML) criterion assuming baud‐rate (BR) sampling. The second algorithm also assumes BR sampling and is based on the special properties of Zadoff‐Chu training sequences. In the third algorithm, oversampling at double the baud‐rate (DBR) is used and the least‐squares (LS) estimation criterion applied. While the above algorithms assume that the integer timing offset is known, three generalized‐likelihood‐ratio tests (GLRTs) are also considered for integer offset error correction that integrate very well with the proposed estimation algorithms. Our simulation studies show that the DBR‐LS estimator provides the highest estimation accuracy, significantly outperforming both BR estimators and performing very close to the corresponding Cramer–Rao bound. A gain of 4 dB is observed in symbol‐error‐rate performance using the DBR‐LS algorithm. The DBR‐GLRT also provides substantially higher probability of error correction.

In this paper, we consider the problem of joint timing-offset and channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). This problem is solved for generic pulse-shaping filters, taking into account the filter truncation in practical communication and considering both pilot-based and semi-blind estimation strategies. Beginning with pilot-based estimation, we propose a novel Maximum-likelihood joint timing-offset and channel estimator, as well as an alternative estimator based on the special properties of Zadoff-Chu sequences. The first algorithm offers high accuracy, almost overlapping with the Cramer-Rao bound (CRB), while the second offers very low computational complexity. We then develop a semi-blind estimator based on the expectation maximization (EM) framework, exploiting the underlying Hidden Markov Model to apply Baum-Welch forward-backward recursion. The semi-blind CRB is also obtained as an indicator of the best achievable performance. Using simulations, we show that the semi-blind algorithm yields superior accuracy to pilot-based estimation, as well as improved symbol-error-rates and performs very close to the semi-blind CRB. Additionally, a low-complexity approximate EM algorithm is proposed for the case of rectangular pulses. Finally, we consider the possibility of errors in integer-offset estimation and propose pilot-based and semi-blind generalized likelihood ratio test (GLRT) schemes for correcting such errors.

This paper treats the problem of optimal resource allocation over time in a finite-horizon setting, in which the resource become available only sequentially and in incremental values, and the utility function is concave and can freely vary over time. Such resource allocation problems have direct applications in data communication networks (e.g., energy harvesting systems). This problem is studied extensively for special choices of the concave utility function (time invariant and logarithmic) in which case the optimal resource allocation policies are well-understood. This paper treats this problem in its general form and analytically characterizes the structure of the optimal resource allocation policy and devises an algorithm for computing the exact solutions analytically. An observation instrumental to devising the provided algorithm is that there exist time instances at which the available resources are exhausted, with no carryover to future. This algorithm identifies all such instances, which in turn, facilitates breaking the original problem into multiple problems with significantly reduced dimensions. Furthermore, some widely used special cases in which the algorithm takes simpler structures are characterized, and the application to the energy harvesting systems is discussed. Numerical evaluations are provided to assess the key properties of the optimal resource allocation structure and to compare the performance with the generic convex optimization algorithms.