Demand For Connectivity Research Articles

Improving coverage of indoor millimetre wave systems using spherical reflectors

AbstractThe increased demand for user device connectivity has led to spectral congestion which is driving exploration of higher frequency spectrum, such as millimetre wave frequencies. As millimetre wave propagation is predominantly line‐of‐sight, shadowing becomes prevalent in indoor environments due to obstructions from clutter. Accordingly, there is a need to find solutions which can provide additional coverage. In this work, octant‐shaped spherical reflectors are deployed in small office environments with varying degrees of clutter to provide additional ray paths to improve millimetre wave coverage into shadow regions. Cost weightings have been allocated to prioritise regions of high importance and are used to compare reflector deployment strategies. Appropriate positioning of reflectors in the environment is shown to improve coverage into shadow regions in all three environments considered, with placement in the top corners of a room found to be a valid solution. In densely cluttered environments, more than one reflector may be required to provide sufficient coverage. Reducing the reflector radius by 0.1 m has been shown to increase reflected power by up to 6 dB in some cases.

A Review of Agricultural IoT: Challenges, Applications & Future Research Direction

The field of wireless communication has had significant boom in wireless communication, mostly driven by increasing demand for connectivity and technological advancements in low power transceiver development. An Agriculture IoT (AIoT) refers to the technology to enhance agricultural sustainability, productivity, and efficiency. The agricultural IoT uses artificial intelligence to build a network of interconnected systems by fusing digital sensors, IoT devices, advanced analytics, and cloud computing. Agricultural products will be in high demand by 2030 because of the 40% raise in global population. IoT technology is currently utilized in various industries, including agricultural for automation processes. Therefore time is saved, total productivity is increased and decisions are made more effectively and resulting in higher crop yields. AIoT is also being utilized more to keep updated on the sustainability of agricultural supply chains, environment and water usage. Several IoT applications and problems have been covered in this paper. There is also an outline of potential future agricultural research directions.

Discussion on “School is Society” and Its Enlightenment to the Construction of University Environment

The principle of “school is society” indicates that controlling the environment is the only educational method. By transforming the school into a microcosm of society, we can cultivate individuals who possess a social spirit and adaptability, preparing them to thrive in real-world social situations. By analyzing the social performance of colleges and universities in terms of their training objectives for society, research concepts for the development of society, and service consciousness for the improvement of society, as well as the current demand for a proper connection with society, it can be found that taking “school is society” as the guiding ideology for the construction of the university environment tends to be simplified, purified, balanced, broadened, and provided with practical space. This will be beneficial to the development of higher education.

Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights

By adjusting the propagation environment using reconfigurable reflecting elements, intelligent reflecting surfaces (IRSs) have become potential techniques used to improve the efficiency of wireless communication networks. In IRS-assisted communication systems, accurate channel estimation is crucial for optimizing signal transmission and achieving high spectral efficiency. As mobile data traffic continues to surge and the demand for high-capacity and low-latency wireless connectivity grows, IRSs are becoming pivotal technologies in the development of next-generation communication networks. IRSs offer the potential to revolutionize wireless propagation environments, improving network capacity and coverage, particularly in high-frequency wave scenarios where traditional signals encounter obstacles. Amidst this evolving landscape, machine learning (ML) emerges as a powerful tool to harness the full potential of IRS-assisted communication systems, particularly given the escalating computational complexity associated with deploying and operating IRSs in dynamic environments. This paper presents an overview of preliminary results for IRS-assisted communication using recurrent neural networks (RNNs). We first implement single- and double-layer LSTM, BiLSTM, and GRU techniques for an IRS-based communication system. In the next phase, we explore a hybrid approach, combining different RNN techniques, including LSTM-BiLSTM, LSTM-GRU, and BiLSTM-GRU, as well as their reverse configurations. These RNN algorithms were evaluated with respect to bit error rate (BER) and symbol error rate (SER) for IRS-enhanced communication. According to the experimental results, the BiLSTM double-layer model and the BiLSTM-GRU combination demonstrated the highest BER and SER accuracy compared to other approaches.

A study of satellite network based on a comprehensive analysis

Satellite networks have emerged as a crucial component of modern communication systems. Historically, traditional terrestrial communication networks faced limitations in reaching remote and isolated areas. Conversely, Satellite networks possess the ability to provide coverage over vast geographical regions, including areas where laying terrestrial infrastructure is impractical or prohibitively expensive. The development of satellite networks was driven by the increasing demand for global connectivity, enabling seamless communication across various applications such as navigation, remote sensing, weather monitoring, and telecommunications. Moreover, advancements in technology have made satellite networks more efficient and capable. The launch of smaller yet more powerful satellites has reduced costs while increasing data transmission capacity and speed. They also play a vital role in disaster management and emergency response. This paper mainly focuses on analyzing the advantages and disadvantages of satellite networks along with several challenges encountered during their application in different fields. Considering that satellite network technology continues to develop at a significant pace, this study will also discuss future trends and make predictions accordingly. Therefore, through diverse analytical perspectives, this research demonstrates that satellite networks have emerged as critical infrastructure in the modern digital era while highlighting their highly promising prospectsa novel approach often overlooked by existing scientific literaturethus providing ample motivation for scientists to explore this domain.

DRL-assisted task offloading in enhanced time-expanded graph (eTEG)-modeled aerial computing

Space–air–ground integrated networks (SAGINs), categorized under aerial computing (AC), are emerging as a promising hierarchical platform designed to meet the seamless connectivity demands of the forthcoming 6G era. However, efficiently offloading ground tasks to space entities via SAGINs presents unprecedented challenges, primarily due to the mobility of these networks. In response, an enhanced time-expanded graph (eTEG) is proposed to model the dynamic distribution of heterogeneous SAGIN resources, including transmission bandwidth, computation, and storage, thereby optimizing task offloading and resource allocation by employing eTEG. Specifically, this optimization challenge is addressed using a deep reinforcement learning (DRL) approach, aimed at streamlining decision-making for task offloading and resource management to significantly reduce end-to-end delay and enhance network performance. Simulation experiments conducted to evaluate the proposed DRL-based method demonstrate its effectiveness in reducing energy consumption and improving stability, thereby outperforming other methods by achieving reduced delays and satisfying user requirements.

Distributed Regional Photovoltaic Power Prediction Based on Stack Integration Algorithm

With the continuous increase in the proportion of distributed photovoltaic power stations, the demand for photovoltaic power grid connection is becoming more and more urgent, and the requirements for the accuracy of regional distributed photovoltaic power forecasting are also increasing. A distributed regional photovoltaic power prediction model based on a stacked ensemble algorithm is proposed here. This model first uses a graph attention network (GAT) to learn the structural features and relationships between sub-area photovoltaic power stations, dynamically calculating the attention weights of the photovoltaic power stations to capture the global relationships and importance between stations, and selects representative stations for each sub-area. Subsequently, the CNN-LSTM-multi-head attention parallel multi-channel (CNN-LSTM-MHA (PC)) model is used as the basic model to predict representative stations for sub-areas by integrating the advantages of both the CNN and LSTM models. The predicted results are then used as new features for the input data of the meta-model, which finally predicts the photovoltaic power of the large area. Through comparative experiments at different seasons and time scales, this distributed regional approach reduced the MAE metric by a total of 22.85 kW in spring, 17 kW in summer, 30.26 kW in autumn, and 50.62 kW in winter compared with other models.

Effective capacity of STAR-RIS assisted RSMA network with imperfect SIC

The proliferation of various Internet of Everything applications places urgent demand for high energy efficiency, massive connection and wide coverage for the future communication era. Motivated by this, rate-splitting multiple access (RSMA) and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has attracted wide publicity from industry and academia. In this paper, we consider a STAR-RIS assisted two-user RSMA network with imperfect successive interference cancellation, and all channels are modeled as Nakagami-m distribution. By adopting effective capacity (EC) as the metric, we analyze the system performance under certain quality-of-service constraint. Specifically, analytical expressions of EC for a pair of RSMA users are provided. To explore further insights, high signal-to-noise ratio (SNR) conditions are also considered. Furthermore, the effect of some specific elements on system EC performance are analyzed, and some significant conclusions are obtained as follows: (1) As SNR grows, the effective capacities of two users improve, but finally converge to constant at high SNR. (2) The increase in the amount of the STAR-RIS elements can obviously increase the effective capacities. (3) Compared to traditional STAR-RIS assisted non-orthogonal multiple access networks, STAR-RIS assisted RSMA network has distinct advantages to improve system EC performance.

Optical dromions with fractional temporal evolution by enhanced modified tanh expansion approach

AbstractThe current work studies optical dromions that are governed by the nonlinear Schrödinger’s equation. The fractional temporal evolution is considered to suppress the Internet bottleneck that is a growing problem in the rising demand for Internet connectivity across the globe. The model is addressed by the enhanced modified tanh expansion approach. This reveals optical dromions that would emerge with slow evolution and thus introduce traffic signaling effect with optical dromion transmission.

TOWARD ROBUST IN-CAR CONNECTIVITY: SIMULATIVE INSIGHTS INTO THE BEHAVIOUR OF SINGLE PATCH AND 2x2 ARRAY ANTENNAS FOR 2.4 GHZ WI-FI APPLICATIONS

The incessant demand for reliable and robust in-car connectivity drives the evolution of automotive wireless communication. In this pivotal research, we ventured into the realm of 2.4 GHz Wi-Fi applications, emphasising refining antenna designs that focused specifically on the Bio-composite material single patch and the 2x2 array configurations. Our simulations, executed meticulously in CST Microwave Studio, yielded noteworthy findings. The single patch antenna, fortified with a quarter-wave feeding line, delivered an efficiency of 51% and a gain of 2.83 dBi. While promising, its bandwidth limitations beckoned a deeper dive into alternative configurations. Subsequently, our exploration of the 2 x 2 array antenna, enriched with a power divider, stood out as a beacon of improvement. Notably, it exhibited a significant gain elevation, registering at 5.79 dBi. This uptick underscores the 2 x 2 array's superiority over its single-patch counterpart in amplifying in-car Wi-Fi strength and hints at its potential to set a new benchmark in automotive wireless solutions. In synthesis, this research does more than just present empirical data; it offers a roadmap for automotive manufacturers and WiFi solution providers, leading them towards more efficient and robust in-car wireless ecosystems. Beyond its immediate implications, our study lays a strong foundation for future explorations, potentially steering the discourse towards more intricate array antenna designs tailored for vehicular environments.

The mixed blessing of work connectivity demands: Self‐regulatory focus as a moderator

SummaryDue to the rapid development of information and communication technologies, employees increasingly feel pressure to engage in work connectivity behavior after regular working hours (WCBA), giving rise to a novel form of job demand known as WCBA demands. This research examined the upside and downside of WCBA demands. Specifically, we defined WCBA demands as stressors that can be appraised by employees as challenges and/or threats. Challenge appraisals are related to job involvement, which in turn leads to job satisfaction and job performance; threat appraisals are related to job‐induced tension, which in turn leads to family dissatisfaction and family incivility. We also examine regulatory focus as a boundary condition underlying the challenge‐threat paradox of WCBA demands. To test the relationships among our hypotheses, we conducted a daily diary survey study of 176 employee‐supervisor‐spouse triads across five consecutive workdays. The findings show that employees with higher levels of promotion focus were more likely to appraise WCBA demands as challenges, which improved their work outcomes via increased job involvement. Employees with higher levels of prevention focus appraised WCBA demands as threats, which undermined their family outcomes via job‐induced tension. Since these findings assume that WCBA demands act as stressors, we conducted a supplemental qualitative study (N = 19) confirming that employees commonly perceive WCBA demands as stressors. The implications for theory and research are discussed.

Sequential Data-Based Fault Location for Single-Line-to-Ground Fault in a T-Connection Power Line

Due to the demand for temporary rapid grid connection in renewable energy power plants, the topology structure of T-connected power lines has been widely used in the power grid. In this three-terminal system, fault localization is difficult because of traditional impedance-based or traveling wave-based fault localization methods; the three-terminal data should be synchronized and communicated. Since different terminal assets belong to different enterprises, it is actually difficult to maintain good synchronization between them. Therefore, in practical applications, the fault location of T-connected power lines often fails. This article proposes a single terminal fault location method for a T-connection power line to address this issue. It is based on the fact that the local topology of the T-connected power line in the healthy phase remains unchanged during the fault-clearing process. It utilizes the sequential current and voltage data changes generated by the sequential tripping ping emitted by the circuit breaker from different terminals to describe the constant topology of the healthy phase as an equation and calculates the accurate fault location after solving the equation. The Levenberg–Marquardt algorithm was used to calculate fault distance and transition resistance, and the effectiveness of this method was verified through simulation.

Outage Performance of Underlay CR-NOMA-based D2D Communications under Imperfect CSI and SIC

Background: Non-orthogonal multiple access (NOMA) is a promising technique for improving wireless communication performance in the future. In addition to NOMA, cognitive radio (CR) is another technology that can address the issue of limited spectrum availability and meet the increasing demands for wireless connectivity. Methods: This paper focuses on investigating underlay CR-NOMA-based D2D communications. The study assumes a decode-and-forward (DF) mode used in the system, where nearby users (D1, D2) act as helper users to assist distant users (D3, D4). The paper derives the closed-form expressions for outage probability (OP) and throughput at the far users in three scenarios: (1) Perfect successive interference cancellation (SIC) and perfect channel state information (CSI), (2) Imperfect CSI, and (3) Imperfect SIC. Results: In CR-NOMA mode, the results indicate that the performance of user D4 was better than user D3. Additionally, the OP performance of distant users employing CR-NOMA mode surpasses that of users using CR-OMA mode. The optimal power allocation (PA) values are investigated. Conclusion: The presence of imperfect CSI and SIC has an unfavorable influence on the outage performance. Monte Carlo simulations are used to validate the derived analytical expressions.

Human-Nature Connection: Eco-ethical Reading of Louise Gluck’s The Wild Iris

Relationship connotes a power relation of one reaping the benefits at the expense of the other; where connection demands mutual respect and benefits on both sides. In a transactional and utilitarian view, nature has been conceptualized as a source on which humanity consumes. However, ecofeminists believe that there is another way possible that offers mutual coexistence and respect. This brings us to understand nature is neither a source nor only a victim; it is an entity that deserves ethical behavior from human beings. This paper finds a sense of such ethicality in Louise Gluck’s poetry collection ‘The Wild Iris’ (1992) where her use of images reinvents human-nature connectivity through humanity’s realization of its mortality and the natural world’s infinite spiritual renewal and potential for regeneration. Reading her poetry from an ecofeminism perspective by applying the insights of Val Plumwood, this paper claims that environmental ethics is a profit-loss calculation, thus representing humanity’s relationship to nature, where eco-ethical reading demands subtle ways of forging interconnection between humans and nature.

The politics of corporate social responsibility disclosure: Evidence from China

While businesses fluctuate with politics, firms try to find ways to deal with uncertainty. We investigate how political incentives shape corporate social responsibility (CSR) reporting activities using the setting of China. Based on a sample of non-mandatory CSR disclosure firms, we find that firms experiencing political turnover are more inclined to issue CSR reports voluntarily, supporting the political connection demand hypothesis rather than the political cost hypothesis. Such effects are more prominent when the political turnover is unexpected and successors are non-locals. Meanwhile, non-SOEs, not politically connected enterprises, and firms located in low government efficiency areas are more likely to issue CSR reports. Further analysis shows that these firms disclosed CSR information experiencing political turnover fail to ensure the reporting quality but can obtain resources in the future. Overall, we highlight that CSR disclosure is a way for firms to establish political connections with politicians. Our study sheds light on the determinants of voluntary CSR disclosure from a political perspective.

Overloaded Spread Spectrum OFDMA in Outdoor Environment in the Low Interleaving Scenario

The development of mobile communication technology starts with creating or modifying a Radio Access Technique (RAT) to meet user needs and increase capacity. This paper focuses on enhancing spectral efficiency and average throughput using an OFDM-based multiple access method, specifically overloaded spread spectrum OFDMA. The study found that with 24% overloading for BPSK modulation, there is approximately a 30% increase in spectral efficiency in outdoor scenarios. There is a significant improvement using higher modulation as well. This improvement highlights the potential of Overloaded Spread Spectrum OFDMA to optimize mobile communication systems, ensuring better resource utilization and system performance to handle the growing demand for data and connectivity effectively

Simulation and Analysis of the Code Domain NOMA with UFMC for 5G Wireless Networks

In fifth generation (5G) wireless networks, radio access techniques and multi-carrier waveforms play a vital role in meeting the diversified demands of ultra-low latency, massive connectivity, and higher throughput. Multi-access schemes used conventionally in 4G system was Orthogonal Multiple Access (OMA) technique. The OMA techniques suffer from inefficient spectrum utilization, high latency, and supports a limited number of users. Next-generation networks, Non-Orthogonal Multiple Access (NOMA), has a great potential, in which multiple users are simultaneously served using the same time, frequency, or code resource increasing the throughput. Code domain-NOMA (CD-NOMA) is the key technique implemented in the system design where multiple users are distinguished based on unique user-specific spreading codes. The NOMA system could significantly benefit from Universal Filtered Multi-Carrier (UFMC) modulation waveform in terms of flexibility, spectral efficiency, compatibility with Multiple Input Multiple Output (MIMO) technique, and relaxed synchronization requirements. The novel integrated system proposed in the paper is CD-NOMA-UFMC with convolutional codes. The major outcome of the paper is that the combination of UFMC air interface modulation technique with CD-NOMA access method can be the most effective way to meet the growing demands of 5G.

Optimal channel selection for tri-band Wi-Fi in a residential scenario

The growing use of Internet of Things devices and the increasing demand for high-speed, reliable, and secure wireless connectivity pose significant challenges for existing wireless networking solutions in modern smart homes. As such, there is an increasing urgency for the development of advanced and effective wireless technologies that can fulfil the new requirements for interconnected devices and services deployed in households. Tri-band Wi-Fi 6E equipment addresses these needs by reducing the network congestion and enhancing the performance across the 2.4 GHz, 5 GHz, and 6 GHz bands. However, improper band management can lead to frequent interference and network issues. As such, this work introduces a dynamic Wi-Fi link orchestration solution that follows a heuristic model. This approach aims to optimize the network layout and channel allocation based on device metrics, utilizing the EasyMesh specification for simplified network setup and management. The model was implemented and tested in a residential environment network, using EasyMesh and tri-band Wi-Fi 6E devices. Results show the effectiveness of the model in improving network capacity and adapting the links to the current traffic, outperforming the initial and base network configuration.

Dynamic Neural Network-Based Resource Allocation Framework for Latency-Aware and Energy-Efficient Optimization in Wireless Communication Systems

In wireless communication, efficiency is a crucial factor due to the inherent constraints of the radio frequency spectrum and the escalating demand for connectivity. With a limited spectrum available, efficient communication protocols are vital to maximize its utilization and accommodate the increasing number of devices. This study introduces a dynamic resource allocation system for wireless communication, encompassing essential components such as Base Stations (BS), Mobile Devices (MD), and a Centralized Controller (CC). BSs, serving as access points, establish wireless communication with MD within predetermined coverage areas. The CC acts as the neural centre of the system, collecting real-time data from both BSs and MDs to facilitate an adaptive resource allocation process. The comprehensive mechanism involves a scheduling algorithm for optimized data transmission, considering priority and Quality of Service (QoS) requirements, alongside power control mechanisms that dynamically adjust power levels to optimize energy efficiency while maintaining communication quality. MDs communicate with nearby BSs, relaying data transmission information to the CC, which strategically coordinates resources based on the collected data. This integrated system enhances resource utilization, minimizes latency, and improves energy efficiency, collectively contributing to the robustness and reliability of the wireless communication infrastructure.

Throughput and coverage based Base Station–Relay Station deployment for 5G cellular network

SummaryFifth generation cellular networks have high data rates and connectivity demands. The relaying approaches are widely used in cellular networks to improve coverage, user throughput, and capacity at low cost. The increasing data requirements of the mobile network increase energy consumption. Energy efficiency‐based approaches also impact network coverage and throughput. Therefore, developing an energy‐efficient network with optimal coverage and throughput is considered a challenging issue. It can be resolved with optimal deployment of Base Station (BS), Relay Station (RS), and minimizing power consumption. In this research, a joint clustering‐based deployment technique is proposed for BS–RS deployment by considering the network throughput and coverage ratio. After enabling optimal BS–RS deployment, the network traffic is estimated with an On‐demand real‐time traffic estimation framework (ODTE). It estimates user association values using Beacon frames. In addition, the Relay Assisted Base station Power Scheduling (RABPS) approach to minimize power consumption is included. Based on Erlang's probability, the RABPS algorithm is enhanced with sleep mode activation during off‐peak hours. The proposed 5G network ensures uninterrupted connectivity and energy efficiency with varying time slots of power scheduling. The throughput performance of the clustering‐based approach is increased with a coverage ratio of 88%. The simulation results show the superiority of the proposed 5G BS–RS deployment and power scheduling in terms of throughput, coverage ratio, and power consumption.