Search

Abstract Wireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually shows high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods lack the abilities of modeling the dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) is proposed, which uses Conv-LSTM or Conv-GRU for neighbor data, daily cycle data, and weekly cycle data modeling, and then assigns different weights to the three kinds of feature data through the attention layer, improves their feature extraction ability, and suppresses the feature information that interferes with the prediction time. Finally, the model is combined with timestamp feature embedding, multiple cross-domain data fusion, and jointly with other models to assist the model in traffic prediction. Experimental results show that compared with the existing models, the prediction performance of the proposed model is better. Among them, the RMSE performance of the att-MCSTCNet (Conv-LSTM) model on Sms, Call, and Internet datasets is improved by 13.70 ~ 54.96%, 10.50 ~ 28.15%, and 35.85 ~ 100.23%, respectively, compared with other existing models. The RMSE performance of the att-MCSTCNet (Conv-GRU) model on Sms, Call, and Internet datasets is about 14.56 ~ 55.82%, 12.24 ~ 29.89%, and 38.79 ~ 103.17% higher than other existing models, respectively.

The past decade witnessed a huge interest in the communication machinery called tunneling nanotubes (TNTs) which is a novel, contact-dependent type of intercellular protein transfer (IPT). As the IPT phenomenon plays a particular role in the cross-talk between cells, including cancer cells as well as in the immune and nervous systems, it therefore participates in remodeling of the cellular networks. The following review focuses on the placing the role of tunneling nanotube-mediated protein transfer between distant cells. Firstly, we describe different screening methods used to study IPT including tunneling nanotubes. Further, we present various examples of TNT-mediated protein transfer in the immune system, cancer microenvironment and in the nervous system, with particular attention to the methods used to verify the transfer of individual proteins.

Industrial Internet of Things (IIoT) adopting emerging cellular networks attracted great attention in various industrial fields, including the building sector. However, the capability of IIoT and the performance of cellular networks when applie for building automation (BA), especially for time-critical control tasks, are not effectively evaluated yet. The potential impacts of network constraints on the control performance also need further investigation for the alternative architecture. These fundamental questions have not been addressed sufficiently yet. This study therefore proposes a cellular network-based IIoT architecture as a newly-proposed and recommendable solution for building automation systems. The architecture consists of smart field-level devices embedded with cellular network-enabled IoT controllers and a configured cellular network. Performance of the latest cellular networks, including 4th generation (4G) and 5th generation (5G) networks, are experimentally tested under different conditions. A typical building time-critical control task is selected to assess the capability of proposed architecture. The results show that the proposed IIoT architecture adopting 4G and 5G networks has good capability for the time-critical control tasks. • A cellular network-based IIoT architecture is proposed for building automation. • Prototypes of integrated smart field devices via cellular network are developed. • Feedback process control using IIoT architecture and 4G/5G network is evaluated. • Results show good performance of process control, a time-critical task in buildings. • Results show the capacity of 5G networks for meeting building control requirements. [ABSTRACT FROM AUTHOR]

Kalman filter is a well-established accuracy correction method in control, guidance, and navigation. With the popularity of mobile communication and ICT, Kalman Filter has been used in many new applications related to positioning based on spatiotemporal data from the cellular network. Despite the low accuracy compared to Global Positioning System, the method is an excellent supplement to other positioning technologies. It is often used in sensor fusion setups as a complementary source. One of the reasons for the Kalman Filter’s inaccuracy lies in naive radio coverage approximation techniques based on multivariate normal distributions assumed by previous studies. Therefore, in this paper, we evaluated those disadvantages and proposed a Gaussian mixtures model to address the non-arbitrary shape of the radio cells’ coverage area. Having incorporated the Gaussian mixtures model into Switching Kalman Filter, we achieved better accuracy in positioning within the cellular network.

Of particular interest within fifth generation (5G) cellular networks are the typical levels of radiofrequency (RF) electromagnetic fields (EMFs) emitted by ‘small cells’, low-power base stations, which are installed such that both workers and members of the general public can come in close proximity with them. In this study, RF-EMF measurements were performed near two 5G New Radio (NR) base stations, one with an Advanced Antenna System (AAS) capable of beamforming and the other a traditional microcell. At various positions near the base stations, with distances ranging between 0.5 m and 100 m, both the worst-case and time-averaged field levels under maximized downlink traffic load were assessed. Moreover, from these measurements, estimates were made of the typical exposures for various cases involving users and non-users. Comparison to the maximum permissible exposure limits issued by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) resulted in maximum exposure ratios of 0.15 (occupational, at 0.5 m) and 0.68 (general public, at 1.3 m). The exposure of non-users was potentially much lower, depending on the activity of other users serviced by the base station and its beamforming capabilities: 5 to 30 times lower in the case of an AAS base station compared to barely lower to 30 times lower for a traditional antenna.

The current level of development of the cellular services market, qualitative changes in the means and methods of providing services, and the increased volume and diversity of information circulating in cellular networks have evoked the need for service quality assessment system improvement. To maintain competitiveness, the main efforts of operators are aimed at improving quality and increasing the service life of subscribers in the network by ensuring the required level of customer satisfaction in high-quality services through a system of organizational–technical and socioeconomic measures to bring the achieved level of quality-of-service (QoS) provision in accordance with the existing, emerging, or projected needs of subscribers. To achieve improved QoS provision, a functional relationship between network parameters has been established: The impact of key performance indicators on a key quality indicator through the use of cubic Hermitian splines (CHS) has been determined. The use of splines, as a signal model, can significantly improve the quality of signal processing due to the continuity of values and partial derivatives in the joints of spline gluing. CHS are characterized by calculation simplicity, as they provide high-speed computing, which, in turn, is important for real-time work when processing large data sets. Experimentally, it was shown that the use of splines allows for the ability to calculate statistical estimates of the required parameters of spline approximations, but also their confidence intervals, which increases the accuracy and probability of further calculations and is a distinct advantage of this approach. Also, the methods of service quality management through machine learning have been improved, which are effective tools for modeling integrated indicators of communication services quality control, monitoring their condition in terms of final copies of services, determining the causes of degradation, and reporting. It monitors the service performance provided by the cellular operator. Finally, the method checks the readiness and availability of services, detects network nodes through which quality degradation occurs, collects various quality metrics, and compares them with pre-installed quality indicators. [ABSTRACT FROM AUTHOR]

The non-orthogonal multiple access (NOMA), which is a power domain multiplexing technology, has received considerable attention for 5G and beyond cellular systems due to its higher spectral efficiency. Coordinated multipoint (CoMP) transmission has been adapted in NOMA systems to improve the performance of a cell edge user. Conventional CoMP combines received signals from multiple sources using maximum ratio combining (MRC) in order to improve the user's signal-to-noise ratio (SNR). While MRC provides the highest diversity gain of any diversity combining technology, it requires latency for co-phasing and combining the received signals. Recently, urban air mobility (UAM) has emerged as a viable option for future mobility in metropolitan areas in order to avoid terrestrial traffic congestion. However, a flight control information of a UAM, especially an unmanned UAM is very sensitive to latency. Hence MRC is not recommendable for UAM due to the inherent latency. We consider selection combining (SC) which has less latency rather than MRC as a combining technology in CoMP. This study derives the outage performance of UAM with selective CoMP in closed-form. The analytical results are compared and verified with that of the Monte Carlo simulation. The outage performances of a UAM in NOMA-NOMA and NOMA-OMA modes are compared. It is shown that the NOMA-OMA mode has better performance than the NOMA-NOMA mode irrespective of the given conditions, and has over 3 dB SNR gains under the given conditions. When the performance of UAM in the NOMA-NOMA mode is not satisfying, it is recommended to switch to the NOMA-OMA mode based on this study. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a novel technology that provides fast data processing with efficiency on 5G networks. It can be propitious in Internet of Things (IoT)-based contexts, as it renders a cloud-computing and IT services environment. Moreover, MEC has no terminal servers, as it is often deployed near the network’s edge. However, the unique features and working principles of MEC will lead to a number of new security and privacy-related concerns. In this paper, the security issues are discussed along with their multiple solutions. Finally, suggestions to strengthen the security of MEC services are offered. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the fast growth in telecommunication (Telco) techniques from 2G to upcoming 5G. Precise outdoor localization is important for Telco operators to manage, operate and optimize Telco networks. Differing from GPS, Telco localization is a technique employed by Telco operators to localize outdoor mobile devices by using measurement report (MR) data. When given MR samples containing noisy signals (e.g., caused by Telco signal interference and attenuation), Telco localization often suffers from high errors. To this end, the main focus of this paper is how to improve Telco localization accuracy via the algorithms to detect and repair outlier positions with high errors. Specifically, we propose a context-aware Telco localization technique, namely ${\sf RLoc}$ RLoc , which consists of three main components: a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results by better ones (ideally ground truth positions). Unlike most existing works to detect and repair every flawed MR sample independently, we instead take into account spatio-temporal locality of MR locations and exploit trajectory context to detect and repair flawed positions. Our experiments on the real MR data sets from 2G GSM and 4G LTE Telco networks verify that our work ${\sf RLoc}$ RLoc can greatly improve Telco location accuracy. For example, ${\sf RLoc}$ RLoc on a large 4G MR data set can achieve 32.2 meters of median errors, around 17.4 percent better than state-of-the-art. [ABSTRACT FROM AUTHOR]

We are witnessing a new era in autonomous systems with unprecedented user experiences, excessively recovered road safety and air quality, a wide range of transportation conditions and utilization cases, as well as a plethora of advanced time-sensitive applications. To realise this ambitious vision, Vehicular Ad-hoc Networks (VANET) and drones must have significantly improved communication. They must be remarkably intelligent and capable of simultaneously supporting hyper-fast, ultra-reliable, uninterrupted service, as well as low-latency massive information exchange. Although the Sixth Generation (6G) is successful in providing a seamless integration of heterogeneous elements as well as offering high coverage area with maximizing resource utilization; however, the accomplishment of faster computation, reducing bandwidth usage, low power consumption, and high throughput is remaining critical more than other purposes. This study introduces an End-To-End Oriented Management Architecture (EEOMA) based on fog computing and software-defined network (SDN) technologies for boosting the performance in hybrid networks of Vehicular Ad hoc Network (VANET) and Drone. Two techniques are proposed through EEOMA to manage the data transmission on both the SDN and the drones and meet network constraints: (i) a transport service aggregation approach for simplifying the amount of traffic data traveling across the network bandwidth and (ii) a normalized throughput of a channel with adjusting the interference ratio that indicates medium access control (MAC) layer overhead. The experimental results show that there is 1.2% to 6.1% enhancement of packet delivery and packet loss ratios. More trustworthiness in performance measurement is reached by the proposed EEOMA in terms of total throughput, response time, and power consumption. [ABSTRACT FROM AUTHOR]

In cellular networks, a deep knowledge of the traffic demand pattern in each cell is essential in network planning and optimization tasks. However, a precise forecast of the traffic time series per cell is hard to achieve, due to the noise originated by abnormal local events. In particular, mass social events (e.g., concerts, conventions, sport events…) have a strong impact on traffic demand. In this paper, a data-driven model to estimate the impact of local events on cellular traffic is presented. The model is trained with a large dataset of geotagged social events taken from public event databases and hourly traffic data from a live Long Term Evolution (LTE) network. The resulting model is combined with a traffic forecast module based on a multi-task deep-learning architecture to predict the hourly traffic series with scheduled mass events. Model assessment is performed over a real dataset created with geolocated social event information collected from public event directories and hourly cell traffic measurements during two months in a LTE network. Results show that the addition of the proposed model significantly improves traffic forecasts in the presence of massive events.

In modern networks, the use of drones as mobile base stations (MBSs) has been discussed for coverage flexibility. However, the realization of drone-based networks raises several issues. One of critical issues is drones are extremely power-hungry. To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones. Motivated by the fact that the charging drones also need to be charged, we deploy ground-mounted charging towers for delivering energy to the charging drones. We introduce a new energy-efficiency maximization problem, which is partitioned into two independently separable tasks. More specifically, as our first optimization task, two-stage charging matching is proposed due to the inherent nature of our network model, where the first matching aims to schedule between charging towers and charging drones while the second matching solves the scheduling between charging drones and MBS drones. We analyze how to convert the formulation containing non-convex terms to another one only with convex terms. As our second optimization task, each MBS drone conducts energy-aware time-average transmit power allocation minimization subject to stability via Lyapunov optimization. Our solutions enable the MBS drones to extend their lifetimes; in turn, network coverage-time can be extended.

This paper provides a comprehensive survey on the coexistence of cellular and IEEE 802.11 standards from a holistic viewpoint that takes into account the coexistence of all existing and future cellular and IEEE 802.11 standards in all the available unlicensed spectrum bands. Unlike existing survey works focusing mostly on any unlicensed band and/or standard, we start by giving an overview of unlicensed spectrum bands, including 2.4 GHz, 5 GHz, 6 GHz, and 60 GHz. We then review the operation of cellular technologies, namely Long-Term Evolution Unlicensed (LTE-U), Licensed Assisted Access (LAA), and New Radio Unlicensed (NR-U), worldwide in the unlicensed spectrum bands. Further, we summarize scenarios and categories of coexistence mechanisms, conditions for a fair coexistence, and coexistence-related features. An extensive study on the coexistence mechanisms, deployment scenarios, as well as standardization efforts for the coexistence between cellular and IEEE 802.11 standards, is carried out. Finally, we highlight the coexistence challenges and open problems, the convergence of the Third Generation Partnership Project (3GPP) and IEEE standards, as well as future research directions. Moreover, to provide insights on the relative measures, we also carry out comparative studies of several key concerns with regard to the coexistence, namely unlicensed spectrum band, regulatory requirement, coexistence mechanism, and cellular standardization effort. Each study presents a comparison among potential features of one of these concerns in tabular forms. Finally, we summarize key lessons that are learned and discussed throughout the paper.

Recently, the non-orthogonal multiple access (NOMA) technique, which transmits multiple users in the same time-frequency block, has been focused on for future cellular networks. Conventional NOMA-based aerial cellular networks consider an unmanned aerial vehicle (UAV) as an aerial user, in which secure control information for UAV is important. In recent days, the deployment of urban air mobility (UAM) has been actively discussed for a new aerial transport service in an urban area. Since UAM conveys passengers, both the traffic and the control information for passengers and a vehicle are important. Therefore, this paper considers a NOMA-based aerial cellular network with a UAM as an aerial user and a ground user as a terrestrial user. And we derive closed-form outage probabilities of the forward and reverse links of UAM, and the Monet-Carlo simulation verifies the results. Further, the condition to maintain the balance between the forward and the reverse links for the identical quality of service (QoS) is derived. Numerical results show that the outage probability of the forward link decreases with the increase of the received signal-to-noise ratio (SNR). However, we noticed that the outage probability of the reverse link shows the error floor, which does not decrease with the received SNR and remains constant, caused by the interference from the ground user (GU). Also, it is shown that there is an average transmit power of UAM to conform the link balance regardless of strong or weak users. [ABSTRACT FROM AUTHOR]

In this paper, we study the three-dimensional (3D) path planning problem for cellular-connected unmanned aerial vehicle (UAV) taking into account the impact of 3D antenna radiation patterns. The cellular-connected UAV has a mission to travel from an initial location to a destination. In this process, there is a trade-off between the flight time and the expected communication outages duration, which requires planning a suitable route to avoid the weak coverage region. However, as the existing cellular networks are primarily designed for ground coverage, the communication coverage in the sky tends to be intermittent and irregular due to the potential for severe interference and obstructions (such by buildings), which brings great challenges to 3D path planning. To address this challenge, we first construct a 3D coverage map that stores the expected outage probability over each locations. We then propose a multi-step dueling DDQN (multi-step D3QN) based algorithm to design the local optimal UAV path by leveraging the constructed coverage map. In this algorithm, the UAV acts as the agent to learn the appropriate action to complete the flight mission. Numerical results show the effectiveness of the proposed algorithm for connectivity-aware UAV path planning and the superiority of 3D path design over its 2D counterparts. [ABSTRACT FROM AUTHOR]

Emergence of various types of wireless applications has brought about fast growing and diversified traffic in cellular networks. To gain a comprehensive understanding of the influences caused by the differentiated and dynamic traffic is vital for the design of the next-generation wireless networks. In this paper, we develop a mathematical framework for meta-distribution analysis by utilizing queueing theory and stochastic geometry to capture the spatial (geographical location) and temporal randomness (queue status) of traffic. We derive the ${k}$ -th moment of the conditional successful transmission probability (STP) and the closed-form expressions of the meta distribution for the cell-center users (CCUs) and the cell-edge users (CEUs), respectively. The results are further extended to obtain the analytical expression of the meta distribution by taking the temporal random arrival of traffic into consideration. Moreover, the mean local delays for the CCUs and CEUs are derived. Finally, the impact of key network parameters on the meta distribution and the corresponding mean local delay is investigated. [ABSTRACT FROM AUTHOR]

Haptic communication is a form of non-verbal communication involving touch and feel. Haptic communication is a major requirement for the Tactile Internet that deals with mechanism to transmit touch, feel, and skills between two geographically distant entities, in realtime. Lately, haptic communication has become an essential requirement for variety of realtime robotic and Augmented/Virtual Reality applications. With very stringent delay and reliability requirements, haptic communication poses significant challenges for network engineers. This becomes further complicated when the cellular technology is used as the access medium for haptic communication. Since cellular networks are resource constrained, accommodating haptic users along with existing non-haptic users become a hard scheduling problem. In this paper, we propose an efficient latency-aware uplink resource allocation scheme satisfying end-to-end delay requirements of haptic users in a Long Term Evolution based cellular network. The proposed scheme first predicts the downlink and processing delays for users' transmission flows. Subsequently, the model apply an optimal scheduling scheme for the uplink transmissions which satisfies expected end-to-end latency constraint. Our extensive simulations indicate that the proposed algorithm outperforms some of the widely used state-of-the-art scheduling schemes. [ABSTRACT FROM AUTHOR]

High-throughput unmanned aerial vehicle (UAV) communication may unleash the true potential of novel applications for aerial vehicles but also represents a threat for cellular networks due to the high levels of generated interference. In this article, we investigate how a beamforming system installed on board a UAV can be efficiently used to ensure high-throughput uplink UAV communications with minimum impact on the services provided to users on the ground. We study two potential benefits of beamforming, namely, spatial filtering of interference and load balancing, considering different beam switching methodologies. Our analysis is based on system-level simulations followed by a series of measurement campaigns in live Long-Term Evolution (LTE) networks. Our results show that using UAV-side beamforming has a great potential to increase uplink throughput of a UAV while mitigating interference. When beamforming is used, even up to twice as many UAVs may be served within a network compared with UAVs using omni-directional antennas, assuming a constant uplink throughput target. However, to fully exploit the potential of beamforming, a standardized solution ensuring alignment between network operators and UAV manufacturers is required.

The application of millimeter wave (mmWave) spectrum for the next generation broadband communications has received significant attention recently. In this work, we investigate the performance limits for the coexistence of the geo-stationary orbit (GEO) broadband satellite networks and terrestrial mmWave cellular networks. Based on the assumption that the statistical channel state information (SCSI) is available at the terrestrial base station (BS), we propose a virtual uplink based transmit beamforming (BF) algorithm to maximize the ergodic capacity of the terrestrial user while satisfying the interference probability constraint of the satellite users. Furthermore, we derive the closed-form expressions for the outage probability (OP) and ergodic capacity (EC) of the terrestrial user. Besides, the asymptotic OP expression at high signal-to-noise ratio (SNR) is also developed in terms of diversity order and array gain of the terrestrial user. Finally, numerical results are given to confirm the validity of the proposed BF scheme and theoretical derivations, and reveal the impact of key parameters on the performance of the terrestrial user coexisting with the multiple satellite users.

With the development of the mobile Internet, the demand for indoor location based services(ILBS) is increasing. Applications and devices in indoor environments can determine their position based on Received Signal Strength Indication(RSSI) of bluetooth Beacons. However, because of multipath effects, shadow fading and the blockage of obstacles, RSSI values are unstable for positioning. The propagation model are various in different environments, which have caused great difficulty in high-precision indoor positioning. In this paper, a novel positioning algorithm based on cellular network and bluetooth Beacon's RSSI is proposed for ILBS. The whole algorithm is divided into two parts: Offline Preparation and Online Positioning. During Offline Preparation, Beacon node is cellular networked in regular quadrilateral; a universal ranging model is derived from traditional ranging model for high-precision positioning. During Online Positioning, missing RSSI values are replaced firstly; next, Outliers Removed Median-Kalman Filter is used to process RSSI, which makes the signal smoother to range; then, the cell in which the mobile terminal is located is determined according to the strongest four RSSI values; after that, using filtered RSSI, distances without height influence could be calculated; finally, Weighted Multi-Point Positioning Algorithm With Cellular Network Assistance is used to calculate the real-time location of the mobile terminal. Experiment results show that our algorithm achieves average accuracy in 0.3m-0.5m and limits the maximum error within 0.8 m.

In existing dynamic spectrum sharing (DSS) systems, each node arbitrates channel access independently based on carrier-sensing mechanisms such as the listen-before-talk (LBT) protocol. Owing to the uncoordinated channel access between all nodes, the channel is occupied in a random pattern. This makes it difficult to reduce the mismatch in channel quality indicator (CQI) while increasing the spatial reuse gain between the different nodes; therefore, the areal capacity gain obtained by adding nodes is much lower in existing DSS systems than in the cellular system. In this paper, we propose a different means of improving the areal capacity for downlink DSS systems. It is a group-wise DSS approach that ensures full frequency reuse in each group of base stations (BSs) by performing LBT only between the representative BSs, each of them selected by each group. Once a channel is secured by each group, all its member BSs share a channel simultaneously. This approach makes closed-loop feedback-based link adaptation practical while boosting spatial reuse gain even in the DSS environment. To implement the group-wise DSS, we propose a single unified framework that employs elementary algorithms for BS grouping and carrier-sensing threshold adjustment. Our system-level simulation results demonstrate that the proposed framework boosts the areal capacity gain by approximately 4.42 times as much as the conventional approach.

We propose a load balancing algorithm for a multi-RAT (radio access technology) network including a non-terrestrial network (NTN) and a terrestrial network (TN). Fifth generation (5G) and beyond-5G networks consider NTNs to provide connectivity and data delivery to large numbers of user equipments (UEs). However, previous load balancing algorithms do not consider the coexistence of NTNs and TNs and ignore the different resource allocation units in a multi-RAT network. Hence, we define a radio resource utilization ratio (RRUR) as a common load metric to measure the cell load of each RAT and employ an adaptive threshold to determine overloaded cells. The proposed algorithm consists of two steps to overcome the uneven load distribution across 5G cells: intra-RAT load balancing and inter-RAT load balancing. Based on the RRUR of a cell, the algorithm first performs intra-RAT load balancing by offloading the appropriate edge UEs of an overloaded cell to underutilized neighboring cells. If the RRUR of the cell is still higher than a predefined threshold, then inter-RAT load balancing is performed by offloading the delay-tolerant data flows of UEs to a satellite link. Furthermore, the algorithm estimates the impact of moving loads to the target cell load to avoid unnecessary load balancing actions. Simulation results show that the proposed algorithm not only distributes the load across terrestrial cells more evenly but also increases network throughput and the number of quality of service satisfied UEs more than previous load balancing algorithms.

Wireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually show high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods, lacking abilities of modeling the dynamic spatial-temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. To improve the accuracy of 5G/B5G cellular network traffic prediction, more cross-domain data was considered, a cross-service and regional fusion transfer learning strategy (Fusion-transfer) based on the spatial-temporal cross-domain neural network model (STC-N) was proposed. Multiple cross-domain datasets were integrated. The training accuracy of the target service domain based on the data characteristics of its source service domain according to the similarity between services and the similarity between different regions was improved, so the predictive performance of the model was enhanced. The experimental results show that the prediction accuracy of the traffic prediction model is significantly improved after the integration of multiple cross-domain datasets, the RMSE performance of SMS, Call and Internet service can be improved about 8.39%, 13.76% and 5.7% respectively. In addition, compared with the existing transfer strategy, the RMSE of the three services can be improved about 2.48%~13.19%.

The integration of unmanned aerial vehicles (UAVs) into cellular networks as aerial user equipment (aUE) has attracted increasing interest from both academia and industry in recent years. To ensure stable uplink (UL) service performance, the potential UL capacity must be determined for a variety of communication techniques to guide the system design or to obtain an optimized solution. Unlike traditional interferences from ground UEs, which contribute to only local and nearby cells, UAV systems involve strong LoS paths to even far side base stations (BSs) due to higher altitude. As a result, intercell interference becomes the dominating factor in the estimation of the potential capacity. In this article, we propose a theoretical model on the ergodic sum power of the interference arising from all UAVs maintaining LoS paths with the target BS. The solution to this model is difficult to obtain, and is essentially rooted in the dynamic trajectories of interfering UAVs within a vast geological range. To address this problem, we divide the model into noncorrelated parts, where each part contains the interfering UAVs that are independently and identically distributed (i.i.d.). Then we utilize the ergodic method to solve the interference power of each part. Based on this, the original nonanalytical expression for the ergodic sum interference power is transformed into a solvable integration problem, and the closed-form solution is successfully derived. Simulation based experiments are conducted on both rural macro (RMa) cell and urban macro (UMa) cell scenarios. The results validate the feasibility and accuracy of the proposed model, and confirm the severe influence of the intercell interference on the UL capacity.

Fifth generation (5G) is a recent wireless communication technology in mobile networks. The key parameters of 5G are enhanced coverage, ultra reliable low latency, high data rates, massive connectivity and better support to mobility. Enhanced coverage is one of the major issues in the 5G and beyond 5G networks, which will be affecting the overall system performance and end user experience. The increasing number of base stations may increase the coverage but it leads to interference between the cell edge users, which in turn impacts the coverage. Therefore, enhanced coverage is one of the future challenging issues in cellular networks. In this survey, coverage enhancement techniques are explored to improve the overall system performance, throughput, coverage capacity, spectral efficiency, outage probability, data rates, and latency. The main aim of this article is to highlight the recent developments and deployments made towards the enhanced network coverage and to discuss its future research challenges.

Dimeric translationally controlled tumor protein (dTCTP), also known as histamine-releasing factor, amplifies allergic responses and its production has been shown to increase in inflammatory diseases such as allergic asthma. Despite the critical role of dTCTP in allergic inflammation, little is known about its production pathways, associated cellular networks, and underlying molecular mechanisms. In this study, we explored the dTCTP-mediated inflammatory networks and molecular mechanisms of dTCTP associated with lipopolysaccharides (LPS)-induced severe asthma. LPS stimulation increased dTCTP production by mast cells and dTCTP secretion during degranulation, and extracellular dTCTP subsequently increased the production of pro-inflammatory molecules, including IL-8, by airway epithelial cells without affecting mast cell activation. Furthermore, dimeric TCTP-binding peptide 2 (dTBP2), a dTCTP inhibitor peptide, selectively blocked the dTCTP-mediated signaling network from mast cells to epithelial cells and decreased IL-8 production through IkB induction and nuclear p65 export in airway epithelial cells. More importantly, dTBP2 efficiently attenuated LPS-induced severe airway inflammation in vivo, resulting in decreased immune cell infiltration and IL-17 production and attenuated dTCTP secretion. These results suggest that dTCTP produced by mast cells exacerbates airway inflammation through activation of airway epithelial cells in a paracrine signaling manner, and that dTBP2 is beneficial in the treatment of severe airway inflammation by blocking the dTCTP-mediated inflammatory cellular network.

In this paper, a resource allocation (RA) scheme based on deep reinforcement learning (DRL) is designed for device-to-device (D2D) communications underlay cellular networks. The goal of RA is to determine the transmission power and spectrum channel of D2D links to maximize the sum of the average effective throughput of all cellular and D2D links in a cell accumulated over multiple time steps, where a cellular channel can be allocated to multiple D2D links. Allowing a cellular channel to be shared by multiple D2D links and considering performance over multiple time steps require a high level of system overhead and computational complexity so that optimal RA is practically infeasible in this scenario, especially when a large number of D2D links are involved. To mitigate the complexity, we propose a sub-optimal RA scheme based on a multi-agent DRL, which operates with shared information in participating devices, such as locations and allocated resources. Each agent corresponds to each D2D link and multiple agents perform learning in a staggered and cyclic manner. The proposed DRL-based RA scheme allocates resources to D2D devices promptly according to dynamically varying network set-ups, including device locations. The proposed sub-optimal RA scheme outperforms other schemes, where the performance gain becomes significant when the densities of devices in a cell are high.

The operational and technological structures of radio access networks have undergone tremendous changes in recent years. A displacement of priority from capacity–coverage optimization (to ensure data freshness) has emerged. Multiple radio access technology (multi-RAT) is a solution that addresses the exponential growth of traffic demands, providing degrees of freedom in meeting various performance goals, including energy efficiencies in IoT networks. The purpose of the present study was to investigate the possibility of leveraging multi-RAT to reduce each user’s transmission delay while preserving the requisite quality of service (QoS) and maintaining the freshness of the received information via the age of information (AoI) metric. First, we investigated the coordination between a multi-hop network and a cellular network. Each IoT device served as an information source that generated packets (transmitting them toward the base station) and a relay (for packets generated upstream). We created a queuing system that included the network and MAC layers. We propose a framework comprised of various models and tools for forecasting network performances in terms of the end-to-end delay of ongoing flows and AoI. Finally, to highlight the benefits of our framework, we performed comprehensive simulations. In discussing these numerical results, insights regarding various aspects and metrics (parameter tuning, expected QoS, and performance) are made apparent.

Summary: Accurate individual (per‐user) traffic prediction in cellular networks is considered a critical capability for the system performance improvement in terms of dynamic bandwidth allocation and network optimization. However, existing methods have limitations in capturing the characteristics of individual traffic, because the observed traffic data are usually incomplete and traffic consumption patterns significantly differ among users. In this paper, to fully exploit the inherent temporal‐spatial correlations of individual traffic data, this work models the traffic data as a three‐dimensional traffic tensor. Different from the "three‐step" strategies (i.e., "data processing + decomposition + prediction") in the existing methods, we propose a novel tensor completion (TC)‐based "two‐step" strategy, that is, "data processing & decomposition + prediction" for individual traffic prediction, which can recover and decompose the traffic data simultaneously. Furthermore, an efficient algorithm based on the alternating direction method of multipliers (ADMM) framework is proposed to solve the resulting model. To the best of our knowledge, this is the first work to apply the TC‐based "two‐step" strategy for individual traffic prediction in cellular networks. Experiments conducted on a real cellular traffic dataset empirically validate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The fifth-Generation mobile network introduces a new technology called Device-to-device (D2D) Communication which allows User Equipment (UE) to communicate directly with each other, without the need for Base Station or Evolved Node–B (eNB). Thus the mobile data traffic over the eNB can be reduced and throughput can be increased. The objective of this paper is to provide relaying service in cellular network, which has the capability of expanding the network coverage and reducing the traffic over cellular network by using D2D communication. It is achieved by using a technology called Proximity Service (ProSe), in which ProSe Direct Communication and ProSe Direct Discovery enable to identify the UE which are out of network coverage. Then the relay node is selected for serving the UE which are out of network coverage on the basis of Channel Quality Indicator (CQI) and the signal strength between the UE that are in coverage and the UE that are out of network coverage. The UE whose CQI value is high are selected based on certain criteria and are allowed to act as relays. The experiments show that the proposed technique with 5 UERelay maintains the D2D communication for longer period than the traditional techniques. Thus, the UE that are out of network coverage are served for more duration by extending the network coverage of the eNB and by relaying data using D2D offloading. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)