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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]

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]

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.

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]

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]

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]

Video streaming on mobile devices necessitates a balance between Quality of Experience (QoE) and energy consumption. Given large data sizes from video, extensive amounts of battery power are required for downloading, processing, and playing each video segment. However, video quality may suffer drastically in an effort to pursue energy efficiency. In balancing these two objectives, our research incorporates advanced energy models, processor clock rates, buffer management, and network quality aware downloading. Furthermore, multi-user systems present unique challenges because the availability of network resources can vary greatly over time. We introduce an algorithm that accounts for these fluctuations and effectively balances QoE and energy consumption. Present application in LTE networks and foundations for future implementation in 5G systems are provided. We provide comparison of the performance of our algorithm with an optimal solution and show superior performance against state-of-the-art DASH-inspired algorithms. Finally, we show our algorithm gains in both QoE and energy metrics across users in large-scale multi-cell scenarios. [ABSTRACT FROM AUTHOR]

Enforcing network slice isolation in 5G Radio Access Networks (RANs) can degrade a network slicing solution’s ability to efficiently support different types of traffic. In addition, competing virtual network operators may share one network infrastructure that forces network slicing solutions to provide fair treatment between them. This work presents Flexible Priority Scheduling (FPS). This RAN network slicing solution provides isolated treatment to slices with different traffic types, further exploring their needs and flexibility to achieve a more efficient solution. It does so by defining a contract interface that allows a flexible representation of different types of traffic, from heavy throughput services to low latency and bursty traffic. Furthermore, the presented contract representation allows a fair treatment of the tenant’s traffic in the Medium Access Control (MAC) scheduler that enforces this slicing solution: Priority Adaptation Slice Scheduler (PASS). [ABSTRACT FROM AUTHOR]

Water Cycle Algorithm (WCA) is a nature-inspired metaheuristic algorithm. There is a lack of literature surrounding the use of WCA in modern cellular mobile networks. Another issue with modern networks is the importance of discretization of parameters in location management techniques. To deal with these issues, a Discrete WCA has been applied to the Reporting cell Planning (RCP) scheme for location management in Cellular Networks, with a view to optimize the Location management cost. The Location Management Cost is analyzed by taking various sizes for the cellular network for both reference as well as real network scenario, from the point of view of the reduction in the Cost per call arrival, and the number of iterations that are needed to achieve the optimum value. Comparisons are made by varying the population size within a said network size. Based on the simulation results, some salient trends are commented upon. The implementation renders an improvement of up to 23.34% in terms of absolute cost per call arrival, and an improvement up to 21.41% in terms of the iterations required to achieve minimum cost. This exhibits a promising insight into the wide scope of applicability of DWCA, and commands further works to optimize modern mobile networks. [ABSTRACT FROM AUTHOR]

The increased programmability of communication networks makes them more autonomous, and with the ability to actuate fast in response to users and networks' events. However, it is usually a difficult task to understand the root cause of the network problems, so that autonomous actuation can be provided in advance. This paper analyzes the probable root causes of reduced accessibility in 4G networks, taking into account the information of important key performance indicators (KPIs), and considering their evolution in previous time-frames. This approach resorts to interpretable machine learning models to measure the importance of each KPI in the decrease of the network accessibility in a posterior time-frame. The results show that the main root causes of reduced accessibility in the network are related with the number of failure handovers, the number of phone calls and text messages in the network, the overall download volume, and the availability of the cells. However, the main causes of reduced accessibility in each cell are more related to the number of users in each cell and its download volume produced. The results also show the number of principal component analysis (PCA) components required for a good prediction, as well as the best machine learning approach for this specific use case. In addition, we finished our considerations with a discussion about 5G network requirements where proactivity is mandatory. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations (BSs) in wireless communication has been claimed to be an effective technique for services planned in next generation cellular networks (5G and 5G+). Effective 3D deployments of BS mounted on drone (drone-BS), in the Area of Interest (AoI) may increase the Quality-of-Service (QoS) of wireless communication in the Internet of Things (IoT). In this article, we solve a dynamic deployment problem (location optimization) of multiple drone-BSs that is NP-hard according to the Air-to-Ground (ATG) model using optimization algorithms with the aim of ensuring optimal rates of coverage of the ground users (user equipments) assumed to be located at certain distance intervals in a defined urban environment. In this regard, we have developed Optimal Fitness-value Search Approaches at Continuous-data range (OFSAC-PSO and OFSAC-EML) and Optimal Fitness-value Search Approaches at Discrete-data range (OFSAD-PSO and OFSAD-EML), which are both based on Particle Swarm Optimization (PSO) and Electromagnetism-Like (EML) algorithm. In the study where Monte-Carlo simulations are run, we have created different scenarios for uniform and non-uniform distributions of user equipments (UEs). We have made comparisons according to the following metrics for the approaches that we have developed: the fitness function values of drone-BSs, AoI coverage rates, the number of iteration of simulations, drone-BS altitudes, optimal 2D coverage map, and the 3D optimal locations of drone-BSs. Our simulation results show that, based on the compared metrics, OFSAC developed with PSO-based is optimal compared to other approaches. [ABSTRACT FROM AUTHOR]

while the fifth generation (5G) cellular system is being deployed worldwide, researchers have started the investigation of the sixth generation (6G) mobile communication networks. Although the essential requirements and key usage scenarios of 6G are yet to be defined, it is believed that 6G should be able to provide intelligent and ubiquitous wireless connectivity with Terabits per second (Tbps) data rate and sub-millisecond (sub-ms) latency over three-dimensional (3D) network coverage. To achieve such goals, acquiring accurate location information of the mobile terminals is becoming extremely useful, not only for location-based services but also for improving wireless communication performance in various ways such as channel estimation, beam alignment, medium access control, routing, and network optimization. On the other hand, the advancement of communication technologies also brings new opportunities to greatly improve the localization performance, as exemplified by the anticipated centimeter-level localization accuracy in 6G by extremely large-scale multiple-input multiple-output (MIMO) and millimeter wave (mmWave) technologies. In this regard, a unified study on integrated localization and communication (ILAC) is necessary to unlock the full potential of wireless networks for dual purposes. While there are extensive studies on wireless localization or communications separately, the research on ILAC is still in its infancy. Therefore, this article aims to give a tutorial overview on ILAC towards 6G wireless networks. After a holistic survey on wireless localization basics, we present the state-of-the-art results on how wireless localization and communication inter-play with each other in various network layers, together with the main architectures and techniques for localization and communication co-design in current two-dimensional (2D) and future 3D networks with aerial-ground integration. Finally, we outline some promising future research directions for ILAC. [ABSTRACT FROM AUTHOR]

To employ millimeter wave technology in 5G networks, two inherent challenges need to be addressed in dynamic outdoor environments. Firstly, different types of obstacles can easily block the links. Secondly, the link quality can drop significantly in a mobile environment. It is critical to discriminate between the two different situations to take appropriate actions. Existing work makes the distinction based on RSSI variation measured in a time window, which is very time-consuming, leading to a large volume of data loss to achieve high accuracy. This paper proposes a learning-based prediction framework to classify link blockage and link movement efficiently and quickly. A classifier is trained with data blockage instances using different learning methods. It is used to make a prediction based on diffraction values on different multipath components formed around a receiver. Simulation results for both blockage and link movement show that the prediction framework can predict blockage with close to 90% accuracy. The accuracy of detecting the blockage (not the link movement) is measured in the experiments as well to analyze the feasibility of the method. The prediction framework can eliminate the need for having time-consuming methods to discriminate between link movement and link blockage. The simulations show that our framework does not need a large amount of training data to achieve the desired prediction accuracy. The experiments using commodity millimeter wave radios demonstrate a very high prediction accuracy. [ABSTRACT FROM AUTHOR]

With the popularity of mobile devices, the next generation of mobile networks has faced several challenges. Different applications have been emerged, with different requirements. Offering an infrastructure that meets different types of applications with specific requirements is one of these issues. In addition, due to user mobility, the traffic generated by the mobile devices in a specific location is not constant, making it difficult to reach the optimal resource allocation. In this context, network function virtualization (NFV) can be used to deploy the telecommunication stacks as virtual functions running on commodity hardware to meet users' requirements such as performance and availability. However, the deployment of virtual functions can be a complex task. To select the best placement strategy that reduces the resource usage, at the same time keeps the performance and availability of network functions is a complex task, already proven to be an NP-hard problem. Therefore, in this paper, we formulate the NFV placement as a multi-objective problem, where the risk associated with the placement and energy consumption are taken into consideration. We propose the usage of two optimization algorithms, NSGA-II and GDE3, to solve this problem. These algorithms were taken into consideration because both work with multi-objective problems and present good performance. We consider a triathlon circuit scenario based on real data from the Ironman route as an use case to evaluate and compare the algorithms. The results show that GDE3 is able to attend both objectives (minimize failure and minimize energy consumption), while the NSGA-II prioritizes energy consumption. [ABSTRACT FROM AUTHOR]

We study the problem of inter cell interference coordination (ICIC) with fixed transmit power in OFDMA-based cellular networks, in which each base station (BS) needs to decide as to which subchannel, if any, to allocate to each of its associated mobile stations (MS) for data transmission. In general, there exists a trade-off between the total throughput (sum of throughputs of all the MSs) and fairness under the allocations found by resource allocation schemes. We introduce the concept of τ - α - fairness by modifying the concept of α - fairness, which was earlier proposed in the context of congestion control for packet-switched networks. The concept of τ - α - fairness allows us to achieve arbitrary trade-offs between total throughput and fairness by selecting an appropriate value of α in [ 0 , ∞) . We show that for every α ∈ [ 0 , ∞) and every τ > 0 , the problem of finding a τ - α - fair allocation is NP-Complete. Further, we show that for every α ∈ [ 0 , ∞) , there exist thresholds such that if the potential interference levels experienced by each MS on every subchannel are above the threshold values, then the problem can be optimally solved in polynomial time by reducing it to the bipartite graph matching problem. Also, we propose a simple, distributed subchannel allocation algorithm for the ICIC problem, which is flexible, requires a small amount of time to operate, and requires information exchange among only neighboring BSs. We investigate via simulations as to how the algorithm parameters should be selected so as to achieve any desired trade-off between the total throughput and fairness. Finally, we compare the performance of the proposed algorithm with those of the simulated annealing based and opportunistic subchannel allocation algorithms in terms of the total throughput, fairness index and computational complexity. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) cellular mobile networks are expected to support mission-critical low latency applications in addition to mobile broadband services, where fourth-generation (4G) cellular networks are unable to support Ultra-Reliable Low Latency Communication (URLLC). However, it might be interesting to understand which latency requirements can be met with both 4G and 5G networks. In this paper, we discuss (1) the components contributing to the latency of cellular networks and (2) evaluate control-plane and user-plane latencies for current-generation narrowband cellular networks and point out the potential improvements to reduce the latency of these networks, (3) present, implement and evaluate latency reduction techniques for latency-critical applications. The two elements we detected, namely the short transmission time interval and the semi-persistent scheduling are very promising as they allow to shorten the delay to processing received information both into the control and data planes. We then analyze the potential of latency reduction techniques for URLLC applications. To this end, we develop these techniques into the long term evolution (LTE) module of ns-3 simulator and then evaluate the performance of the proposed techniques into two different application fields: industrial automation and intelligent transportation systems. Our detailed evaluation results from simulations indicate that LTE can satisfy the low-latency requirements for a large choice of use cases in each field. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the impact of delaying delay-tolerant calls under certain conditions in cellular networks. We propose to queue the call if the user agrees when the terminal has bad radio conditions and the system is loaded. The call is served as soon as radio conditions become good or the current load goes below a given threshold. We model the system as a continuous-time Markov chain, which allows us to compute the blocking probability, the mean waiting time and the mean service time. Numerical results show that when the proportion of users with delay tolerance is 20%, the system can bear 16% more calls with the same blocking probability, and 113% more calls if 80% of users are delay tolerant. [ABSTRACT FROM AUTHOR]

This paper studies the interference graph construction problem for device-to-device (D2D) communications underlaying cellular networks. Firstly, an improved interference graph construction method compared to the previous work in Zhang et al. (IEEE Trans Vehicular Technol 66(4):3293–3305, 2017) is proposed. The difference is mainly that, in this work the BS allocates resources for transmitting probe packets for links in a centralized manner; while in the previous work the links select resources for transmitting probe packets in a random and autonomous manner. With this "BS-allocation" method, the BS can obtain more useful information about the interference graph than the previous "random allocating" method. Secondly, this work proposes a new theoretical analysis metric, i.e., the missing rate; while previous work analyzed the traditional convergence time. This difference is caused by that this work considers the dynamic scenario in which cellular and D2D links arrive to and leave the cell dynamically, while the previous work considered the static scenario. When considering dynamic scenario, it is possible that the interference graph has changed before the BS completes the graph construction. Hence, we must evaluate the accuracy of the constructed interference graph, i.e., the missing rate, for dynamic scenario. Simulation results validate the theoretical analysis and show that the proposed method outperforms existing methods. The impact of parameters on the missing rate is also investigated. [ABSTRACT FROM AUTHOR]

The cell assignment problem is combinatorial, with increased complexity when it is tackled considering resource allocation. This paper models joint cell assignment and resource allocation for cellular heterogeneous networks, and formalizes cell assignment as an optimization problem. Exact algorithms can find optimal solutions to the cell assignment problem, but their execution time increases drastically with realistic network deployments. In turn, heuristics are able to find solutions in reasonable execution times, but they get usually stuck in local optima, thus failing to find optimal solutions. Metaheuristic approaches have been successful in finding solutions closer to the optimum one to combinatorial problems for large instances. In this paper we propose a fast and efficient heuristic that yields very competitive cell assignment solutions compared to those obtained with three of the most widely-used metaheuristics, which are known to find solutions close to the optimum due to the nature of their search space exploration. Our heuristic approach adds energy expenditure reduction in its algorithmic design. Through simulation and formal statistical analysis, the proposed scheme has been proved to produce efficient assignments in terms of the number of served users, resource allocation and energy savings, while being an order of magnitude faster than metaheuritsic-based approaches. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and massive multi-input multi-output (MIMO) have both been proposed for achieving spectrum efficiency and energy efficiency in fifth generation wireless networks. However, few studies have considered the combination of both technologies in a single system. Accordingly, this paper investigates the performance of NOMA within massive MIMO for the first time. The analysis adopts a random matrix theory approach. The approach and considers both the case where the users share the same frequency for transmission purposes and the case where each user can use different frequencies for its transmissions. A new signal-interference-to-noise ratio performance measure is proposed. Closed-form expressions for the bit error rate, outage probability and outage capacity are additionally derived. [ABSTRACT FROM AUTHOR]

Cellular genetic algorithms (cGAs) are a class of evolutionary algorithms in which the population is structured as a grid and interactions between individuals are restricted to the neighbourhood. Like any other optimisation algorithm, the cGA's efficiency lies in its ability to find an adequate balance between its exploratory and exploitive capabilities. The search selection pressure represents a good indicator of the state of that balance. From that point of view, it has been shown that the cGA's grid-to-neighbourhood relationship can be used to reflect this property. Until today, not much has been done in that area of research and many questions still surround this grid-to-neighbourhood effect. This paper describes a systematic study on the effects of that ratio on the efficiency of the cGA. This is done by proposing a dynamic cGA that adapts its ratio through evolving its grid structure using some strategy. The study is conducted using a wide range of dynamic and static ratio-control policies and, for the first time, by considering both synchronous and asynchronous cGAs. As a validation problem, we have opted for a real-world complex problem in advanced cellular networks: the users' mobility management. A wide set of differently sized and realistic instances of this problem have been used, and several comparisons have been conducted against other top-ranked solvers. The experiments showed that the ratio strategy rules the cGA's convergence, efficiency and scalability. Its effectiveness is correlated with the ratio-adaptation policy and the replacement synchronism being used. Indeed, our proposals that are based on deterministic and dynamic strategies with an asynchronous replacement were able to outperform most of the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

To improve the performance of LTE cellular networks operating in the licensed frequency spectrum, a new LTE-LAA technology has been developed that allows LTE devices to transmit data in unlicensed frequency bands already used by Wi-Fi devices. The channel access method in LTE-LAA networks, as well as in Wi-Fi networks, is based on the mechanism of carrier sense with collision avoidance; however, LTE-LAA transmission can only begin at the boundary of licensed slots. In this work, we develop a mathematical model for the joint operation of LTE-LAA and Wi-Fi networks, which allows to assess the fairness of the distribution of channel resources between LTE-LAA stations and Wi-Fi stations depending on the duration of the licensed slot, the regulation of which will become possible as part of the New Radio technology of fifth generation cellular networks. [ABSTRACT FROM AUTHOR]

Dissemination of wireless networks and mobile devices, such as smartphones, has motivated the appearance of several types of location-based services. Consequently, the interest in low-complexity cost-effective mobile positioning techniques against the traditional method based on global positioning system has emerged. An interesting alternative is to utilize radio frequency (RF) signals to estimate the location of mobile terminals, as in multilateration and RF fingerprinting techniques. In this context, the objective of this work is to propose a new radio strength signal-based user equipment location method using a machine learning regression-based model to find directly the geographical coordinates of the mobile user in cellular networks. Numerical results show that, in most cases, the proposed method can meet the location accuracy requirements established by the Federal Communications Commission for network-based localization methods. [ABSTRACT FROM AUTHOR]

The ubiquitous nature of smartphone services and the popularity of online social networking can be a lethal combination that spreads malware and computer viruses in a quick and efficient manner to a large number of Internet users. In this article, we propose a new cellular botnet named SoCellBot that exploits online social networks (OSNs) to recruit bots and uses OSN messaging systems as communication channels between bots. Our proposed botnet is the first that uses the OSN platform as a means to recruit and control mobile cellular bots. The structure and characteristics of OSNs make this botnet harder to detect, more resilient to bot failures and more cost-effective to cellular bots. We present a comprehensive study of this new type of botnet in this article. We first analyze the characteristics of the botnet via simulations. We then present an analytical model to estimate the number of infected users (smart phones) over time. We also provide a real-life implementation of the botnet on a small-scale social network as proof of concept. Finally, we study and recommend effective mechanisms to detect recruitment malware spread by such a botnet in its early stages of propagation. The objective of this work is to raise awareness of new mobile botnets that exploit OSNs to recruit and control bots so that preventive measures can be implemented to deter this kind of attack in the future. [ABSTRACT FROM AUTHOR]

We are on the entry of the exponential advancement of the internet-of-things (IoT) due to the quick development of internet-connected smart-objects. As the number of connected smart-objects increase, IoT will continue to advance by providing connectivity and interactions between the physical and the cyber world. This connectivity is characterized by low throughput, delay sensitivity, small and wide coverage, low power consumption, low device, etc. Which explains the emergence of low power wide area network (LPWAN). LPWAN technologies are an alternative promising connectivity solutions for Internet of Things. However, the lack of an overall LPWAN knowledge that present a comprehensive analysis of LPWAN technologies is presently constraining the achievement of the modern IoT vision. In this paper, we begin with a detailed analysis of the conventional high power long-range network technologies that considers IoT applications and requirements. We further point out the need for dedicated low power wide area technologies in IoT systems. In addition, we analyse the technical specification based on the PHY and MAC layers of the technologies that are already deployed, or likely to be deployed. The focus is to incorporate both standard and proprietary technologies in our study. Furthermore, we present the modelling techniques and performance metrics that are adopted in LPWAN networks analysis. Finally, challenges and open problems are presented. The main contribution of this study is that it provides an enhanced summary of the current state-of-the-art of LPWAN suitable to meet the requirements of IoT, while uniquely providing LPWAN's modelling techniques, performance metrics and their associated enablers. [ABSTRACT FROM AUTHOR]

To increase spectrum efficiency, the radio resource management with frequency reuse factor = 1 is usually adopted in multi-cell orthogonal frequency division multiple access systems. Such a reuse-one strategy results in severe co-channel interference, especially penalizing mobile users near the cell border. This paper presents an effective subcarrier-assignment algorithm to mitigate the co-channel interference for multi-cell orthogonal frequency division multiple access downlink systems. The proposed algorithm consists of an initial assignment phase and a compensation phase. In the first phase, each subcarrier is assigned to mobile users with higher channel gains and lower mutual co-channel interferences. The second phase further compensates mobile users who do not get adequate resources to meet the requirement of quality of service in the first phase. System simulation results reveal that the proposed algorithm can significantly improve the number of QoS-satisfied users compared with previous used scheme. [ABSTRACT FROM AUTHOR]

The next generation wireless cellular network is aimed to address the demands of users and emerging use cases set by industries and academia for beyond 2020. Hence, The next generation 5G networks need to achieve very high data rates, ultra-high reliability, extremely low latency, energy efficiency and fully connected coverage. To meet these demands, ultra-dense networks (UDN) or ultra-dense heterogeneous networks (UDHetNet), millimeter wave (mmWave) and multicell cooperation such as coordinated multipoint (CoMP) are the three leading technology enablers. In this paper, we have made an extensive survey of the current literature on 5G wireless communication focusing on UDN, mmWave and CoMP cooperation. We first discuss the architecture and key technology enablers to achieve the goals of the 5G system. Subsequently, we make an in-depth survey of underlying novel ultra-dense heterogeneous networks, mmWave and multicell cooperation. Moreover, we summarize and compare some of the current achievements and research findings for UDHetNet, mmWave and CoMP. Finally, we discuss the major research challenges and open issues in this active area of research. [ABSTRACT FROM AUTHOR]

Given Public Land Mobile Networks (PLMN) ubiquitous infrastructure and excellent coverage, they can be used for providing location information for various location-based services, especially in indoor environments. This paper investigates indoor positioning solutions which utilize received signal strength measurements obtained by mobile device from PLMN cells belonging to multiple mobile network operators. Two indoor positioning methods, based on Support Vector Machine learning algorithms and space-partitioning principle, are proposed. The first method utilizes Support Vector Regression (SVR) algorithm, whilst the second one introduces space-partitioning principle and the combined use of Support Vector Classification and SVR algorithms. The proposed techniques are thoroughly investigated in a real indoor environment. In addition, several criteria for choosing relevant cells for the positioning purposes have been explored. Positioning with SVR has demonstrated good results, while utilizing space-partitioning principle has further reduced the average positioning error by 27%. Moreover, the proposed solution has outperformed positioning methods based on k Nearest Neighbours and Artificial Neural Networks, when implemented in the same verification test bed. [ABSTRACT FROM AUTHOR]

In this paper, we generalize conventional time division multiple access (TDMA) wireless networks to a new type of wireless networks coined generalized wireless powered communication networks (g-WPCNs). Our prime objective is to optimize the design of g-WPCNs where nodes are equipped with radio frequency (RF) energy harvesting circuitries along with constant energy supplies. This constitutes an important step towards a generalized optimization framework for more realistic systems, beyond prior studies where nodes are solely powered by the inherently limited RF energy harvesting. Towards this objective, we formulate two optimization problems with different objective functions, namely, maximizing the sum throughput and maximizing the minimum throughput (maxmin) to address fairness. First, we study the sum throughput maximization problem, investigate its complexity and solve it efficiently using an algorithm based on alternating optimization approach. Afterwards, we shift our attention to the maxmin optimization problem to improve the fairness limitations associated with the sum throughput maximization problem. The proposed problem is generalized, compared to prior work, as it seemlessly lends itself to prior formulations in the literature as special cases representing extreme scenarios, namely, conventional TDMA wireless networks (no RF energy harvesting) and standard WPCNs, with only RF energy harvesting nodes. In addition, the generalized formulation encompasses a scenario of practical interest we introduce, namely, WPCNs with two types of nodes (with and without RF energy harvesting capability) where legacy nodes without RF energy harvesting can be utilized to enhance the system sum throughput, even beyond WPCNs with all RF energy harvesting nodes studied earlier in the literature. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show that conventional TDMA wireless networks and WPCNs with only RF energy harvesting nodes are considered as lower bounds on the performance of the generalized problem setting in terms of the maximum sum throughput and maxmin throughput. Moreover, the results reveal valuable insights and throughput-fairness trade-offs unique to our new problem setting. [ABSTRACT FROM AUTHOR]

With the explosive increase of smartphones, tablets and laptops, the past few years have witnessed the exponential rise of mobile data traffic. This has already caused data overload on cellular networks. As a result, cellular networks can not offer sufficient capacity to accommodate the numerous mobile data requirements. Therefore, it is an urgent agenda for cellular network operators to find out immediate solutions. As a promising way to tackle this problem, mobile data offloading is considered to transfer traffic which originally generates towards cellular network to complementary networks. This article aims to present a comprehensive overview of mobile data offloading. According to the participation of infrastructure, we classify the existing strategies into two major categories, namely infrastructure-based strategies and infrastructure-less strategies. Then, we review the technical aspects and discuss the state of the art in each category. After discussing advantages and disadvantages of these strategies, we also introduce the performance metrics used in offloading system, as well as the future challenges. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is expected to be part of future cellular networks. It is commonly assumed that D2D links will exist and need to be managed. However, the question with what probability D2D links will occur in a network and by which parameters this occurrence is influenced, has not been posed yet. We assume a certain event flow, consisting of (1) the occurrence of communication requirements (2) device pairing and (3) mode selection, that leads to the existence of a D2D flow. Under this event flow, we are interested in the influence of service penetration among UEs, pairing strategies and mode selection types on the occurrence probabilities of D2D communication. We propose a stochastic geometry based formula framework which captures the mean number of D2D links that occur in a certain area. This framework reveals a saturation effect of D2D flows and an interesting interplay among the device pairing and mode selection step. We show that any existing network that uses the considered action flow can be described with this framework and further, by simulation, that it can be used to correctly predict D2D occurrence probabilities. [ABSTRACT FROM AUTHOR]

In mammals, circadian rhythms, such as sleep/wake cycles, are regulated by the central circadian clock located in the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN consists of thousands of individual neurons, which exhibit circadian rhythms. They synchronize with each other and produce robust and stable oscillations. Although several neurotransmitters are expressed in the SCN, almost all SCN neurons are γ-amino butyric acid (GABA)-ergic. Several studies have attempted to understand the roles of GABA in the SCN; however, precise mechanisms of the action of GABA in the SCN are still unclear. GABA exhibits excitatory and/or inhibitory characteristics depending on the circadian phase or region in the SCN. It can both synchronize and destabilize cellular circadian rhythms in individual SCN cells. Differing environmental light conditions, such as a long photoperiod, result in the decoupling of circadian oscillators of the dorsal and ventral SCN. This is due to high intracellular chloride concentrations in the dorsal SCN. Because mice with functional GABA deficiency, such as vesicular GABA transporter- and glutamate decarboxylase-deficient mice, are neonatal lethal, research has been limited to pharmacological approaches. Furthermore, different recording methods have been used to understand the roles of GABA in the SCN. The excitability of GABAergic neurons also changes during the postnatal period. Although there are technical difficulties in understanding the functions of GABA in the SCN, technical developments may help uncover new roles of GABA in circadian physiology and behavior. [ABSTRACT FROM AUTHOR]

Satisfying the increasing demand for high data rates has become a main challenge for cellular network operators. Moreover, the demand for video traffic in cellular networks has been continuously increasing. In 2016, wireless video accounted for more than half of the total mobile data traffic, and it is expected to further increase in the upcoming years. This will increase the amount of data to be transmitted in cellular networks, and further increase the challenge for cellular network operators. Device-to-Device (D2D) communication, introduced by the Long Term Evolution-Advanced (LTE-A) standard, is a new communication technique that allows direct communication between devices in cellular networks without going through the Base-Station (BS). We present two algorithms for improving the throughput of video transmission in cellular networks. The algorithms are called Cached and Segmented Video Download (CSVD), and DIStributed, Cached, and Segmented video download (DISCS). The algorithms send segments of video files to selected User Equipments (UEs) in the cellular network, to cache and forward them to requesting UEs using D2D communication. We study the performance of both algorithms analytically in terms of the hit ratio. Furthermore, we use the Discrete EVent System Specification (DEVS) formalism to build an LTE-A network model and use the model to study the performance of CSVD and DISCS in terms of the cell’s aggregate data rate as well as the average data rate. Simulation results show that CSVD and DISCS achieve significant performance improvements in terms of the aggregate and average data rates. [ABSTRACT FROM AUTHOR]

The world of mobile communications is poised for yet another disruptive transformation - the introduction of 5G. It will change the way mobile phones communicate. New technologies at the device level and novel network architectures to manage them will be soon seen everywhere. This article explores what 5G is all about. It also talks briefly about the preparations underway across the globe to deploy 5G by 2020. [ABSTRACT FROM AUTHOR]

Full duplex communication highly improves spectrum efficiency of a wireless communication link. However, when it is applied to a cellular network, the capacity gain from this technology remains unknown. The reason is that full duplex communication changes the aggregate interference experienced by each communication link in cellular networks. In this paper, the capacity gain from full duplex communication is studied for cellular networks of 4G and beyond, where the same frequency channel is adopted in each cell. A two-layer Poisson point process (PPP) is adopted to model the network topology, and stochastic geometry is employed to derive the coverage probability and the average capacity of typical link in a cellular network. On the basis of these derived parameters, the capacity gain from full duplex communication is determined. Numerical results reveal that without mutual interference cancellation (MIC), the capacity gain is small under various power levels; with perfect MIC at base stations, the capacity gain can exceed 60%; with imperfect MIC at base stations, the capacity gain decreases quickly even with a slight drop of MIC performance. [ABSTRACT FROM AUTHOR]

The issues of content placement and content replication for video-on-demand streaming over cellular networks are addressed in this study. Using many replications of a relatively small number of the most popular items a significant performance improvement can be achieved. Our method was verified using real video streaming data taken from traces of live content distribution networks. Simulation results show that replicating a relatively small number of video files can significantly reduce the incoming bandwidth from the Internet backbone, as well as the (time) latency for content delivery. The proposed scheme is particularly suitable for IP-based TV services, for which the content popularity can be very often predicted with relatively high accuracy. In addition, we propose a hybrid cache management scheme, in which the cache is partitioned into two components. The first component is for long-term items, and it is updated relatively rarely, while the second component is updated more frequently. [ABSTRACT FROM AUTHOR]

In relay-assisted cellular networks, mobile stations are connected to base station through two or more single-hop communication links, where the intermediate nodes act as relay stations (RSs). The focus of this paper is on two-hop relay assisted cellular networks, where optimal relay placement is a crucial issue for achieving maximum extension of the cell coverage. However, the location of RS has significant impact on signal-to-interference plus noise ratio (i.e., SINR) and outage probability experienced on the access and backhaul links. Moreover, the frequency re-use factor also has significant influence on the SINR. In this paper, we develop analytical models for computing the SINR and outage probability performance of a two-hop relay assisted cellular network for both downlink (DL) as well as uplink (UL) transmission scenarios, considering the impact of path loss, shadowing, Nakagami fading and co-channel interference. We then investigate optimal placement of RS while satisfying the required criterion on probability of correct decoding, initially by considering the DL scenario alone and then by considering both DL and UL scenarios jointly. Through extensive evaluations, we report the impact of realistic propagation models on outage probability, optimal relay position and the cell coverage radius. Further, the model can be used to find the impact of co-channel re-use factor on optimal relay positioning in two-hop cellular networks. [ABSTRACT FROM AUTHOR]

This paper presents a framework of cellular based cognitive-radio vehicular ad hoc networks (CCR-VANETs) that consists of a cellular network (primary network) and VANET (secondary network). These coexisting networks share the uplink spectrum of the cellular network. Although some scaling law-based results on VANETs have been presented for the description of performance changing patterns, they are unsuitable for CCR-VANET and cannot be directly used for estimating the capacity of the communication pairs in the entire network. In this study, we apply the interference-based transmission capacity analysis for CCR-VANET scenario. The classic car-following model is used to describe the moving pattern of all buses and vehicles, and a two-phase CSMA based on the opportunistic spectrum access (OSA) protocol of the secondary network is adopted. Then the calculation of the transmission opportunity is obtained. After that, the outage probability is analyzed, in which just the first layer of interferers in both primary and secondary networks are considered, as a result of the bound effect of interference power in the wireless network. With the obtained results on outage probability under the worst interfered scenario, we characterize a lower bound on transmission capacity of the secondary network. Finally, the simulation results are conducted to validate our analytical results, and also to give an analysis of the average transmission capacity trade-off between the primary and secondary networks. [ABSTRACT FROM AUTHOR]

This work presents solutions to enhance the performance of urban cellular networks based on Multiple-Input Multiple-Output (MIMO) systems. First, some adjustments to the Base Stations (BSs) are proposed and evaluated, namely the reorientation of the antennas and two new placements of the BSs, while maintaining or even diminishing the number of required BSs. Afterwards, a cooperative wireless system is combined with a relay selection scheme, also proposed here, and its performance is assessed; this scheme allows to exploit the MIMO spatial multiplexing gain in a distributed and simple manner. This study is performed for a realistic urban environment, i.e., using a simulator that incorporates realistic underlying models (test environment, mobility and radio propagation models), and it is envisioned for market available technology, such as Long Term Evolution (LTE) and Bluetooth. The simulation results show that not only all the proposed placements of the BSs present a superior performance with respect to the traditional placement, but also the relay selection scheme, when incorporated in the cooperative system, can be a valid approach to increase the MIMO channel capacity. [ABSTRACT FROM AUTHOR]

We study the layered video transmission optimization problem in the cellular networks, in which all User Equipments (UEs) require the same video content simultaneously via the cellular downlink transmission from the Base Station (BS) and Device-to-Device (D2D) transmission from other UEs. First, we propose a probability-based framework for each video layer to measure the video playback quality in terms of the outage probability. Next, the layered video transmission optimization problem is formulated as a Peak Signal-to-Noise Ratio (PSNR) loss minimization problem, which is then converted to an unmatured probability minimization problem by adding a differentiation weight on each layer based on its importance to the playback quality. In addition, we prove the formulated problem to be a log-convex optimization problem. Finally, we conduct extensive simulations to show that the proposed approach achieves the optimal allocation of the broadcast duration for each layer under various network conditions. [ABSTRACT FROM AUTHOR]

In this paper, mathematical characteristics of uplink and downlink interference regions in device-to-device (D2D) communications underlaying cellular networks are analyzed. We consider that one CUE and one pair of D2D user equipments (DUEs) coexist and share uplink and downlink resources of cellular networks. The DUE pair consists of a D2D transmitting user equipment and a D2D receiving user equipment. Therefore, additional interference between the CUE and the DUE pair is introduced. First, uplink and downlink interference regions are defined and described. Next, mathematical characteristics of uplink and downlink interference regions are derived. Finally, blocking probability is formulated based on the mathematical characteristics values. The analysis results show that uplink resources are more suitable to be shared for the CUE and the DUE pair, but the difference between uplink and downlink is not significant. Meanwhile, the simulation results show that theoretical values of blocking probability well match simulation results. The results can provide a theoretical foundation for resource allocation and optimization of D2D communications. [ABSTRACT FROM AUTHOR]

The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and is in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an attractor-aware offloading ratio selection (AORS) algorithm, which can adaptive select an optimum offloading ratio based on attractor selection for the current networks environment. In the proposed algorithm, the throughput of AP and the cellular load corresponding to the coverage area of the AP, are mapped into the cell activity, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor for each users, the optimal offloading ratio $$\phi $$ , to adapt to the dynamic network environment. Hence, according to the offloading ratio $$\phi $$ , the part of the cellular traffic will be transmitted via WiFi networks. Through simulation, we show that the proposed AORS algorithm outperforms the existing ones with 42 % higher heterogeneous network throughput in a dense traffic environment. [ABSTRACT FROM AUTHOR]

With the development of wireless cloud computing, video caching in the radio access network (RAN) of cellular networks has attracted extensive attention due to its lower delay and higher resource utilization efficiency. Nevertheless, existing video cache management mostly made decisions only according to the video coding requirements, without considering users' individual requirements for the video service and without making full use of the abundant network-side information in real time or from statistics. In this paper, we propose a new QoE (quality of experience)-driven video cache management scheme with the consideration of the parameters from three parties (i.e. client, base station, and RAN cache server) for video provisioning, with statistics of video popularities and under limited cache capacity. Specifically, through experiments we establish the mapping relationship between the QoE value and the three key parameters (i.e. the request rate from the client, the bandwidth of air interface, and the response rate of the cache server). Firstly, we allocate different gross caches for different video clips according to their popularities. Secondly, we optimize the cache space allocation for each individual video clip based on the QoE mapping relationship and the different models of the request rate and the bandwidth, with the convex optimization method and the Lagrange multiplier solution. The experiments results indicate that the proposed video cache scheme has better QoE performance under the constraints of the total cache capacity, specific distributions of the request rate and the bandwidth. [ABSTRACT FROM AUTHOR]

Explosive growth of different type of wireless networks in the last decade has raised an issue of influence of electromagnetic fields originating from radio frequencies to human health. Thus, more and more researchers working on the solutions for the next generation wireless communication systems now have in mind the necessity of keeping the level of radiated power on the minimum level necessary for achieving the required performances. In this paper we analyze solutions for human exposure reduction in dual-hop Orthogonal Frequency Division Multiplexing (OFDM) based decode-and-forward (DF) relay systems, as this type of relay system is adopted for LTE-Advanced networks, also denoted as 4G. In our approach we seek for the solutions that keep the certain performance metric, i.e. system capacity in this analysis, on the same level like in baseline OFDM DF relay system, but attain exposure reduction from relay station (R) on downlink (DL). In one of the considered solutions, R station, having more than one antenna for DL communication, implements transmit antenna selection (TAS) on subcarrier basis, jointly with ordered subcarrier mapping (SCM). TAS solution assumes that on each subcarrier position, the transmit antenna having the best subcarrier channel transfer function is chosen. Ordered SCM is a technique where subcarriers from the first hop are mapped to corresponding subcarriers on the second hop in accordance to their instantaneous signal-to noise ratios. It is proven to be a mapping scheme that maximizes the achievable capacity in OFDM based DF relay systems, enabling bit error rate improvement at the same time. Besides this solution, we analyze the level of human exposure reduction in the cases where only SCM technique is implemented at R, as well as where only TAS is implemented. We have developed a simulation model for assessing the level of human exposure to EMF, with included real-case simulation parameters given in LTE-Advanced relay reference scenario. In this way, we have obtained and analyzed data on the level of human exposure reduction attained with the proposed solutions in indoor and outdoor environment, and for the different positions of end-users relative to R station. [ABSTRACT FROM AUTHOR]

Future 5G cellular networks will need to deliver significantly increased system capacity and user data rates. This expected growth along with today's shortage of spectrum raises the need for new frequency allocations. Millimeter wave spectrum is emerging as a suitable candidate with a vast amount of available bandwidth (around 60 GHz). Extending cellular networks communications on millimeter wave frequencies requires extensive measurement campaigns and analysis of signals propagation characteristics. This paper gives an overview of recent measurement studies and results used for modeling millimeter wave channel behavior in different propagation environments. Also , the paper provides a preliminary simulation analysis of a hybrid LTE-millimeter wave heterogeneous network, which suggests that Gbps user data rates are achievable with sufficient beamforming gains. However, the millimeter wave cellular extensions will require architectural changes to address the technical issues spanning from the transceivers design to the operational procedures in both access and backhaul network parts. [ABSTRACT FROM AUTHOR]

Abstract Proteins targeting the same subcellular localization tend to participate in mutual protein–protein interactions (PPIs) and are often functionally associated. Here, we investigated the relationship between disease‐associated proteins and their subcellular localizations, based on the assumption that protein pairs associated with phenotypically similar diseases are more likely to be connected via subcellular localization. The spatial constraints from subcellular localization significantly strengthened the disease associations of the proteins connected by subcellular localizations. In particular, certain disease types were more prevalent in specific subcellular localizations. We analyzed the enrichment of disease phenotypes within subcellular localizations, and found that there exists a significant correlation between disease classes and subcellular localizations. Furthermore, we found that two diseases displayed high comorbidity when disease‐associated proteins were connected via subcellular localization. We newly explained 7584 disease pairs by using the context of protein subcellular localization, which had not been identified using shared genes or PPIs only. Our result establishes a direct correlation between protein subcellular localization and disease association, and helps to understand the mechanism of human disease progression.