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Cellular network scheduling is crucial for wireless deployments like 4G, 5G, and 6G and is a challenging resource allocation task performed by the scheduler located at the base stations. The scheduler must balance two critical metrics, throughput and fairness, which often conflict, as maximizing throughput favors users with better channel conditions, while ensuring fairness requires allocating resources to those with poorer channel conditions. The proportional fairness metric is a prominent scheduling approach that aims to balance these competing metrics with minimal compromise. The common strategy to attain proportional fairness relies on a greedy approach in which each resource block is allocated to the user who maximizes the proportional fairness criterion. With such a strategy, the scheduler can ensure that the resources allocated to the users at each time instance maximize the proportional fairness metric. However, users can usually tolerate some delay and are willing to accept temporary fairness imbalances if they ultimately improve their performance, provided that the fairness criterion is maintained over time. In this paper, we propose a new scheduler that uses reinforcement learning to enhance proportional fairness. The suggested scheduler considers both current and predicted future channel conditions for each user, aiming to maximize the proportional fairness criterion over a set of predefined periodic time epochs. Specifically, by learning patterns in channel fluctuations, our reinforcement learning-based scheduler allocates each resource block not to the user who maximizes the instantaneous proportional fairness metric, but to the user who maximizes the expected proportional fairness metric at the end of the current time epoch. This approach achieves an improved balance between throughput and fairness across multiple slots. Simulations demonstrate that our approach outperforms standard proportional fairness scheduling. We further implemented the proposed scheme on a live 4G eNodeB station and observed similar gains. [ABSTRACT FROM AUTHOR]

The O-RAN architecture allows for unprecedented flexibility in Radio Access Networks (RANs). O-RAN's components designed to control RANs, such as RAN Intelligent Controllers (RICs), places intelligence at the center of the management and orchestration of 5 G/6 G cellular networks. RICs run applications based on machine learning models, which require massive RAN data for training. Nonetheless, building testbeds to collect these data is challenging since RANs use expensive hardware and operate under a licensed spectrum, usually not available for the academy. Even though producing RAN datasets is challenging, some research groups have already made their data available. In this paper, we survey the primary public datasets available online that are considered in O-RAN papers. We identify the main characteristics and purpose of each dataset, contributing with a complement to their documentation. Also, we empirically showcase the viability of using publicly available datasets for machine learning applications within the O-RAN domain, such as spectrum and traffic classification. [ABSTRACT FROM AUTHOR]

Summary Owing to the exponential increase in wireless network services and bandwidth requirements, sharing the radio spectrum among multiple network operators seems inevitable. In wireless networks, enabling efficient spectrum sharing for resource allocation is quite challenging due to several random factors, especially in multi‐operator spectrum sharing. While spectrum sensing can be useful in spectrum‐sharing networks, the chance of collision exists due to the inherent unreliability of wireless networks, making operators reluctant to use sensing‐based mechanisms for spectrum sharing. To circumvent these issues, we utilize an alternative approach, whereby we propose an efficient spectrum‐sharing mechanism leveraging a spectrum coordinator (SC) in a multi‐operator spectrum‐sharing scenario assisted by deep learning (DL). In our proposed scheme, before the beginning of each timeslot, the base station of each operator transmits the number of required resources based on the number of packets in the base station's queue to SC. In addition, base stations also transmit the list of available channels to SC. After gathering information from all base stations, SC distributes this collected information to all the base stations. Each base station then utilizes the DL‐based spectrum‐sharing algorithm and computes the number of resources it can use based on the number of packets in its queue and the number of packets in the queues of other operators. Furthermore, by leveraging DL, each operator also computes the cost it must pay to other operators for using their resources. We evaluate the performance of the proposed network through extensive simulations. It is shown that the proposed DL‐based spectrum‐sharing mechanism outperforms the conventional spectrum allocation scheme, thus paving the way for more dynamic and efficient multi‐operator spectrum sharing. [ABSTRACT FROM AUTHOR]

Underlying cellular networks, device-to-device (D2D) communications are a practical network technology that can increase power efficiency and spectrum usage for close-proximity wireless services and applications. However, D2D link interference, when sharing resources with cellular users (CUs), poses a major challenge in such distribution situations. In this research, we primarily utilize wireless channel data that exhibits slowly shifting large-scale fading to conduct spectrum sharing and power allocation. The overall ergodic capacity of all cellular user equipment (CUE) links is initially considered as the optimization target in order to maximize the overall throughput of CUE links while ensuring the reliability of each D2D link. Then, the expansion of the minimum ergodic capacity is measured to ensure a more consistent capacity performance across all CUE links. We utilized algorithms that are resilient to channel fluctuations and produce optimal resource allocation. We use MATLAB, and the computer simulation validates its intended performance. [ABSTRACT FROM AUTHOR]

Motives: Communication is the basis of existence of modern civilization. Nowadays, communication is part of the most intensive innovation processes. The level of formation of communication networks largely determines both the preconditions and the current development of regions. Aim: The aim of the study was to analyze the interdependencies between the level of socio-economic development of the examined areas and the formation of communication networks on the example of local territorial units of Western Ukraine. Results: The correlation analysis revealed significant links between communication networks and regional development. It is not always possible to determine the exact cause and effect in a specific case. The results of this study indicate that the socio-economic development of Western Ukraine has a more significant impact on the functioning of communication networks than the communication networks have on the socio-economic development. Like other natural and socio-economic resources, the presence of communication networks is a prerequisite for the development of the region. The emergence, perception and widespread use of new communications depend on several factors, one of which is the socio-economic development of a given area. [ABSTRACT FROM AUTHOR]

The emergence of Fifth Generation (5G) technology has marked a significant advancement in communication networks offering unique speed, capacity, and reliability. However, the successful deployment of 5G networks in urban environments requires careful consideration of various factors, including frequency band selection and infrastructure planning. Since Iraq has not yet witnessed the 5G network implementation, studying how the frequency bands vary and affect performance will provide insights into a smoother migration toward the next-generation cellular systems. This research aims to investigate the feasibility and potential benefits of deploying 5G wireless networks in Basra City utilizing a diverse range of frequency bands at 1.8, 2.1, and 3.6 GHz. A cellular network was designed and analysed to identify the most suitable frequency bands for 5G deployment in this Iraqi city as a case study which can be then generalized for other terrain-like regions. The site locations, antenna heights, transmission power, and antenna down-tilt degree were optimized through three design scenarios. Furthermore, four measurement themes were adopted to evaluate network performance as coverages, power levels, Carrier-to-Interference (C/I), and power density measurement themes. The results showed that despite the network with 3.6 GHz band slightly recorded lower levels of power density, the 1.8 GHz band proved its outperformance compared with the other two bands in terms of coverage, capacity, and C/I. Besides, the 1.8 GHz based design served almost 100 km2 target area with excellent coverage. Consequently, the lower frequency 5G bands seemed to better accommodate users’ demands in Basra City with the outlined constraints.

These days, 5G mobile technology highly influences the wireless communication process, which is more responsive than the other generation. However, the 5G communication process handles the speed and latency issues while transmitting data over the network. Especially in rural areas, most people face this speed and latency problem while connecting to the network. Therefore, the network uses massive amounts of data to analyse this problem by integrating 5G with the Internet of things (IoT). The IoT-based data transmission and collection helps manage the data capacity, connection, and transmission rate. Therefore, this study uses the novel secured narrowband IoT framework for 5G communication and operational goals based on their position in the market. The 5G network uses business connectivity and access information to manage the data transmission. The network uses cellular bandwidth and IoT devices to connect more during this process. According to the connection, the information is transmitted with the minimum delay. Thus, experimental results have proposed these parameters: an accuracy ratio of 92.5%, a security ratio of 94.4%, a reliability ratio of 93.5%, a latency ratio of 91.8%, a cost-effective ratio of 20.5%, and a throughput ratio of 25.4% compared to other methods.

Radio sensing is a key envisioned feature for next-generation wireless systems, allowing the use of communication infrastructure and air interface components to also perceive the environment. Enabling efficient use of radio access network (RAN) resources to meet various communication and sensing use case requirements is a primary goal in such systems. This article bridges the gap among RAN architecture requirements, along with radio and signal processing techniques to realize the integration of radar sensing into next-generation cellular systems, e.g., the open RAN (O-RAN) framework. Motivated by the O-RAN disaggregated architecture, this article introduces a framework to analyze sensing processing from the perspective of the radio unit (RU)–distributed unit (DU) functional split and the fronthaul throughput requirements of interfaces between RUs and DUs. Accordingly, the feasibility of adopting sensing in current architectures while meeting fronthaul throughput limitations is demonstrated. Further, it is shown that receiver (Rx) processing techniques tailored for cellular system (e.g., to enable wideband sensing over disjoint bandwidths) can efficiently fit into the DU processing framework. Evaluation results revealing detection performance gains are also presented. [ABSTRACT FROM AUTHOR]

As wireless communication traffic experiences rapid growth, the carbon emissions caused by the communication industry are also on the rise. To achieve “carbon neutrality”, researchers are considering the use of renewable energy sources to power cellular networks, thereby reducing carbon emissions. However, a challenge arises when using renewable energy, specifically owing to the unpredictable nature of both the energy consumption of the cellular network and the power generation from renewable sources. This inconsistency results in low renewable energy utilization and reduced carbon efficiency. Herein, we construct a carbon efficiency model of solar-powered cellular networks using practical data from solar radiation. We propose a mechanism that alternately optimizes the performance of the renewable energy network and the cellular network. This approach is based on convex optimization theory and the Dinkelback algorithm, and it leads to the design of a carbon efficiency optimization algorithm. This algorithm aims to improve the carbon efficiency of cellular networks and reduce their carbon emissions. Simulation results demonstrate that our optimization scheme yields a maximum improvement of 2.56 × 108 bps/g in the carbon efficiency of the cellular network as compared to conventional power allocation schemes such as the traditional water filling method and heuristic energy sharing and charge/discharge algorithms. [ABSTRACT FROM AUTHOR]

Energy efficiency is regarded as a critical concern in modern cellular networks, primarily due to the increasing demand for wireless communication services and the environmental impact associated with energy consumption. In this paper, an innovative approach named ACSO (Adaptive Cell Switch Off) is proposed to optimize energy efficiency in cellular networks. It focusses on leveraging clustering techniques and dynamic CSO (Cell Switch Off) strategies. In this approach, clustering of BSs (Base Station) is performed based on their traffic profiles, and within each cluster, an optimal set of BSs is determined to be switched off at different time intervals. By considering the characteristics of network traffic and spatial distribution, an attempt is made to strike a balance between energy conservation and the maintenance of high QoE (Quality of Experience) for users. Extensive simulations using a real-world dataset are conducted to compare our approach with existing methods, and superior performance is demonstrated in terms of efficient BS selection, algorithm execution time, and user QoE. Additionally, potential future research directions in the area of dynamic CSO are discussed, along with its implications for the development of energy-efficient cellular networks. [ABSTRACT FROM AUTHOR]

Due to the shortage of wireless resources and the emergence of a large number of users, determining how to guarantee the quality-of-service (QoS) requirements of users and make more users work in the same spectrum has become an urgent research topic. In this paper, we study a multi-user MIMO (MU-MIMO) cellular network system model in which cellular users (CUs) share the same spectrum resource with multiple device-to-device (D2D) pairs. To maximize the number of admitted D2D pairs sharing the same spectrum with the CUs, a joint power allocation and channel gain (JPACG) algorithm is proposed. The optimization problem is divided into two steps to be solved. First, the power allocation of CUs without D2D pairs admitted is solved. Then, the optimization problem is transformed into minimizing the interference to CUs when CUs are treated as primary users. The admittance order of D2D pairs is determined by the transmission power and channel gain. The proposed algorithm uses a convex optimization algorithm to solve the problem of power allocation joint interference channel gain in order to maximize the number of admitted D2D pairs under the constraints of the signal-to-interference-plus-noise ratio (SINR) threshold and maximum transmission power. In addition, the effect of the number of admitted D2D pairs on the total sum rate of all users is also analyzed. The simulation results show that the proposed JPACG algorithm can achieve better performance in admitting D2D pairs. [ABSTRACT FROM AUTHOR]

BackgroundHuman mobility behaviour modelling plays an essential role in the understanding and control of the spread of contagious diseases by limiting the contact among individuals, predicting the spatio-temporal evolution of an epidemic and inferring migration patterns. It informs programmatic and policy decisions for effective and efficient intervention. The objective of this research is to assess the human mobility pattern and analyse its implication for malaria disease epidemiology.MethodsIn this study, human mobility patterns in Benishangul-Gumuz and Gambella regions in Western Ethiopia were explored based on a cellular network mobility parameter (e.g., handover rate) via real world data. Anonymized data were retrieved for mobile active users with mobility related information. The data came from anonymous traffic records collected from all the study areas. For each cell, the necessary mobility parameter data per hour, week and month were collected. A scale factor was computed to change the mobility parameter value to the human mobility pattern. Finally, the relative human mobility probability for each scenario was estimated. MapInfo and Matlab softwares were used for visualization and analysis purposes. Hourly travel patterns in the study settings were compared with hourly malaria mosquito vector feeding behaviour.ResultsHeterogeneous human movement patterns were observed in the two regions with some areas showing typically high human mobility. Furthermore, the number of people entering into the two study regions was high during the highest malaria transmission season. Two peaks of hourly human movement, 8:00 to 9:00 and 16:00 to 18:00, emerged in Benishangul-Gumuz region while 8:00 to 10:00 and 16:00 to 18:00 were the peak hourly human mobility time periods in Gambella region. The high human movement in the night especially before midnight in the two regions may increase the risk of getting mosquito bite particularly by early biters depending on malaria linked human behaviour of the population.ConclusionsHigh human mobility was observed both within and outside the two regions. The population influx and efflux in these two regions is considerably high. This may specifically challenge the transition from malaria control to elimination. The daily mobility pattern is worth considering in the context of malaria transmission. In line with this malaria related behavioural patterns of humans need to be properly addressed.

The growing complexity of cellular networks makes it harder for network operators to control and manage the system. To ease the management and automatically detect network problems, unsupervised techniques have been put to use. This work proposes a novel method that combines Multi-Resolution Analysis (MRA) by wavelet transforms and unsupervised clustering with pre-initialized Gaussian Mixture Models (GMMs) for the totally unsupervised grouping of cellular network behaviors using different metrics. The application of multi-resolution decomposition, allows the much simpler clustering technique to take into account temporal information that would require of a much complex method otherwise, being useful for cluster analysis by experts as different duration issues are now segregated and automatically labeled. The generated labels are indicative of the intensity and duration of the anomalies, such labeling can be linguistic or visual, providing faster issue identification. The proposed approach has been tested with real network data, successfully separating different behaviors analyzed in the evaluation section of the manuscript.

We consider the problem of jointly enhancing the network throughput, minimizing energy consumption, and improving the network coverage of mobile networks. The problem is cast as a reinforcement learning (RL) problem. The reward function accounts for the joint optimization of throughput, energy consumption, and coverage (through the number of uncovered users); our RL framework allows the network operator to assign weights to each of these cost functions based on the operator’s preferences. Moreover, the state is defined by key performance indicators (KPIs) that are readily available on the network operator side. Finally, the action space for the RL agent comprises a hybrid action space, where we have two continuous action elements, namely, cell individual offsets (CIOs) and transmission powers, and one discrete action element, which is switching MIMO ON and OFF. To that end, we propose a new layered RL agent structure to account for the agent hybrid space. We test our proposed RL agent over two scenarios: a simple (proof of concept) scenario and a realistic network scenario. Our results show significant performance gains of the proposed RL agent compared to baseline approaches, such as systems without optimization or RL agents that optimize only one or two parameters.”

The exponential surge in mobile device usage has propelled a steep rise in mobile data demand, particularly with the advent of 5G networks aiming to cater to this burgeoning need. Ensuring top-notch quality of service (QoS) is pivotal in deploying such advanced wireless technologies without compromising base station throughput. Continuous enhancements in established cellular networks like 3G, 4G, and emerging 5G networks remain imperative to uphold satisfactory network performance. This paper comprehensively analyzes 5G network performance across indoor and outdoor environments in Muscat, Oman. The study involved walk tests within three indoor locations and drive tests outdoors, using devices from three mobile providers: Omantel, Ooredoo, and Vodafone. Metrics such as signal quality, data rate, ping, and serving time were assessed. Results indicated the dominance of 4G networks indoors and 5G outdoors across all providers. In the outdoor scenario, the observed data rate for the 5G network did not meet anticipated levels, potentially due to handover issues or other factors affecting signal continuity. Moreover, the measurement data collected within indoor scenarios indicates that approximately 80% of the recorded data stems from 4G networks. As a consequence, this disproportionate reliance on 4G is likely influencing network performance, possibly leading to decreased data rates. While 5G showcased incremental improvements over 4G in some instances, it is still in its nascent phase. Recommendations include continuous network monitoring by operators and augmented 5G base station deployment to heighten QoS by ensuring higher data rates and minimal latency across both indoor and outdoor settings.

Emerging cellular networks integrate diverse technologies like millimeter wave (mmWave) to deliver high capacity but face challenges like blockage sensitivity. Dual connections (DC) enhance throughput but ensuring information freshness (Age-of-Information, AoI) is crucial for real-time applications. This paper proposes a novel approach for AoI optimization in DC cellular networks (4G/5G) with TCP multipath transport. We address the stochastic nature of AoI and channel conditions with a combined random and stochastic optimization approach. Modeling the system as a multi-queue system with LTE and mmWave connections, we propose a difference learning-based algorithm that dynamically selects the optimal queue for each packet. This eliminates the need for statistical information like packet arrival rates, enabling real-time adaptation. Compared to existing methods, our algorithm significantly reduces AoI under various network conditions (over 40%), demonstrating its effectiveness for real-time applications.

Network monitoring is essential for operators to review and optimize network behaviour, and to troubleshoot any issues that arise. With the adoption of unlicensed band and spectrum-sharing technologies, conventional optimization techniques are no longer feasible. As cellular networks grow in complexity, data-driven solutions are becoming increasingly central to ensuring optimal network performance, reliability, and user experience. Recent cellular research has focused on data-driven analysis and optimization to ensure high Quality of Service in a cellular network. However, the biggest challenge for such work is data collection at scale from sophisticated and reliable network monitoring tools. This work bridges a gap in cellular network literature with a thorough review of cellular network monitoring tools. We rigorously test and review eleven applications that are popularly used to collect information on cellular networks. We understand, analyze, and critique their architecture to assess their reliability. We share insights on their performance and ease of use. Most importantly, we analyze the ability of applications to collect accurate cellular network data at scale. We also review recent literature on cellular network monitoring and analysis after carefully selecting 32 papers relevant to the topic.

Unlicensed cellular networks and spectrum-sharing standards assist operators in meeting the ever-increasing demand for mobile data. However, several incumbents are already operational in these frequencies, rendering the wireless environment extremely dynamic and unpredictable. The challenges associated with unlicensed Licensed Assisted Access (LAA) operations in the 5 GHz band and New Radio in Unlicensed (NR-U) in the 6 GHz band are best addressed through a data-driven approach. This requires operator data from current cellular deployments. Further, from an operator’s perspective, the precision and reliability of predictive models must be analyzed before deployment. Counterfactual machine learning is ideal for quantifying causal impact in a dynamic, unlicensed cellular environment. However, the literature lacks a framework that combines data-driven solutions, counterfactual analysis, and conventional optimization. This work contributes a dataset from the LAA networks of three major cellular operators in Chicago consisting of 15 features and 9676 samples. Additionally, it proposes a framework for analyzing the performance of unlicensed networks that leverages machine learning for predictive modeling, employs counterfactual analysis for model explainability and network performance enhancement, and utilizes optimization for validation. We show that operator data is necessary to build reliable prediction models for network throughput, and signal strength, among others. Further, the impact of network parameters is shown to differ in unlicensed and licensed cellular network models. Next, a counterfactual machine learning framework is proposed to explain and analyze the predictive models. The framework proposes counterfactual policies to enhance unlicensed cellular network performance. Finally, we validate the suggested counterfactual policies through joint network optimization.

Abstract Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.

In this paper, the prototypical deployment of a Multiple-Input-Multiple-Output (MIMO) antennas system, denoted as Smart Selective Antennas System (SSAS), aiming at mitigating inter-cell interference effects of cellular networks for in-flight Unmanned Aerial Vehicles (UAVs), is discussed. In detail, the proposed SSAS is beneficial to increase the communication reliability over existing cellular networks, especially with regard to complex Beyond Visual Line of Sight (BVLOS) drones’ missions and applications. Its deployment is motivated as existing 4G Long Term Evolution (LTE) cellular networks (as well as 5G networks) are mainly designed and optimized for terrestrial utilization, thus not taking into account interference effects on flying connected devices. The prototypical implementation of the SSAS has been expedient to conduct multiple experimental flights with a drone at different altitudes, collecting performance results and validating the proposed SSAS as a viable solution for inter-cell interference mitigation.

Proximity estimation forms the backbone of contact tracing solutions, as quarantining potentially infected individuals is essential for controlling the spread of epidemics. It is also essential for device discovery in Device-to-Device (D2D) and Vehicle-to-Vehicle (V2V) communications, which will be critical in future 6G networks. Despite the widespread coverage of cellular networks, no previous work has evaluated cellular-based proximity estimation in an experimental setting. In this paper, we collect Channel State Information (CSI) measurements from an actual cellular network and utilize them to train and evaluate two proposed solutions. Capitalizing on CSI spatial correlation, we propose a data-driven method to classify devices based on their respective proximity. The proposed neural network has an accuracy of 91.18% when classifying devices as within 5 meters from one another or not, from only 10 seconds of CSI measurements. This method is complemented by a model-based approach that provides a solid theoretical model for estimating proximity. The model-based approach uses Bayesian inference of the conditional distribution of power correlation across devices, assuming spatially-correlated shadowing and stochastic geometry tools. The proposed Bayesian regressor fits our dataset better than the standard exponentially-decaying correlation model, reaching a $2.8 \times $ lower RMSE while requiring fine-tuning of only two more parameters. A Bayesian classifier is also proposed and reached a 91.67% accuracy on the binary case while also outperforming the data-driven model significantly at higher distances.

Cellular network scheduling is crucial for wireless deployments like 4G, 5G, and 6G and is a challenging resource allocation task performed by the scheduler located at the base stations. The scheduler must balance two critical metrics, throughput and fairness, which often conflict, as maximizing throughput favors users with better channel conditions, while ensuring fairness requires allocating resources to those with poorer channel conditions. The proportional fairness metric is a prominent scheduling approach that aims to balance these competing metrics with minimal compromise. The common strategy to attain proportional fairness relies on a greedy approach in which each resource block is allocated to the user who maximizes the proportional fairness criterion. With such a strategy, the scheduler can ensure that the resources allocated to the users at each time instance maximize the proportional fairness metric. However, users can usually tolerate some delay and are willing to accept temporary fairness imbalances if they ultimately improve their performance, provided that the fairness criterion is maintained over time. In this paper, we propose a new scheduler that uses reinforcement learning to enhance proportional fairness. The suggested scheduler considers both current and predicted future channel conditions for each user, aiming to maximize the proportional fairness criterion over a set of predefined periodic time epochs. Specifically, by learning patterns in channel fluctuations, our reinforcement learning-based scheduler allocates each resource block not to the user who maximizes the instantaneous proportional fairness metric, but to the user who maximizes the expected proportional fairness metric at the end of the current time epoch. This approach achieves an improved balance between throughput and fairness across multiple slots. Simulations demonstrate that our approach outperforms standard proportional fairness scheduling. We further implemented the proposed scheme on a live 4G eNodeB station and observed similar gains.

The integration of 5G New Radio (5G-NR) and Wi-Fi networks in heterogeneous environments holds significant promise for delivering critical services with relatively high reliability and low latency. In this paper, we tackle the critical challenge of establishing robust multi-connectivity within heterogeneous systems integrating 5G NR and Wi-Fi systems. In this context, our focus is on optimizing a new, distributed multi-Connectivity solution, with a main emphasis on identifying the ideal base stations (BSs) and Wi-Fi access points (APs) with their respective spectrum associations. This solution is essential for ensuring low-latency and reliable communication required, particularly for mission-critical services. The simulation results demonstrate the significant advantages of the proposed algorithm compared to reference schemes. It leads to network performance improvements by enhancing system utility function, reducing service access time, and increasing packet success rates, through high-quality connectivity with relatively low interference probability. In particular, for mission-critical services, the proposed algorithm leads to a 16 % and 41% increase in effective network throughput when the offered load is 190 Mbps, which outperforms the benchmark approaches. Furthermore, it improves the utility function by approximately 0.12, and 0.19 compared to the reference approaches. This reflects a substantial improvements in success delivery rates for mission-critical services by 10 % and 18 %, respectively, when compared to the reference approaches, particularly evident at an offered load of approximately 190Mbps. [ABSTRACT FROM AUTHOR]

Cost efficiency is an essential metric in the design of major part of what is expected to be the 5G network. Some of the 5G solutions, mainly those involving mmWave technology and network densification, are extremely expensive. Much of what was said about 5G rollout strategies has been said with regard to the world's major leaders in 5G technology and telecom infrastructure investment. However, a much more numerous group of countries often referred as emerging markets or middle-income economies has never hold a leadership in the development of cellular technologies. To date, few of them have fully leveraged the potential of 4G systems while many still have not ensured a return on investment in 3G. This gave rise to the views that these countries are not ready for 5G challenges. However, all of them are doomed to enter the era of another industrial revolution, where exactly 5G is to become the main driver. Yet, the use cases and the portfolio of services of the future networks will apparently depend on economic realities of the country where it will be deployed. There is a rather vague picture at the moment of how the 5G rollout may take place in this part of the world and that is the main reason for this study. In this paper, we outline the technical challenges and economic tensions associated with the developing of 5G physical infrastructure and address spectrum allocation issues with a particular focus on the needs of the future enhanced mobile broadband component. Then we discuss the role of government in establishing favorable regulatory framework, providing supportive policies and finding local drivers to facilitate 5G rollout and the challenges, confronting the operators paving their way to 5G. [ABSTRACT FROM AUTHOR]

In the last 20 years, the localization of Mobile Station (MS) in cellular networks has become an exciting topic of the multitude of applications related to location information. The localization of MSs below the US Federal Communications Commission is considered the primary driving force to investigate and supply optimum solutions. A new Mobile Station localization technique is proposed, and its efficiency is evaluated in this paper. The proposed technique, hybrid SADOA/AOA, combines the signal attenuation difference of arrival (SADOA), which generates a circle on which the MS may lie, and the angle of arrival (AOA) techniques, which uses trigonometry laws of sine and cosine to locate the MS. This technique uses Taylor series expansion to solve the hyperbolas and Line of Bearings derived by the two techniques individually. Then it proceeds to use the linear least squares algorithm iteratively to find the location. Simulation results showed that the hybrid SADOA/AOA outperforms the conventional SADOA technique without hardware modification, and it can overcome the shadowing effect of the environment. [ABSTRACT FROM AUTHOR]

Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data. Synopsis: ISCHIA leverages the inherent proximity of spatial transcriptomics spot data to reconstruct networks of co-occurring cell types. Co-occurrence analysis is performed within and not across spots, and it is independent of cell type and transcript abundance. ISCHIA applies co-occurrence analysis to spatial transcriptomics data to reconstruct cellular networks. Co-occurrence analysis is complementary to differential gene expression, as it captures the spatial arrangement of cell types and transcripts in the tissue. ISCHIA identifies an inflammation-induced cellular network involving M cells and fibroblasts in human ulcerative colitis samples. ISCHIA leverages the inherent proximity of spatial transcriptomics spot data to reconstruct networks of co-occurring cell types. Co-occurrence analysis is performed within and not across spots, and is independent of cell type and transcript abundance. [ABSTRACT FROM AUTHOR]

This research article provides an extensive analysis of novel methods of cryptographic protection as well as advancements in authentication and authorization techniques within cellular networks. The aim is to explore recent literature and identify effective authentication and authorization methods, including high-speed data encryption. The significance of this study lies in the growing need for enhanced data security in scientific research. Therefore, the focus is on identifying suitable authentication and authorization schemes, including blockchain-based approaches for distributed mobile cloud computing. The research methodology includes observation, comparison, and abstraction, allowing for a comprehensive examination of advanced encryption schemes and algorithms. Topics covered in this article include multi-factor authentication, continuous authentication, identity-based cryptography for vehicle-to-vehicle (V2V) communication, secure blockchain-based authentication for fog computing, internet of things (IoT) device mutual authentication, authentication for wireless sensor networks based on blockchain, new secure authentication schemes for standard wireless telecommunications networks, and the security aspects of 4G and 5G cellular networks. Additionally, in the paper a differentiated authentication mechanism for heterogeneous 6G networks blockchain-based is discussed. The findings presented in this article hold practical value for organizations involved in scientific research and information security, particularly in encryption and protection of sensitive data. [ABSTRACT FROM AUTHOR]

Solid waste management (SWM) is a pressing concern and significant research topic that requires attention from citizens and government stakeholders. Most of the responsibility of waste management is on the municipal sector for its collection, reallocation, and reuse of other resources. The daily solid waste production is more than 54,850 tonnes in urban areas and is difficult to manage due to limited resources and different administrative and service issues. New technologies are playing their role in this area but how to integrate the technologies is still a question, especially for developing countries. This paper is divided into two main phases including a detailed investigation and a technological solution. In the first phase, the data is collected by using the qualitative method to investigate and identify the issues related to waste management. After a detailed investigation and results, the gap is identified by using statistical analysis and proposed a technological solution in the second phase. The technology-based solution is used to control and manage waste with a low-cost, fast, and manageable solution. The new sensor-based technologies, cellular networks, and social media are utilized to monitor the trash in the areas. The trash management department receives notification via cellular services to locate the dustbin when the dustbin reaches a maximum level so the department may send a waste collector vehicle to the relevant spot to collect waste. The smart and fast solution will connect all stakeholders in the community and reduce the cost and time and make the collection process faster. The experiment results indicated the issues and effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance. In this paper, our proposal involves substituting the retransmission rule of the SUCRe protocol with a neural network (NN) to enhance the identification of the strongest user and resolve collisions in a decentralized manner on the UEs' side. The proposed NN-based procedure is trained offline, admitting different congestion levels of the system, aiming to obtain a single setup able to operate with different numbers of UEs. The numerical results indicate that our method attains substantial connectivity performance improvements compared to other protocols without requiring additional complexity or overhead. In addition, the proposed approach is robust regarding variations in the number of BS antennas and transmission power while improving energy efficiency by requiring fewer attempts on the RA stage. [ABSTRACT FROM AUTHOR]

It is clear that 5G networks have already become integral to our present. However, a significant issue lies in the fact that current 5G communication systems are incapable of fully ensuring the required quality of service and the security of transmitted data, especially in government networks that operate in the context of the Internet of Things, hostilities, hybrid warfare, and cyberwarfare. The use of 5G extends to critical infrastructure operators and special users such as law enforcement, governments, and the military. Adapting modern cellular networks to meet the specific needs of these special users is not only feasible but also necessary. In doing so, these networks must meet additional stringent requirements for reliability, performance, and, most importantly, data security. This scientific paper is dedicated to addressing the challenges associated with ensuring cybersecurity in this context. To effectively improve or ensure a sufficient level of cybersecurity, it is essential to measure the primary indicators of the effectiveness of the security system. At the moment, there are no comprehensive lists of these key indicators that require priority monitoring. Therefore, this article first analyzed the existing similar indicators and presented a list of them, which will make it possible to continuously monitor the state of cybersecurity systems of 5G cellular networks with the aim of using them for groups of special users. Based on this list of cybersecurity KPIs, as a result, this article presents a model to identify and evaluate these indicators. To develop this model, we comprehensively analyzed potential groups of performance indicators, selected the most relevant ones, and introduced a mathematical framework for their quantitative assessment. Furthermore, as part of our research efforts, we proposed enhancements to the core of the 4G/5G network. These enhancements enable data collection and statistical analysis through specialized sensors and existing servers, contributing to improved cybersecurity within these networks. Thus, the approach proposed in the article opens up an opportunity for continuous monitoring and, accordingly, improving the performance indicators of cybersecurity systems, which in turn makes it possible to use them for the maintenance of critical infrastructure and other users whose service presents increased requirements for cybersecurity systems. [ABSTRACT FROM AUTHOR]

This paper investigates a cellular network assisted by unmanned aerial vehicles (UAVs) in the presence of a fluctuating 3-dimensional (3D) antenna beamwidth. The primary objective is to perform an analysis of typical user equipment (T-UE) performance with a specific focus on coverage probability and spectral efficiency (SE) in the presence of fluctuations of 3D antenna beamwidth. Within this analytical framework, the macro base stations (MBSs) are meticulously characterized through the application of an independent 2D homogeneous Poisson point process (PPP), while the low-altitude platforms (LAPs) are described using an independent 3D PPP. The study entails the derivation of association probabilities, determining the likelihood of the T-UE associating with MBSs, line-of-sight LAPs, and non-line-of-sight LAPs. Through rigorous mathematical analysis, the paper formulates precise analytical expressions that encapsulate the association and coverage probabilities, taking into account the inherent variability in the UAV antenna beamwidth. This research focuses on a thorough performance evaluation of the T-UE across diverse network configurations, encompassing LAP density, the transmission power of LAPs, and the critical signal-to-interference ratio threshold. The outcomes of this study distinctly underscore the substantial disruptive impact resulting from fluctuating beamwidth on the performance of the T-UE within the UAV-assisted cellular network. Additionally, this performance is further impacted by larger densities and transmission power of the LAP. Hence, it is imperative to take into account the influence of these fluctuations on network association, coverage, and SE whenever contemplating a UAV-assisted cellular network. [ABSTRACT FROM AUTHOR]

The lack of publicly open network traffic datasets for research purposes is hindering machine learning applications to wireless network analysis and design. In this work, a number of published traffic throughput temporal evolutions are digitized and used for traffic anomaly and change point detection. The mean traffic temporal evolutions are extracted by digitizing graphical curves and organized into a time series dataset. The procedure of reusing such dataset for testing traffic analysis models is presented. The procedure consists of the following steps. Firstly, α -stable distributed traffic variations are added to the mean throughput values. Secondly, traffic anomalies are generated by analyzing traffic redistribution after switching on and off base stations and sectors. Lastly, traffic trend changes are introduced to model impact of global events such as COVID-19 pandemic. Several machine learning models are used to illustrate applicability of created dataset for detecting traffic anomalies and trend changes. [ABSTRACT FROM AUTHOR]

As a strategy to coordinate inter-cell interference in cellular networks, a fractional frequency reuse (FFR) system is proposed, in which the frequency bandwidth is split into two orthogonal bands; users staying near the center of a FFR cell use the band with a frequency reuse (FR) factor of one (i.e., full FR), and users located close to the cell edge utilize the band with a FR factor greater than one (i.e., partial FR). Full FR coverage, which identifies full FR and partial FR regions (that is, near-center and near-edge regions) within a FFR cell, has a crucial effect on system performance. Some of the authors of this paper recently investigated the optimization of full FR coverage to maximize system throughput. They analytically showed that under the constraint of satisfying a specified target outage probability, the optimal full FR coverage is a non-increasing function of base station power when all base station powers in the cellular network are scaled at an equal rate. Interestingly, in this paper, it is proven that as the power of a single base station is scaled, the optimal full FR coverage in that cell is a non-decreasing function of base station power. Our results provide useful insight into the design of full FR coverage in relation to the transmit power of a base station. It gives a deeper understanding of the intricate relationship between important FFR system parameters of base station power and full FR coverage. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) of cellular networks improves the precision of user localization and provides the means to disclose location information to over-the-top (OTT) service providers. The network data analytics function (NWDAF) can further elaborate this information at an aggregated level using artificial intelligence techniques. These powerful features may lead to the improper use of user location information by mobile network operators (MNOs) and OTT service providers. Moreover, vulnerabilities at various layers may also leak user location information to eavesdroppers. Hence, the privacy of users is likely at risk, as location is part of their sensitive data. In this paper, we first go through the evolution of localization in cellular networks and investigate their effects on location privacy. Then, we propose a location-privacy-preserving integrated solution comprising virtual private mobile networks, an independent authentication and billing authority, and functions to protect wireless signals against location information leakage. Moreover, we advocate the continuous and detailed control of localization services by the user.