Power substations play a crucial role in ensuring the reliable transmission of electricity to residential and commercial establishments. This paper addresses the critical issue of insulator fault detection in electric substations, emphasizing the importance of timely identification to prevent accidents. Conventional inspection methods relying on manual intervention pose high risk, especially in high-voltage substations. To overcome this, infra-red (IR) thermal cameras are employed, but human analysis of IR thermal images is subjective and time-consuming. The current paper develops a computer vision system uses advanced segmentation algorithms to automatically detect and categorize defects in insulators based on Infrared Thermography (IRT) images. The proposed system combines unsupervised clustering for efficient training with supervised learning for accurate classification. In particular, it first employs clustering techniques for unsupervised learning to reduce human labeling needs. Then, in the supervised learning phase, a Gaussian kernel support vector machine (SVM) algorithm classifies various insulator defects using extracted features. The trained algorithm utilizes the extracted characteristics to effectively identify and classify several fault types in thermal images of insulators. The system’s efficiency in quickly identifying faults enables timely intervention, preventing potential power outages and safety hazards.

Massive surge in mobile data traffic, high power consumption, and limited spectrum availability are the major challenges of existing cellular networks. Industries and academia have conducted many studies and proposed solutions to meet the exploding user demand and minimize the network operators’ capital and operational expenditures. The cloud radio access network (CRAN) system is a paradigm shift for cellular networks due to its central processing, which is also considered a potential candidate for 5G and beyond fifth generation (5G) networks. This paper presents a comprehensive overview of the state-of-the-art CRAN architecture and critically analyzes its performance metrics. Moreover, it provides the first holistic insight into the recent advances in CRAN architecture; integrating CRAN architecture with state-of-the-art technologies such as massive multiple-input multiple output (MIMO) systems by equipping the transmitter and the receiver with multiple antennas, intelligent reflecting surfaces, unmanned aerial vehicles, integrated sensing and communication, rate splitting method, and energy harvesting. Further, the related research challenges are also discussed, proposing future directions.

Massive data traffic over capacity-limited fronthaul link of a cloud radio access network (CRAN) poses a severe challenge in modern wireless systems. We propose a novel CRAN architecture to address this challenge that leverages a large array of antennas or massive MIMO at the baseband unit (BBU) to communicate with a set of distributed remote radio heads (RRHs) and subsequently serve multiple user equipment (UEs) over the access link. Transmission of data from massive MIMO at BBU takes place over a millimeter wave (mmWave) channel to exploit large bandwidth. The proposed architecture leverages a substantial array gain of massive MIMO in conjunction with a large idle mmWave frequency spectrum that can deal with a wide range of throughput requirements. In addition, a data compression strategy is designed by finding an optimal quantization noise covariance matrix across all the RRHs to handle the fronthaul traffic load. To this end, an optimization problem to maximize the sum rate of the UEs is thus formulated subject to constraints on fronthaul capacity, data compression, and total transmit powers at the BBU and RRHs. To solve this non-convex and intractable problem, a path following an iterative algorithm based on the difference of convex (DC) programming is used. The performance of the proposed architecture is evaluated in comparison with nontrivial benchmark schemes/architectures. Simulation results reveal that the proposed architecture can significantly improve the throughput of a CRAN by overcoming the fronthaul bottleneck by using massive MIMO over the mmWave channel.

Falls are a leading cause of mortality among individuals aged 65 and above, making timely fall detection alarms essential for preventing fatalities. Contactless radio-frequency (RF) technology for fall detection has gained traction due to its wide coverage and privacy-preserving features. However, existing RF-based systems often assume falls create predictable RF signal patterns, which can be problematic, especially among visually impaired people (VIP), who cannot detect environmental changes. To overcome this challenge, we propose an innovative approach that focuses on recognising normal, repeatable human activities and detecting falls as deviations from these patterns. Our prototype, developed using commercial UWB Xethru radar, was tested on human subjects, including VIPs. The results demonstrated a classification accuracy of 98.8% within a 1.5-meter range in indoor environments, proving our system’s high reliability and adaptability for real-time fall detection. This approach provides a more dependable solution for protecting the elderly, especially those with visual impairments, from fall-related dangers.

Cloud Radio Access Networks (CRAN) face a critical challenge due to the limited capacity of fronthaul links overwhelmed by massive data transmissions. This paper proposes a novel CRAN design that effectively tackles this challenge. Our approach combines three key elements: (1) Massive MIMO at the baseband unit to leverage large array gain and interference suppression; (2) a novel simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) that can both transmit and reflect signals concurrently, improving fronthaul capacity through energy splitting technique by enabling communication with remote radio heads serving multiple user equipments; and (3) a data compression technique by optimizing the quantization noise covariance matrix across remote radio heads, significantly reducing the fronthaul traffic load. We formulate a problem to maximize the overall network sum-rate by jointly optimizing transmit power, fronthaul capacity, beamforming vectors at RRHs, data compression, and STAR-RIS transmission-reflection coefficients. To address the nonconvexity of the resulting joint optimization problem, successive convexification along with alternating optimization technique are used to develop an iterative algorithm. Simulations demonstrate that our STAR-RIS-aided CRAN design surpasses conventional reflecting-only RIS aided CRAN by providing full-space coverage and thus offering more degrees-of-freedom compared to traditional RIS.