ABSTRACT An intuitive investigation into the design of a compact planar circularly polarised (CP) metasurface antenna (MA) for futuristic mid-band 6 G Wireless cellular networks has been proposed. The proposed design encompasses a simple, customary, and innovative approach for asserting a symmetrically rotated circularly polarised generator (SRCPG) for producing CP radiated waves, along with a top loading of a periodically meta-unit radiating cell which serves as an actuator for overall steady performance. The SRCPG along with MA meta-cells executes dominant magnetic coupling and serves simultaneously an impedance transformer and an effective radiator inducing wideband resonance with stable radiated CP waves (dominant LHCP). The inherent CP intuitions of periodic meta-cells are explored via modal and surface current investigations to ascertain bandwidth and radiation potential characteristics. A 40 mm × 40 mm fabricated prototype antenna has been validated experimentally to operate from (5.25–8.21) GHz with 44% 10-dB bandwidth, 38.23% 3-dB axial ratio bandwidth from (5.5–8.1) GHz, and realised peak gain of 8.64 dBic. The performance statistics with stable performance demonstrate its potential in mid-band 6 G wireless cellular applications.

ABSTRACT This work presents the design and simulation of a peacock eye‐shaped antenna, developed to address the global bandwidth shortage in wireless cellular networks, particularly in 3G, 4G, and 5G communication systems. The antenna aims to mitigate key challenges such as the high propagation loss at millimeter‐wave frequencies, global spectrum congestion, and the need for miniaturization without compromising performance. To overcome these issues, the design incorporates high‐gain, beam‐steerable antenna arrays and focuses on enhancing radiation efficiency and gain across a wide frequency range. Additionally, challenges in maintaining compactness while achieving broadband performance are addressed using design techniques like slot integration, antenna shape modification, and partial ground removal. A bio‐inspired approach, modeled after natural structures such as the peacock's eye, is adopted to simplify the development process and improve design efficiency. The proposed antenna operates over the super high‐frequency range of 2–30 GHz—covering the S band, ISM band, and parts of the microwave spectrum—and is fabricated on an FR4 substrate with compact dimensions of 50 × 30 × 0.8 mm 3 . The final design achieves a wide bandwidth of 28 GHz, a peak gain of 8.5 dBi, and strong directional field strength, making it highly suitable for next‐generation wireless and mobile communication applications.

Ultra dense network (UDN) and reconfigurable intelligent surface (RIS) are two latest technologies in encountering the increasing demands for network capacity and quality of service in wireless cellular networks. UDN is created by densely deploying femtocells in macrocells area. It causes complex interferences because distances among femtocells are likely very close. RIS provides solution in regulating the reflection of the signal emitted from the transmitter to the receiver to resolve the obstacles. However, RIS reflects the interference signals as well causing more complex interference problems. This paper proposes a solution using clustering method as interference management in RIS-aided UDN network. By clustering method, nearby femtocells are grouped and allocated different frequency channels among femtocells in a cluster. The performance of two systems–the baseline system and the one employing a clustering method–is evaluated based on signal to interference plus noise ratio (SINR), throughput, and bit error rate (BER). Simulation results indicate that SINR and throughput improved by 1.57% and 1.73%, respectively. Meanwhile, the BER for the baseline system is 5.78×10-8 and decreased when applying the proposed method system with a value of 2.26×10-8. The proposed clustering method is promising to confront the interference problems.

Quality of service (QoS) of wireless cellular networks affect due to more power consumption, maximum bit error rate (BER), minimum throughput and improper resource allocation. Improvement in QoS can be done by reducing power consumption, BER and enhancing throughput. Hence there is a need to address the approaches for reduction in power consumption, BER, enhancement in throughput and proper resource allocation through different schemes. In this paper grey wolf optimization (GWO) technique is investigated with different database functions and Its outcome is contrasted with alternative methods like particle swarm optimization (PSO) and genetic algorithm (GA), It is evident that the GWO algorithm performs exceptionally well in terms of BER and power consumption minimization than the other techniques. Hence the QoS of the wireless cellular network will not affect due to minimization of the BER and power consumption through our proposed scheme.

Resilience Enhancement for Electricity and Cellular Wireless Networks Using Mobile Energy Storage Systems

The increasing reliance on digital infrastructure in the 5 G era necessitates highly reliable wireless cellular networks. To this end, modeling the complex network resilience and reliability is crucial, especially in the face of temporal stochasticity and spatial heterogeneity. A primary challenge in evaluating network resilience arises from the intricate multi-fold geometry associated with irregularly deployed cellular networks, defined as the Multi-fold Coverage Evaluation (MCE) problem. This challenge results in the Challenges of Network Geometry (CNG) using existing formulation and modeling approaches. To address this issue, we propose a novel method called the Probabilistic Geometrical Coverage Model (PGCM). The PGCM employs advanced computational geometry and stochastic field techniques to effectively tackle the MCE problem and overcome the difficulties associated with CNG. To facilitate practical implementation, we introduce a proportional hazard model using a stochastic sequences method. The method allows us to track the reliability of base stations using daily alarm streams. Furthermore, to ensure compliance with privacy regulations, a geostatic extrapolation is implemented to estimate the spatiotemporal field of user demands. It helps in assessing service resilience and reliability, capturing the dynamics of heterogeneous regions even during disruptive events. Through testing on real-world wireless cellular networks, our approach demonstrates its effectiveness in enabling timely evaluations of large networks. Our approach underscores the critical role of resilience in achieving high service reliability and provides valuable insights for privacy-respecting network performance modeling and monitoring.

<i>Objective</i> - To investigate, analyze and optimize (where needed) the properties and predictive analyses of selected 5G Mobile wireless network parameters (i.e. Signal to interference noise ratio (<I>SINR</I>) and Throughput as measures of network performance) and Interference conditions in the presence of building obstacles; using the novel approach of combining signal data and visual data in wireless communications. <i>Methods</i>- Using a sample set (i.e., 200 data points) of real life 5G Outdoor Micro cellular tests data and urban building image datasets from validated open source data stores; experimental, investigative and comparative analyses were carried out using the novel approach of combining signal data and visual data using Machine Learning (i.e. Computer Vision) Hybrid deep learning artificial intelligence CNN-based model (i.e., High performance CNN), analytical and mathematical optimization algorithms. The key idea is to leverage camera imagery and Machine Learning (Computer Vision) to successfully predict and analyze network parameters like <I>SINR</I>, Throughput and amount of Interference in the presence of signal obstacles which usually attenuate received signals aperiodically. Additionally obstacle related losses were analysed and network parameter optimization was also demonstrated. <i>Results</i> - The predictive analyses in the presence of obstacles (i.e. concrete buildings) of selected 5G wireless network parameters of <I>SINR</I> and Throughput were carried out successfully using the Hybrid High performance CNN model (HP CNN); with the model showing excellent efficiency by using lesser resources and image datasets from a different environment. Furthermore, the analytical and predictive analyses of a representation of the user interference (i.e. <I>I/PG</I>) in the presence of obstacles were also successfully carried out, and a new OPL algorithm was also proposed in relation to important user obstacle penetration losses. Additionally, the 5G network parameter (i.e. <I>SINR</I>) was mathematically optimized with reference to minimal interference as a demonstration of being an effective tool for engineers and network designers to analytically tune and manage network performance in subsystems and systems more efficiently. <i>Conclusions</i> - This work and diverse related works being carried out; gives no doubt that this novel hybrid intelligent approach presents great possibilities and capabilities for the modern wireless communications field and associated technologies for now and in the future; and its a key approach to autonomous, more efficient network performance management and AI-driven network parameter, attenuation, and interference management.

A-Gamba: An Adaptive Graph-Mamba Model for Traffic Prediction in Wireless Cellular Networks

In traditional telecommunications networks, introducing new functions or processes, especially with diverse approaches to quality assurance, often requires fundamental changes to the architecture and configuration of existing networks, which will lead to hardware modifications. Consequently, such changes usually faced widespread resistance from operators due to the significant costs involved. A viable solution that gained widespread popularity in the late first decade of this century is the software-centric redesign of network functions to facilitate centralized management, reduce expansion and update costs, and enhance the overall efficiency of the system. Alongside extensive academic research in this direction, this solution is now significantly implemented in the industry, particularly in fifth-generation communications and beyond. The diverse and potentially conflicting requirements of new applications in modern wireless telecommunications present a significant challenge in maintaining connectivity and coherence among various blocks of an integrated network to meet the quality needs of diverse applications. One proposed solution in this field, based on the approach of software-defined networks, is network slicing. The main concept of slicing involves sharing various physical network resources over virtual networks with centralized management and predefined quality of service requirements. For this purpose, different network slices must be managed and configured by a central control unit. In the cellular wireless network studied in this research, this unit is introduced as the mapping layer. This layer monitors its serviced network and manages the allocation of radio resources to slices based on a specific method to meet the service needs of each slice. Following an initial introduction, the proposed idea is compared with some existing and new methods. Simulation results from this research indicate that the proposed slicing method performs better in terms of meeting key performance indicators compared to other methods reviewed, especially when the demand for resources exceeds the capacity allocated to a slice, relative to the agreed service level.

The popularity of coastal imaging products has continued to grow in recent years due to decreasing costs and increasing image resolution and spatial coverage. Within USACE, the CorpsCam initiative was developed to help monitor coastal areas of interest and consists of three tiers: the research grade Argus system, a commercial off the shelf trail camera, and the crowd-sourced citizen science CoastSnap (Harley and Kinsela, 2022). Images captured at all tiers are transmitted over wireless cellular network to an internal server for processing and analysis. CorpsCam projects have focused on coastal monitoring and exploring new ways to quantify extracted features such as shoreline change, surface current measurements, and estimated bathymetry in the nearshore. This paper will investigate the accuracies of using low-cost cameras at various study sites to extract and quantify water level from imagery.

Miniature circular patch antenna design using defected ground structure based on complementary split-ring resonator for 5 G wireless cellular networks

In this research paper, an 8x8 antenna Massive MIMO array, with diamond ring slots elements for 5G and 6G cellular networks has been designed using Computer Simulation Technology (CST) Microwave Studio Software. The configuration of the design consisting of eight double-fed diamond ring slot antenna elements placed at different corners of the printed circuit board (PCB). A low cast FR-4 (lossy) dielectric with an overall dimension of 75x150 mm2 is used as the designed substrate. Moreover, a common ground of copper pure (lossy) metal is also used. For antenna elements copper annealed material is used to get better results in very low cost and for the purpose of easy fabrication. The antenna elements are fed by 50 ohms microstrip feedline. The lower cutoff frequency of the array is almost 3 GHz while the upper cutoff frequency is almost 4.4 GHz. So, the overall impedance bandwidth i.e. at S11= -10 dB, of the design is approximately 1200 MHz which is very efficient for future 5G and 6G cellular communication networks. The characteristics impedance and operation impedance of the design are very close to each other i.e. 50 ohms which shows very minute impedance mismatching. The value of the Scattering Parameter or S-parameter in the given bandwidth is around -15 dB which is an optimum value for this array. The value of Voltage Standing Wave Ratio (VSWR) also lies the threshold value of 2. The radiation efficiency of the array is almost 80%, while its total efficiency is equal to 75%. The resonance frequency of the array is 4 GHz which is very efficient and reliable for future 5G and 6G cellular wireless networks. Farfield directivity of the array is 4.4 dBi while farfield gain of the array is 3.44 dBi. This array can easily be used for 5G applications. For the combined and collective pattern of the overall structure the post processing is applied in CST to achieve a narrow beam in particular direction. The resultant intense beam can be sent in any particular direction using the beamforming technique.

Energy Minimization for Distributed Microservice-Aware Wireless Cellular Networks

System utility maximization scheme for wireless cellular networks

Given the increasing global emphasis on sustainable energy usage and the rising energy demands of cellular wireless networks, this work seeks an optimal short-term, continuous-time power-procurement schedule to minimize operating expenditure and the carbon footprint of cellular wireless networks equipped with energy-storage capacity, and hybrid energy systems comprising uncertain renewable energy sources. Despite the stochastic nature of wireless fading channels, the network operator must ensure a certain quality-of-service (QoS) constraint with high probability. This probabilistic constraint prevents using the dynamic programming principle to solve the stochastic optimal control problem. This work introduces a novel time-continuous Lagrangian relaxation approach tailored for real-time, near-optimal energy procurement in cellular networks, overcoming tractability problems associated with the probabilistic QoS constraint. The numerical solution procedure includes an efficient upwind finite-difference solver for the Hamilton-Jacobi-Bellman equation corresponding to the relaxed problem, and an effective combination of the limited memory bundle method (LMBM) for handling nonsmooth optimization and the stochastic subgradient method (SSM) to navigate the stochasticity of the dual problem. Numerical results, based on the German power system and daily cellular traffic data, demonstrate the computational efficiency of the proposed numerical approach, providing a near-optimal policy in a practical timeframe.

Application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

This study aims to enhance handoff performance in mobile communication networks by addressing the limitations of conventional systems through the implementation of the Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional handoff methods have been constrained by a modest success rate of 80%, leading to unreliable connections during user mobility. These challenges arise from factors such as weak signal strength, high network congestion, and suboptimal mobility management. To tackle these issues, a customised ANFIS model was developed using MATLAB/Simulink, introducing a dynamic and intelligent approach to the handoff decision-making process. The results demonstrated significant performance improvements. The ANFIS-based system achieved a 95% handoff success rate, a substantial increase from the conventional method. Quantitative enhancements included a rise in signal-to-noise ratio (SNR) to 25 dB, an improvement in signal strength to an average of -70 dBm, and an increase in system efficiency to 95%. Moreover, the ANFIS approach facilitated a growth in network capacity, allowing concurrent users to increase from 100 to 150. Quality of Service (QoS) metrics, previously rated as poor, were elevated to good, ensuring a seamless user experience. These findings underscore the transformative potential of ANFIS in managing handoff strategies, offering more reliable connectivity and improved performance in demanding wireless environments. The study advocates policy adjustments to prioritise the adoption of intelligent systems like ANFIS to meet the growing requirements of modern mobile communication networks.

ABSTRACTThe wireless cellular network (WCN) provides services to many users with limited radio resources. In WCN, resource allocation is important to provide the desired quality of service (QoS). Among the two possible types of calls in WCN, hand‐off calls (HCs) have higher priority than new calls (NCs). In this paper, we developed a mathematical model to estimate call blocking using an optimal number of guard channel (GC) for HCs by considering target hand‐off call dropping probability (HCDP). We named this model the optimal guard channel reservation (OCR) policy. This analytical model assumes the long‐term average of the traffic parameters and so is far from making any correlation with the operation of the existing system. In real, the traffic parameters are dynamic. Further, we proposed an optimal adaptive guard channel reservation (OACR) policy to obtain optimal performance. It adjusts the GCs dynamically w.r.t the traffic conditions to keep HCDP below the target. We have implemented the OACR scheme by using a test bed. Finally, we have applied both mathematical model (i.e., OCR policy) and OACR policy to a wireless cellular network, that is, Global System for Mobile Communication (GSM) 900 system.

The fifth-generation technology standard (5G) is the cellular technology standard of this decade and its adoption leaves room for research and disclosure of new insights. 5G demands specific skillsets for the workforce to cope with its unprecedented use cases. The rapid progress of technology in various industries necessitates a constant effort from workers to acquire the latest skills demanded by the tech sector. The successful implementation of 5G hinges on the presence of competent individuals who can propel its progress. Most of the existing works related to 5G explore this technology from a multitude of applied and industrial viewpoints, but very few of them take a rigorous look at the 5G competencies associated with talent development. A competency model will help shape the required educational and training activities for preparing the 5G workforce, thereby improving workforce planning and performance in industrial settings. This study has opted to utilize the Fuzzy Delphi Method (FDM) to investigate and evaluate the perspectives of a group of experts, with the aim of proposing a 5G competency model. Based on the findings of this study, a model consisting of 46 elements under three categories is presented for utilization by any contingent of 5G. This competency model identifies, assesses, and introduces the necessary competencies, knowledge, and attributes for effective performance in a 5G-related job role in an industrial environment, guiding hiring, training, and development. Companies and academic institutions may utilize the suggested competency model in the real world to create job descriptions for 5G positions and to develop curriculum based on competencies. Such a model can be extended beyond the scope of 5G and lay the foundation of future wireless cellular network competency models, such as 6G competency models, by being refined and revised.

SummaryOn‐demand manufacturing in Industry 4.0 requires flexibility of the networks which can be provided with the fifth generation (5G) of mobile communications wireless connectivity. A key component in the efficient utilization of the radio resources in a manufacturing scenario is network resource management (NRM). We show how NRM can be automated with artificial intelligence (AI). We introduce several futuristic industrial use cases that require AI in various parts of the process. We analyze the AI components' benefits and disadvantages in several deployment scenarios. The findings can be used by business stakeholders interested in deploying the 5G cellular wireless network to choose the best NRM and AI implementation strategy for a particular use case. We show that there are many viable options for the AI component in the process automation, but the cost of AI has to be considered in all cases. Also, we point out that an essential component, the standardized information flow on the status of the productivity key performance indicators (KPIs), is needed for the successful deployment and application of the 5G AI.