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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.

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.

Nowadays, high mobility scenarios have become increasingly common. The widespread adoption of High-speed Rail (HSR) in China exemplifies this trend, while more promising use cases, such as vehicle-to-everything, continue to emerge. However, the Internet access provided in high mobility environments still struggles to achieve seamless connectivity. The next generation of wireless cellular technology 5 G further poses more requirements on the end-to-end evolution to fully utilize its ultra-high band-width, while existing network diagnostic tools focus on above-IP layers or below-IP layers only. We then propose HiMoDiag, which enables flexible online analysis of the network performance in a cross-layer manner, i.e., from the top (application layer) to the bottom (physical layer). We believe HiMoDiag could greatly simplify the process of pinpointing the deficiencies of the Internet access delivery on HSR, lead to more timely optimization and ultimately help to improve the network performance.

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emissions and electricity expenses of base stations. However, renewable energy is inherently stochastic and intermittent, imposing formidable challenges on reliably satisfying users' time-varying wireless traffic demands. In addition, the probability distribution of the renewable energy or users’ wireless traffic demand is not always fully known in practice. In this paper, we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations, the battery charging and discharging rates, and the energy purchased from the grid under the constraint of a limited battery size at each base station. In solving the formulated non-convex chance-constrained stochastic optimization problem, a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance. Using this ambiguity set, the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach. Furthermore, a low-complexity distributionally robust chance-constrained energy management algorithm, which requires only interval sets of the mean and covariance of stochastic parameters, is proposed. The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity, energy cost, and reliability.

Femtocells are designed to co-exist alongside macrocells providing higher spectrum efficiency, spatial frequency reuse, to name a few. However, when deployed in the two-tier architecture with macrocells, interference is imminent thereby it is necessary to mitigate the inherent co-tier and cross-tier interference. The integration of cognitive radio (CR) in femtocells introduces the ability of femtocells to dynamically adapt to varying network conditions through learning, adapting, and reasoning leading to informed decisions. This paper proposes a joint threshold power based admittance and contention free resource allocation scheme (known as Proposed SA) for interference mitigation in HetNet cognitive femtocells (CF). In the proposed SA, a CF sets a threshold value on the mutual interference between itself and a close-by mobile user equipment (MUE). To mitigate cross-tier interference, a CF classifies MUEs which fall above this threshold value (high interference value) as Undesired MUEs (UMUEs), whereas MUEs which fall below this threshold are classified as Desired MUEs (DMUEs). To mitigate co-tier interference, proposed SA introduces a scheduling engine which employs matching policy attributes and assigns resource blocks (RBs) of unique DMUEs to CFs to avoid any possible contention problems, thus providing improved co-tier interference. System level simulations have been performed to demonstrate working and effectiveness of proposed SA. Key performance indicators including choice of maximum transmit power for MUE, co and cross tier interference mitigation, as well impact of MUE mobility has been investigated. Performance analysis of the Proposed SA has also found that the scheme performs much better in relation to a random spectrum assignment (Random SA) scheme. The proposed SA caters the mobility of the MUEs very well allowing more and more admittance thereby providing improved interference results and making it a viable candidate for Femtocell Access points (FAPs) implementation.

In this paper, with the aid of the mathematical tool of stochastic geometry, we introduce analytical and computational frameworks for the distribution of three different definitions of delay, i.e., the time that it takes for a user to successfully receive a data packet, in large-scale cellular networks. We also provide an asymptotic analysis of one of the delay distributions, which can be regarded as the packet loss probability of a given network. To facilitate the computation of the obtained analytical formulations, we propose efficient numerical approximations based on the numerical inversion method, the Riemann sum, and the beta distribution. Finally, we demonstrate the accuracy of the obtained analytical formulations and the corresponding approximations against Monte Carlo simulations, and unveil insights on the delay performance with respect to several design parameters, such as the decoding threshold, the transmit power, and the deployment density of the base stations. The proposed methods can facilitate the analysis and optimization of cellular networks, subject to reliability constraints on the network packet delay, that are not restricted to the local (average) delay, e.g., in the context of delay sensitive applications.

Future cellular systems are expected to use millimeter-wave (mm-Wave) frequency bands in addition to the existing microwave bands under 6 GHz. Severe weather conditions, including sand and dust storms (SDSs) and heavy rainfalls, challenge reliable communications over wireless links at those higher frequencies. In such conditions, besides frequency-dependent path-loss, radio signals experience additional attenuation. The SDS attenuation is related to visibility, receiver distance to the storm origin point, soil type, frequency, temperature and humidity. On the other hand, the rainfall attenuation is affected by rainfall rate, polarization, carrier frequency, temperature and raindrop size distribution. Leveraging on experimental measurements carried out in previous works, a novel unified mathematical framework is introduced in this paper to include SDS/rainfall-dependent attenuation in the performance evaluation of terrestrial wireless cellular networks in terms of coverage probability, bit error rate (BER) and achievable rate in the mm-Wave band. Extensive numerical results are presented to show the effects of the different SDS/rainfall parameters on performance, showing that the degradation due to SDS is generally higher than that due to rain and may cause a reduction of even six orders of magnitude in the average achievable bit rate when the frequency increases from 28 to 38 GHz.

In recent years, with the spread of IoT services and the amount of data on the network, the importance of D2D communications has emerged in unloading the network, reducing data access time, and improving performance. Due to this importance, in this article, we have reviewed the latest D2D communication solutions in the recent period. Authors proposed categorizing D2D communication solutions into six categories: M2M, V2V, and D2D in cellular networks, D2D with a caching assistant, D2D in socially aware networks, and D2D based on ICN. Writers examined the proposed solutions in each area, elaborating on the methodology employed, the evaluation, the benefits and drawbacks of each proposal. Moreover, based on the study of the presented solutions, we feel that cellular networks and ICN are the most effective for D2D communications, particularly when awareness and social relationships are incorporated into them. We believe that the review presented in this paper will be useful sufficient for developers, researchers as a reference guide to facilitate the design and implementation of D2D communication solutions.

In this work, we provide a unified framework for full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular networks with low-resolution analog-to-digital and digital-to-analog converters (ADCs and DACs). An objective of this work is to derive an accurate model to account for a wide variety of network irregularities and imperfections, including loopback self-interference (SI) that arises in full-duplex systems and quantization error from low-resolution data converters. Our contributions for forward and reverse links include (1) deriving signal-to-quantization-plus-interference-plus-noise ratio (SQINR) under pilot contamination, linear minimum mean square error (LMMSE) channel estimation and channel hardening; (2) deriving closed-form and approximate analytical expressions of spectral efficiency; (3) deriving asymptotic results and power scaling laws with respect to the number of quantization bits, base station antennas, and users, as well as base station and user equipment power budgets; and (4) analyzing outage probability and spectral efficiency vs. cell shape, shadowing, noise, cellular interference, pilot contamination, pilot overhead, and frequency reuse. Cell shapes include hexagonal, square, and Poisson Point Process (PPP) tessellations. In simulation, we quantify spectral and energy efficiency as well as the impact of SI power, inter-user interference, and cell shape on outage probability. We carry out the analysis for sub-7 GHz long term evolution (LTE) bands and then extend the framework to support millimeter wave (mmWave) bands.

Abstract Background Human 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. Methods In 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. Results Heterogeneous 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. Conclusions High 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.

With the advent of fifth-generation 5G networks, mobile communication data rates are steadily rising. The existing base stations (BSs) must reduce the loads with minimal cost and effort. With traffic demands constantly rising, available spectrum resources must be utilized effectively. One of the most exciting developments in 5G networks, particularly cellular networks, is the potential for devices to communicate directly with one another through a technology called device-to-device communication (D2D). With the transmission split between D2D users on one side and cellular users and the BS on the other, it will be effective in the rapidly expanding capacity and offload traffic from the BS. With the dynamic spectrum access in mind, this article proposes using cognitive radio direct-to-device (CRD2D) communication in 5G networks to make use of the unused spectrum. Throughput and latency are examined, for a carrier sense multiple access (CSMA) based medium access control (MAC) protocol that uses a common control channel (CCC) for device users to negotiate the data channel and to address contention among device users. Device users can take advantage of cognitive radio to access multiple data channels simultaneously in a shared interference zone. Since this property has not been utilized, in prior works, we use it to measure the possible delay and throughput. Cognitive radio capability applied to D2D communication and the allocation of a common control channel for device users significantly increases aggregated network throughput by more than 60% compared to individual D2D throughput without causing any harmful interference to cellular users. The wait time can also be scale back using this method.

A key component of the Internet of things (IoT) ecosystem is wide-area network connectivity, for which cellular network technologies are a promising option through their support of massive machine-type communications (mMTC). However, numerous devices transmitting sporadically small data packets in a highly synchronized way can generate overload on the radio access network. This situation leads to a shortage of resources, especially those associated with the random access procedure for contention, control, and data transmission, causing preamble collision, control message blocking, and data collision. As a result, mMTC traffic can jeopardize the provisioning of quality of service (QoS) to end-devices, decrease the network efficiency, and increase access latency and device energy consumption. This paper proposes medium access control (MAC) techniques for addressing problems related to the support of mMTC in cellular networks. First, a solution for allocating control resources with QoS provisioning and access differentiation is provided, including a resource management model, a scheduling algorithm, and a message prioritization policy. Second, a mechanism for reducing data collisions is also proposed. This solution comprises a protocol in which every device with scheduled retransmission uses a probabilistic policy to decide whether to retransmit, a novel method at the device to estimate the number of nodes trying random access, and two different retransmission policies employing this estimation. Results show that the proposals support QoS, decrease access latency, decrease the device energy consumption, and increase resource utilization under massive random access.

Smart logistics, combining the capabilities of logistics with methods and techniques of the Internet of Things, Information and Communication Technologies, and the highest levels of automation are key to addressing the challenges of the 21st century and minimizing emissions while maximizing logistic performance. High-performance cellular networks are a prerequisite to fully using and leveraging their possibilities. These communication networks were developed based on the need for voice communication and streaming services. While the upcoming requirements are included in the latest versions of cellular networks, the existing infrastructure requires significant improvements and will have to adapt significantly. This study evaluates the performance of the current state of implementation of cellular networks on the German highway experimentally and analytically. The known indicators RSRP, RSSI, and RSRQ are analyzed spatially, over time, and for different driving conditions. The results indicate a high level of spatial correlation and a sufficient level of confidence, which are needed to ensure consistency and repeatability of these measurements. The procedure and the results can be used to assess the suitability of cellular networks for smart logistics applications and continuously monitor their improvement. The results indicate the status of the cellular network on the German highway which is worse compared to the network operator’s self-assessment.

Cellular networks have witnessed an unprecedented expansion represented by the number of subscribers in addition to the unlimited number of applications. In the era of IoT, connections may involve machines working side by side with humans on the same network. Tidal load is one of the key challenges in mobile networks, which represents a temporary phenomenon resulting from increasing traffic volumes at certain times of the day in particular places. This leads to the problem of overutilization of some base stations, particularly in fixed resources allocation systems such as 4G LTE architecture, which contains physically separated base stations. This results in increasing the dropping and blocking probability for the incoming calls in an overloaded area, while others suffer from underutilization or might be almost idle for a particular time. In this paper, an Elastic Call Admission Control (ECAC) approach is proposed to reduce the probability of blocking and dropping to improve the network performance in the tidal load scenario. This is achieved using the concept of gain multiplexing technique and cloud-based cellular architecture for utilizing the shared available recourses of the co-located virtual base stations in the cloud. A Fuzzy Type 2 (FT2) system is used to maintain the switching decision for resource sharing mode, the network load is predicted in advance using Gaussian Process Regression (GPR) model. Performance evaluation is carried out using the cloud-based Fog-Radio Access Network (F-RAN) platform. Compared to legacy LTE architecture, the results illustrate a noticeable enhancement in terms of minimizing blocking probability, enhancing overall network performance represented by network data throughput, and network utilization.

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.

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.

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.

Worldwide Researchers and scientist have started the investigation of the sixth generation (6G) while the fifth generation (5G) cellular system is being deployed. Under this main investigation the main aim of 6G is to provide intelligent and ubiquitous wireless connectivity with Terabits per second (Tbps) data rates. Accurate location information of the mobile devices is very much useful to accomplish these aims with the improvements of various parameters of wireless communication. The development in communication technology often creates new opportunities to improve the localization efficiency as demonstrated by the expected centimetre-level localization accuracy in 6G. While there are comprehensive literatures separately on wireless localization or communications, the 6G study is still in its inception. This article is therefore intended to provide an overview of localization techniques towards 6G wireless networks. Finally, some interesting future localization technique research directions are highlighted.

In recent years, there has been an increased demand for better and faster communication networks to meet the needs of Beyond 5G network. The fiber-wireless integrated network has been widely investigated, taking advantage of the large capacity and low transmission loss properties of the optical fiber to extend the coverage of wireless networks and the cellular networks in particular. To optimize the resource allocation in these networks, machine learning (ML) techniques have been proposed, which aim to predict the cellular traffic in advance to allow proactive resource allocation. Existing works mainly consider two factors in traffic prediction: the spatial correlation amongst nearby based stations and the temporal dynamics observed in historical records. In this paper, we study a crucial and yet unexplored aspect, i.e., meteorological factors, such as rain, wind, or temperature. We first perform statistical analysis on a real-world dataset, the results of which confirm the strong impact of meteorological factors upon traffic volume. Thereafter, we propose a traffic prediction model that captures the temporal, spatial, and meteorological patterns simultaneously. The proposed model learns the network traffic patterns through a novel graph convolutional network - gated recurrent unit (GCN-GRU) cell. The GCN-GRU has a hierarchical structure with two child GRUs capturing the temporal dynamics in traffic and meteorological records respectively and a parent GRU capturing the unified impact of historical traffic and meteorological data upon future traffic. The spatial correlation among traffic records is captured by a graphical convolution module within the proposed GCN-GRU cell. We conduct extensive experiments on real-world datasets. The results confirm the effectiveness of the proposed model, with a performance improvement by up to 24.8% achieved using the hierarchical GCN-GRU model.

Although mobile communication has become ubiquitous in our modern society, operators typically treat the underlying networking infrastructure in a secretive manner. However, detailed topology information is a key enabler for operator benchmarking and can serve as ground-truth data for system-level simulations or user equipment localization schemes. Still, existing approaches for base station localization that are either based on received signal strength or on the spatial distribution of measurements are not accurate enough for such use cases. Accordingly, we propose a localization scheme that operates on end-user measurements of the 4G & 5G timing advance parameter, which acts as a quantized distance measure between the user equipment and the base station. By directly incorporating GPS noise, multipath propagation, and quantization into our stochastic system model, we obtain an estimator that offers a reliable measure of confidence and requires only the configuration of two hyperparameters with a dedicated physical interpretation. We evaluate our approach using a set of drive-test measurements consisting of 190 LTE eNodeBs with ground-truth locations confirmed by an Austrian mobile network operator. Our selective estimator can either operate without prior knowledge, resulting in mean distance errors of below 100 m, or in a classification setup, where it correctly identifies up to 95% of eNodeBs from a set of candidate cell tower locations. To allow for reproducibility, we make our dataset and a reference implementation publicly available.

This work presents the Network Assisted Routing and Allocation Algorithm (NARA), a routing and allocation algorithm for device-to-device (D2D) communication in 5G cellular architectures. NARA algorithm is a new routing and resource block allocation algorithm that aims at reducing the impact of interference and extends network coverage in D2D communications. We model the allocation of resource blocks as a Minimum Graph Coloring Problem. As baselines, we compare the proposed strategy to NAR-G, a previous version that uses a greedy allocation heuristic; MaxC/I, a reference heuristic for resource allocation; and LBS-AOMDV for routing. The results reveal that NARA reduces message loss in 47.4% when compared LBS-AOMDV, 39.87% to NAR-G, and 23.06% when compared to MaxC/I. As for allocation, NARA provides 23.05% more D2D flows than MaxC/I.

Patients who suffer from traumatic brain injury (TBI) often complain of learning and memory problems. Their symptoms are principally mediated by the hippocampus and the ability to adapt to stimulus, also known as neural plasticity. Therefore, one plausible injury mechanism is plasticity impairment, which currently lacks comprehensive investigation across TBI research. For these studies, we used a computational network model of the hippocampus that includes the dentate gyrus, CA3, and CA1 with neuron-scale resolution. We simulated mild injury through weakened spike-timing-dependent plasticity (STDP), which modulates synaptic weights according to causal spike timing. In preliminary work, we found functional deficits consisting of decreased firing rate and broadband power in areas CA3 and CA1 after STDP impairment. To address structural changes with these studies, we applied modularity analysis to evaluate how STDP impairment modifies community structure in the hippocampal network. We also studied the emergent function of network-based learning and found that impaired networks could acquire conditioned responses after training, but the magnitude of the response was significantly lower. Furthermore, we examined pattern separation, a prerequisite of learning, by entraining two overlapping patterns. Contrary to our initial hypothesis, impaired networks did not exhibit deficits in pattern separation with either population- or rate-based coding. Collectively, these results demonstrate how a mechanism of injury that operates at the synapse regulates circuit function.

One of the most important issues accepted by researchers in LTE cellular systems is to develop Queue Management Algorithms for RLC (Radio Link Control). The performance of queue-management algorithms depends on parameters such as latency, packet dropping, and bandwidth usage. Simulation software is used to evaluate the queue-management algorithms developed and to test their performance. In the literature, active queue management algorithms have been compared with wired and wireless networks. In contrast to prior works, in this study, we have analyzed active queue management algorithms using the LTE model in the NS-3 network simulator. When the data and the results obtained from the simulations have been evaluated, it is concluded that the RED algorithm using probabilistic methods and the threshold value is more successful than the other algorithms in LTE networks.

Multimedia content delivery via the cellular infrastructure increases fast due to the very high volumes of mobile video traffic generated by the billions of end devices populating the mobile data network. A critical mass of mobile video content requests refers to the consumption of the same popular video content, which is consumed by different end terminals spanning small geographical regions. Such content requests place a great burden on the backhaul of content-agnostic cellular networks, which fail to exploit the correlation of video requests to decongest their backhaul links. This creates redundant retransmissions while fetching the same video content from a central server to the network edge, using the bandwidth-limited backhaul at peak-time periods. With the integration of multi-access edge computing (MEC) capabilities in 5G mobile cellular networks, mobile network operators can place popular video content closer to the network edge at off-peak time periods, predicting user requests exhibiting a high correlation for a given time interval over smaller geographical regions. In this paper, we investigate popular content placement in multi-tier heterogeneous cellular networks where the edge network infrastructure can cooperate to create content delivery (and placement) clusters to effectively serve correlated video requests. To this end, we model the cooperative content placement problem using the multiple knapsack problem (MKP) formulation and present an exact (optimal) bound-and-bound strategy to solve it. The performance of the proposed strategy is evaluated in-depth using extensive system-level simulations and is compared against that of other state-of-the-art algorithms. Valuable design guidelines and key performance trade-offs are discussed, paving the way towards cluster-based cooperative caching in MEC-enabled cellular network setups.

Non-terrestrial Networks (NTNs) have become an appealing concept over the last few years and they are foreseen as a cornerstone for the next generations of mobile communication systems. Despite opening up new market opportunities and use cases for the future, the novel impairments caused by the signal propagation over the NTN channel, compromises several procedures of the current cellular standards. One of the first and most important procedures impacted is the random access (RA) procedure, which is mainly utilized for achieving uplink synchronization among users in several standards, such as the fourth and fifth generation of mobile communication (4 & 5G) and narrowband internet of things (NB-IoT). In this work, we analyse the challenges imposed by the considerably increased delay in the communication link on the RA procedure and propose new solutions to overcome those challenges. A trade-off analysis of various solutions is provided taking into account also the already existing ones in the literature. In order to broaden the scope of applicability, we keep the analysis general targeting 4G, 5G and NB-IoT systems since the RA procedure is quasi-identical among these technologies. Last but not least, we go one step further and validate our techniques in an experimental setup, consisting of a user and a base station implemented in open air interface (OAI), and an NTN channel implemented in hardware that emulates the signal propagation delay. The laboratory test-bed built in this work, not only enables us to validate various solutions, but also plays a crucial role in identifying novel challenges not previously treated in the literature. Finally, an important key performance indicator (KPI) of the RA procedure over NTN is shown, which is the time that a single user requires to establish a connection with the base station.

Cellular networks are constantly evolving to support the new context and demands of users with increasing internet traffic. The key to a service provider’s success is a direct response to endpoints to retain existing users and attract new users. For this reason, Mobile Network Operators (MNOs) must reach user satisfaction under a user-centered perspective with Quality of Experience (QoE) to achieve a Return on Investment (RoI). In the latest QoE modelling, our previous method performed QoE estimation and synthesis using an Artificial Neural Network (ANN). Nevertheless, such work did not prove the characteristics of the QoE model built on different working situations. The essential features of the area and user diversities need to be considered in detail to summarize the proper model. In this paper, the proposed framework focuses on analyzing QoE model characteristics under different operating situations for comprehensive implementation. The experimental results reveal that the created QoE models from areas of different densities affect the different predictive variables. Inter-Rater Reliability (IRR) analysis exposes the reliability of datasets gathered from users of different groups to distinguish the effects on the response of significant parameters. Additionally, the summary specifies the characteristics of the appropriate model by area and user group as a guideline for improving the multimedia services.

While environmental and financial reasons point to the minimization of the number of radio sites to contain energy use, emissions, and operating expenses of cellular networks, the industry has, in retrospect, partly headed in the opposite direction. The reasons are the deployment of new technologies alongside the existing ones, the use of higher frequencies, and the expansion of radio coverage. In some cases, the issue has been addressed by replacing legacy access sites with Multi-RAT infrastructures, composed of individual radio elements that run multiple technologies concurrently. Now, the transition to 5G poses additional challenges. This paper reviews Multi-RAT architectures, outlines their benefits, discusses how 5G can be integrated, and provides guidance in terms of architectural recommendations, all from an energy consumption standpoint. Specifically, we firstly summarize the transition of monolithic base stations into modern radio elements, exposing the energy rationale and discussing the impact of each main network component. From this basis, we survey different deployment strategies and evaluate their energy implications in light of the constraints and opportunities given by the 5G New Radio standard, its flagship applications, and its transport requirements. Then, we lay down energy-saving estimates quantifying the contribution of each main network segment, revealing the most promising architectures, and identifying the main challenges and the research directions ahead.

Downlink exposure to electromagnetic fields due to cellular base stations in urban environments is studied using the stochastic geometry framework. A two-dimensional Poisson Point Process is assumed for the base station distribution. Mathematical expressions of statistics of exposure are derived from a simple propagation model taking into account the height of the base stations. The error on exposure made by taking a limited number of base stations, instead of the whole set, is quantified. The relative impact of the model parameters on the statistics of exposure is highlighted. The method is then applied and the model parameters are calibrated using experimental data obtained by drive-tests in two Brussels municipalities, in Belgium, for the 2100 MHz and 2600 MHz frequency bands. It is shown that the proposed model fits experimental values, paving the way to a new methodology to assess general public exposure to electromagnetic fields, for any frequency band. An insight is given on how to apply the methodology to a real case without access to experimental data.

In order to support diverse access requirements from various internet-of-things (IoT) applications, we propose a novel access priority provisioning technique that can be applied to the random access (RA) procedure in cellular networks. A key feature of our proposed technique is to allow for each IoT device to differentiate the number of simultaneously transmitted preambles during the RA procedure according to its own access priority. Since simultaneous transmission of multiple preambles (i.e., RA parallelization) can achieve the diversity effect during the access phase (i.e., RA procedure), the IoT devices using more preambles can achieve better access performance compared to those using fewer preambles. This motivates us to newly propose our access priority provisioning technique. We mathematically analyze our proposed technique in terms of the RA failure probability and validate our analytical framework with extensive computer simulations. From the results, we verify the feasibility of our proposed technique for supporting diverse access priorities during the access phase (i.e., RA procedure) without significant modifications to the conventional one.

Cellular networks are constantly evolving to ensure a better Quality of Service (QoS) and quality of coverage ever more important. The radio cellular systems are based on frequency allocation. In this context, frequency allocation principle consists in choosing an optimal frequency plan to meet traffic demand constraints and communication quality while minimizing the radio interferences. This paper proposes an optimal frequency allocation approach based on genetic algorithms to minimize co-channel and adjacent channel interference. The validation of this new approach is confirmed by the results of the work we have done in the GSM network. In fact, we used the file obtained by the OMC-R, which defines the adjacent cells of each cell and the frequencies allocated to the considered area. The results obtained clearly show the effectiveness and robustness of the approach used.

The rapid evolution of cellular system design towards 5G and beyond gives rise to a need for investigation of the new features, design proposals and solutions in realistic settings for various deployments and use case scenarios. While many system level simulators for 4G and 5G exist today, there is particularly a dire need for a 3GPP compliant system level holistic and realistic simulator that can support evaluation of the plethora of AI based network automation solutions being proposed in literature. In this paper we present such a simulator developed at AI4networks Lab, called SyntheticNET. To the best of authors' knowledge, SyntheticNET is the very first python-based simulator that fully conforms to 3GPP 5G standard release 15 and is upgradable to future releases. The key distinguishing features of SyntheticNET compared to existing simulators include: 1) a modular structure to facilitate cross validation and upgrading to future releases; 2) flexible propagation modeling using measurement based, ray tracing based or AI based propagation modeling; 3) ability to import data sheet based on measurement based realistic vendor specific base station features such as antenna and energy consumption pattern; 4) support for 5G standard based adaptive numerology; 5) realistic and user-specific mobility patterns that are yielded from actual geographical maps; 6) detailed handover (HO) process implementation; and 7) incorporation of database aided edge computing. Another key feature of the SyntheticNET is the ease with which it can be used to test AI based network automation solutions. Being the first python based 5G simulator, this ease, in part stems for SyntheticNET's built-in capability to process and analyze large data sets and integrated access to Machine Learning libraries. Thus, SyntheticNET simulator offers a powerful platform for academia and industry alike to investigate not only new solutions for optimally designing, deploying and operating existing and emerging cellular networks but also for enabling AI empowered deep automation in the future.

One of the main challenges that mobile crowdsensing systems must solve is reducing data collection costs while still holding high data delivery probability. Compared with cellular networks, opportunistic networks can significantly reduce data transfer costs at the cost of damaging data delivery probability. This paper proposes an optimal data collection scheme for mobile crowdsensing, which utilizes integrated cellular and opportunistic networks to implement data collection. We use data collecting path to describe how the sensing data are collected and sent to the back-end platform, though cellular networks directly or through multi-hop opportunistic networks. An optimal data collection problem is then formulated as choosing specific data collecting paths from candidate path set to minimize the total crowdsensing cost under the data delivery constraints, which can be considered as a minimum set covering problem. To solve this NP-hard problem, we design and implement a greedy heuristic algorithm that constructs the solution in multiple steps by making a locally optimal decision in each step. We conduct extensive simulations based on three real-world traces: Cambridge, Infocom06, and UPB. The results show that, compared with other data collection approaches, our approach achieves a better tradeoff between cost and data delivery.

Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

This paper studies the trajectory optimization problem of a single cellular-enabled unmanned aerial vehicle (UAV), taking into account the outage performance of the entire trajectory. To provide real-time control, it is critical for UAV to maintain reliable connectivity with the ground base station (GBS). We first consider the connectivity outage performance, which is defined as the sum of the time duration of the outage performance not meeting a predefined threshold during the entire UAV mission. Then we formulate a trajectory optimization problem to minimize the mission completion time, while ensuring a sum constraint of the connectivity outage performance. We show that the connectivity outage constraint can be transformed into a flying area constraint. Since the formulated problem is NP-hard, a low-complexity method is proposed to solve the problem by finding the shortest path in an undirected weighted graph with enlarged GBS coverage. Simulation results demonstrate the superiority of the proposed scheme over other state-of-the-art schemes, in terms of trajectory length and computational complexity.

This paper studies imperfect underlay device-to-device (D2D) association in spectrum-shared cellular networks. It addresses important system and design interference constraints, processing load limitations and transmit power constraints at D2D terminals. The paper proposes decentralized schemes for D2D communication between D2D terminals when downlink channel resources can be reused in the D2D network. The D2D transmitters are characterized considering their processing load limitation and allocated transmit power constraints. Moreover, the downlink channels that can be reused in the D2D network are quantified while meeting interference constraints imposed by the primary cellular network. Two schemes to identify reusable channels, which vary in terms of their efficiency, communication overhead requirement and implementation complexity, are described. Moreover, two D2D association schemes, namely the simultaneous and sequential D2D associations, are proposed and both aim to concurrently maximize the desired link quality and minimize the effect of interference effect at D2D receivers. Generalized analytical results that are applicable for various imperfect association scenarios are presented. The findings are applicable for any D2D channel models and performance metrics. They provide insights into various imperfect underlay D2D association scenarios under the practical system and design constraints.

The continuous increase of mobile data traffic calls for the design of energy-efficient content distribution mechanisms, to be incorporated in the fifth generation of mobile networks, 5G. One of the biggest concerns of the companies and the research community is to reduce the energy consumption in both the user equipments (UEs) and the network equipment. In this article, we present a novel content distribution framework called Network-Coded Cooperative (NCC) Networks, which benefits from the interplay between mobile clouds (MC) and Random Linear Network Coding (RLNC) to reduce the overall energy consumption in the devices that take part in the communication. This novel framework leads to reduced energy consumption by offloading the cellular interface to a link with greater energy efficiency, for instance, WiFi, within the mobile small cell. We evaluate the performance of our framework analytically and in practical implementation (i.e., testbed) in terms of throughput, energy savings, packet decoding ratio, latency, and synchronicity. In comparison to the conventional content distribution system, for the case of four users, the analytical model and the testbed implementation show energy savings of more than 12% and 8%, respectively. Furthermore, network usage is reduced, losses are neutralized, and the content is synchronously distributed to all users.

The performance of network elements depends heavily on their parameter setting in cellular networks. Current practice of parameter setting relies largely on expert experience and self-organizing networks, which is often suboptimal. Therefore, how to find the optimal parameter combinations automatically is increasingly concerned by mobile network operators. In this article, a collaborative learning approach is proposed to meet this demand by predicting the performance of network elements under different parameter combinations before setting. The proposed approach captures the time-aware correlation between different network elements and their parameter combinations based on factorization machine to boost the prediction accuracy. Extensive experiment results demonstrate that the proposed approach outperforms the existing prediction approaches on a large-scale real-world network dataset from a metropolitan LTE network.

Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

Network analytics provide a comprehensive picture of the network’s Quality of Service (QoS), including the End-to-End (E2E) delay. In this paper, we characterize the Core and the Radio Access Network (RAN) E2E delay of 5G networks with the Standalone (SA) and Non-Standalone (NSA) topologies when a single known Probability Density Function (PDF) is not suitable to model its distribution. To this end, multiple PDFs, denominated as components, are combined in a Gaussian Mixture Model (GMM) to represent the distribution of the E2E delay. The accuracy and computation time of the GMM is evaluated for a different number of components and a number of samples. The results presented in the paper are based on a dataset of E2E delay values sampled from both SA and NSA 5G networks. Finally, we show that the GMM can be adopted to estimate a high diversity of E2E delay patterns found in 5G networks and its computation time can be adequate for a large range of applications.

Abstract Through direct communication, device-to-device (D2D) technology can increase the overall throughput, enhance the coverage, and reduce the power consumption of cellular communications. Security will be of paramount importance in 5G, because 5G devices will directly affect our safety, such as by steering self-driving vehicles and controlling health care applications. 5G will be supporting millions of existing devices without adequate built-in security, as well as new devices whose extreme computing power will make them attractive targets for hackers.This paper presents a survey of the literature on security problems relating to D2D communications in mobile 5G networks. Issues include eavesdropping, jamming, primary user emulation attack, and injecting attack. Because multipath routing emerges as a strategy that can help combat many security problems, particularly eavesdropping, the paper contains an extensive discussion of the security implications of multipath routing. Finally, the paper describes results of a simulation that tests three path selection techniques inspired by the literature. The simulation reveals that routing information through interference disjoint paths most effectively inhibits eavesdropping.