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Sparse mobile crowdsensing (MCS) requires participants to collect data from partial cells and then intelligently infer the data of the rest cells. Since collecting data from different cells will probably result in different data inference quality, cell selection (i.e., which cells need to be selected to collect data) is a critical issue in Sparse MCS. Currently, state-of-the-art cell selection algorithms are implemented based on reinforcement learning. These algorithms ignore the problem that the urban environment is usually time dependent, and the cell selection model needs to be kept up-to-date to adapt to the time-dependent environment. However, Sparse MCS applications require participants to collect data only in a few cells, which makes it hard to obtain suitable training data for continuous cell selection model learning. To solve this problem, we model the spatiotemporal correlations in the collected sparse data, and then design various methods to update training data based on it. Particularly, these methods make full use of the gradual changes of data in time and space, and reasonably transform and splice sparse data at different moments. Finally, updated training data is fed to the cell selection model to keep it up-to-date. We conduct experimental evaluations by performing several sensing tasks in air quality monitoring. The results show that our proposed methods can effectively update training data as well as the cell selection model. Compared with several baselines, our best method can reduce inference error by more than 10% on average.

Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased.

The user association process in cellular networks consists in choosing the base station with which the user equipment will negotiate radio resources. The current association is based on measuring the signal strength received by the user equipment from each of the base stations. The association must now deal with the diversification of user application needs and the growing heterogeneity of cellular networks. In this thesis, we show experimentally that the current association process has reached its limits, that it is agnostic of the configurations of the base stations and that it does not allow to control the throughput that the user equipment will obtain following the association. We set up for the realization of our measurements an experimental platform based on the software suite of cellular networks srsRAN and software radios USRP NI-2091. We propose in this thesis a new metric to be used during the association process. This metric, broadcasted by the base station, is a load information which in our case will be represented by the number of user equipment connected to the base station. We also discuss other metrics that can be used as load information. We show, once again experimentally, by modifying the source code of the srsRAN software suite ourselves, that if the user equipment takes this load information into account in the association process, the association decision is improved in 22% of cases.; Le processus d’association des utilisateurs dans les réseaux cellulaires consiste à choisir la station de base avec laquelle l’équipement utilisateur va négocier des ressources radio. L’association actuelle se base sur la mesure de la force du signal reçu par l’équipement utilisateur de la part de chacune des stations de bases. L’association doit aujourd’hui faire face à la diversification des besoins des applications des utilisateurs et à l’hétérogénéité grandissante des réseaux cellulaires. Dans cette thèse, nous montrons expérimentalement que le processus d’association actuel a atteint ses limites, qu’il est agnostique des configurations des stations et qu’il ne permet pas de maîtriser le débit qu’obtiendra l’équipement utilisateur suite à l’association. Nous mettons en place pour la réalisation de nos mesures une plateforme expérimentale basée sur la suite logicielle d’émulation de réseaux cellulaires srsRAN et de radios logicielles USRP NI-2091. Nous proposons dans cette thèse une nouvelle métrique à utiliser lors du processus d’association. Cette métrique, diffusée par la station de base, est une information de charge qui sera dans notre cas représentée par le nombre d’équipements utilisateurs connectés à la station de base. Nous discutons également d’autres métriques utilisables comme information de charge. Nous montrons, une nouvelle fois de manière expérimentale, en modifiant nous-même le code source de la suite logicielle srsRAN, que si l'équipement utilisateur tient compte de cette information de charge dans le processus d'association, la décision d'association est améliorée dans 22% des cas.

The cell selection mechanism that provides services to each mobile station is referred to as cell selection. For maximizing the current and future of cellular networks, it is necessary to optimize the process. The OFDMA-based systems are LTE-Advanced and IEEE 802.16m for satisfying the mobile station minimum demand by having one or more base station simultaneously. The problem is considered as the optimization problem and defines the problem as it is not considered as NP-hard and also not possible for approximation with a reasonable factor. The assumption is that the maximum required bandwidth of a single mobile station is defined as the r-fraction of the capacity of the base station. For cell selection, this paper consists of two algorithms. The first algorithm defines the creation of a (1-r) approximate solution, this is applicable when the coverage of the mobile station is done by one or more base station simultaneously. The second algorithm defines the creation of an approximate solution (1-r /2-r) when the coverage of each mobile station is covered by a maximum one base station. A simulation research that describes the benefit of using our algorithms that have limited and high-loaded power for the future of 4G networks completes the overall study. The performance and capacity are better than the existing algorithms.

In recent years, telecommunication is progressed due to the development of Long-Term Evolution (LTE) standards. This LTE network provides high-speed wireless communication for mobile devices and satisfies the requirements of customers from multi-cells. However, due to the congestion of mobile devices, each cell in the network may get overloaded. So, load balancing is the main challenge to the LTE network for reducing congestion or load in the cell. For load balancing, the optimal cell selection method is presented in this paper. Initially, the load factor of each cell is estimated. Then the load factor is compared with the predefined threshold load value. After the comparison, the heavy loaded cell handover the users to the optimal cell or cell with minimum load. This optimal cell is selected with the Opposition Based Artificial Flora (OAF) algorithm. Simulation results show that the proposed approach decreases 6% of the call blocking ratio (CBR) and 14% of Call Dropping Ratio (CDR) than the previous approaches.

Fifth-generation (5G) cellular networks are a promising technology to meet the rapid growth in wireless traffic. Small cells are critical in fulfilling the requirements of 5G networks. A heterogeneous ultra-dense network (HUDN) is an enabling technology consisting of several types of small cells to enhance the performance of 5G networks effectively. A critical issue of HUDN is the cell selection method because the traditional technique for cell selection is inapplicable in such a network. This study proposes a novel adaptive cell selection (ADA-CS) scheme. It adapts to various characteristics of HUDNs and vehicle movements. It performs six phases to select the best base station with which to be associated. Simulation results show that, with low- and medium-speed vehicles, the ADA-CS scheme outperforms the traditional protocol in terms of the average number of handovers by 42.39%. In addition, it is superior to some relevant recent schemes by up to 36.53%. The adaptation feature of the proposed protocol provides additional improvements regarding the average number of handovers with high-speed vehicles. Therefore, it achieves superiority in terms of the average number of handover failures and unnecessary handovers. In addition, the ADA-CS scheme enhances the average achievable downlink data rates and spectral efficiency per vehicle by 3.98% and 2.79% compared with the traditional and the relevant recent schemes.

The fifth generation of wireless networks is expected to provide high capacity, low latency, high reliability, and massive connectivity services. Ultra-dense network (UDN) is a clear trend for enhancing capacity, coverage, and load balancing. In UDNs, the cell selection issue for moving vehicles should be addressed. In this paper, a cell selection strategy known as a dwell time estimation (DTE) scheme is proposed, which is based on Vehicle-to-Everything (V2X) communications. It selects small cells that have the longest dwell time in a vehicle’s direction to decrease handover (HO) rate. The proposed DTE scheme is evaluated using two datasets, which are 5G small cells and vehicles datasets, that were collected in the city of Los Angeles in California. The simulation result shows that our DTE algorithm outperforms other recent related schemes in terms of the mean number of HOs by up to 36.26% because it prolongs the dwell time of the vehicle within the serving small cell. Consequently, it reduces the average number of HO failures and unnecessary HOs. In addition, it gives improvements in terms of the average achievable downlink throughput and network energy efficiency. Furthermore, it has superiority over the other schemes in terms of the mean packet delay by up to 16.49%.

The growth of the information, communication and telecommunication (ICT) industry is on the ascendancy. The proliferation of mobile devices and other web-based applications has compelled ICT indust...

The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles.

For the design of automotive battery systems, cell selection is a mandatory first step. Since lithium-ion cells bear the disadvantage of a progressive degradation process during utilization, aging characterization enables the quantification of the related phenomena. In order to achieve low development costs and due to the long-term character of cell aging, aging characterization must be conducted in an accelerated manner. This work proposes a new method for this purpose of application that is able to reproduce realistic automotive cell aging in a short time and simultaneously to guarantee consistency in the involved aging mechanisms. This method therefore combines state of the art test approaches with viable and previously unused test and evaluation concepts. As concept paper, this publication focuses on the development procedure for this new method. Summaries of results regarding identified acceleration potentials for aging characterization are also presented.

Background Indigo is a color molecule with a long history of being used as a textile dye. The conventional production methods are facing increasing economy, sustainability and environmental challenges. Therefore, developing a green synthesis method converting renewable feedstocks to indigo using engineered microbes is of great research and application interest. However, the efficiency of the indigo microbial biosynthesis is still low and needs to be improved by proper metabolic engineering strategies. Results In the present study, we adopted several metabolic engineering strategies to establish an efficient microbial biosynthesis system for converting renewable carbon substrates to indigo. First, a microbial co-culture was developed using two individually engineered E. coli strains to accommodate the indigo biosynthesis pathway, and the balancing of the overall pathway was achieved by manipulating the ratio of co-culture strains harboring different pathway modules. Through carbon source optimization and application of biosensor-assisted cell selection circuit, the indigo production was improved significantly. In addition, the global transcription machinery engineering (gTME) approach was utilized to establish a high-performance co-culture variant to further enhance the indigo production. Through the step-wise modification of the established system, the indigo bioproduction reached 104.3 mg/L, which was 11.4-fold higher than the parental indigo producing strain. Conclusion This work combines modular co-culture engineering, biosensing, and gTME for addressing the challenges of the indigo biosynthesis, which has not been explored before. The findings of this study confirm the effectiveness of the developed approach and offer a new perspective for efficient indigo bioproduction. More broadly, this innovative approach has the potential for wider application in future studies of other valuable biochemicals’ biosynthesis.

Location-allocation problem (LAP) has attracted much attention in facility location field. The LAP in continuous plane is well-known as Weber problem. This paper assessed this problem by considering capacity constraints and fixed costs as each facility has different setup cost and capacity limit to serve customers. Previous studies considered profitable areas by dividing continuous space into a discrete number of equal cells to identify optimal locations from a smaller set of promising locations. Unfortunately, it may lead to avoid choosing good locations because unprofitable areas are still considered while locating the facilities. Hence, this allows a significant increment in the transportation costs. Thus, this paper intelligently selected profitable area through a hybridization of enhanced Cell Selection-based Heuristic (CSBH) and Furthest Distance Rule (FDR) to minimize total transportation and fixed costs. The CSBH divides customer distribution into smaller set of promising locations and intelligently selected profitable area to increase possibility of finding better locations, while FDR aims to forbid the new locations of the facilities to be close to the previously selected locations. Numerical experiments tested on well-known benchmark datasets showed that the results of hybrid heuristic outperformed single CSBH and FDR, while producing competitive results when compared with previously published results, apart from significantly improving total transportation cost. The new hybrid heuristic is simple yet effective in solving LAP.

We present a Machine Learning (ML) use-case for reducing latency in wireless networks, by replacing some of the traditional and time-consuming procedures. The proposed method reduces the time needed for collecting a large number of measurements for the target cell selection during the inter-frequency handover procedure. Our paper contributes to the current literature, firstly, by showcasing how ML can replace traditional procedures in order to reduce the latency. Secondly, by presenting results based on the real measurements from a live network. Finally, by training a huge number of models over a large geographical area, where multiple base stations are deployed, reporting the performance over all those models. This contribution extends previous work in the area, that was based on simulations and improves confidence in the applicability of the proposed solution in real networks. The proposed method not only reduces the total handover delay and service disruption but can also be considered as an enabler for new verticals and use-cases, as we discuss in this paper.

Three-dimensional (3D) bioprinting has demonstrated great potential for the fabrication of biomimetic human tissues and complex graft materials. This technology utilizes bioinks composed of cellular elements placed within a biomaterial. Mesenchymal stromal cells (MSCs) are an attractive option for cell selection in 3D bioprinting. MSCs can be isolated from a variety of tissues, can pose vast proliferative capacity and can differentiate to multiple committed cell types. Despite their promising properties, the use of MSCs has been associated with several drawbacks. These concerns are related to the ex vivo manipulation throughout the process of 3D bioprinting. The herein manuscript aims to present the current evidence surrounding these events and propose ways to minimize the risks to the patients following widespread expansion of 3D bioprinting in the medical field.

In this paper, we propose iCellSpeed, an on-device solution to increase data access speed by substantiating unrealized performance potentials. We find that performance potentials are missed in today's mobile networks, as the data speed a user device gets is much lower than what the device could get. The issue is rooted in the current cell selection practice, which misses good candidate cells that offer faster access speed, thus under-utilizing the available capabilities in mobile networks. We design iCellSpeed to facilitate network-controlled cell selection with proactive device-side assistance towards more desirable cells. Our evaluation over AT&T and Verizon confirms its effectiveness. iCellSpeed increases data access speed by more than 10 Mbps at 79% of test locations (> 25Mbps at 29% of locations, up to 80.6 Mbps). It doubles access speed at 62.5% of locations with the gain up to 28.4x. Datasets are available at [9].

Standardization efforts for 5G New Radio (NR) technology are completed and initial deployments based on both Non Standalone (NSA) mode and Standalone (SA) mode have gained momentum in several countries. The 5G rollouts i.e. NSA and SA mode lead to an interim situation where mobile operators need to maintain both Evolved Packet Core (EPC) and 5G Core (5GC). Mobile operators, in future may prefer to migrate to 5GC, albeit maintaining the LTE and NR Radio Access Technology (RAT) to preserve investments. UE inter-RAT (IRAT) mobility between LTE and NR RAT mobility within 5GC, or between EPC and 5GC will be a crucial Key Performance Indicator (KPI) for mobile operators. IRAT mobility for CONNECTED state is already supported in 3GPP specifications but very less attention is paid to mobility when the UE is in INACTIVE state or when the UE performs recovery from Radio Link Failure (RLF). INACTIVE state is supported in both LTE and NR RAT when connected to 5GC, whereas suspend mode in IDLE state is supported when LTE RAT connects to EPC. In this paper we propose UE context retrieval procedure enhancements to support: a) efficient UE mobility between LTE and NR when the UE is in INACTIVE state or suspend mode in IDLE state in LTE and b) enhancements for RLF recovery by allowing reestablishment procedure to be performed if cell selection results in suitable IRAT cell detection. Further, we studied the control plane latency performance and signaling overhead reduction during the IRAT mobility scenarios by applying proposed enhancements. We believe that the UE context retrieval enhancements during IRAT mobility in above mentioned scenarios will improve robustness and minimize service interruption thereby improving the mobility experience.

Circulating tumor cell (CTC) detection is used to identify malignant tumor cells that are disseminated in the patient's circulating blood. It has been widely used in early tumor classification, prognosis judgment and efficacy evaluation. It also plays an important role in finding new tumor markers and developing new anti-tumor drugs. The existing methods to detect CTCs can only be combined with cells enrichment and fluorescence imaging, which is called separation and identification of CTCs. Cell enrichment leads to cell omission, and small fluorescence imaging field leads to long detection time, which affects clinical application. In this paper, 7.5ml whole-blood large-size slices were used to avoid cell selection. The two-step imaging method was adopted. The first step is the large-size slices fast imaging based on the large field of view (LFOV) and high resolution optical system, which is used to initially locate target CTCs. The second step is target CTCs accurately identifying by fluorescence imaging. Through this method, the detection time is greatly reduced, the detection efficiency is greatly improved, accuracy is higher because of the avoidance of cells misdetection. A new solution for clinical detection of CTC cells is provided, which is conducive to promoting the CTCs count as a routine blood test item.

The vision of advanced long-term evolution (LTE-A) project is set to ultimate increase of network capacity in heterogeneous networks (HetNets). In HetNets with small cell configuration, a considerable majority of user devices is eventually connected to the macrocell base station (MBS), while small base stations (BSs), such as femtocell access points (FAPs), are still without any user. This results in unbalanced load and reduces the data rate of macrocell user equipment (MUE). In this paper, a method is proposed for load balancing among FAPs, while desired throughput is achieved. The proposed method uses the estimated received signal strength from different BSs and adjusted pilot signals. Under the critical signal to interference plus noise ratio (SINR) condition, a list of candidate FAPs is prepared. The updated candidate list henceforth does not include the least visited FAPs, which in turn leads to lower unnecessary handoffs. Once the BS with the highest number of free RBs and the highest pilot signal power is selected, FAP allocates the RBs with higher SINRs (qualified RBs) to user. In the case of FAP unavailability, the algorithm compels users to connect to the MBS with adequate qualified RBs. The performance of the proposed method was evaluated under a variety of FAPs density, and the number and velocity of users in terms of throughput and Jain’s fairness index. The results evidence affordable improvements in the throughput and Jain’s index in comparison with other methods.

Magnetic bead-based analyte capture has emerged as a ubiquitous method in cell isolation, enabling the highly specific capture of target populations through simple magnetic manipulation. To date, no “one-size fits all” magnetic bead has been widely adopted leading to an overwhelming number of commercial beads. Ultimately, the ideal bead is one that not only facilitates cell isolation but also proves compatible with the widest range of downstream applications and analytic endpoints. Despite the diverse offering of sizes, coatings, and conjugation chemistries, few studies exist to benchmark the performance characteristics of different commercially available beads; importantly, these bead characteristics ultimately determine the ability of a bead to integrate into the user’s assay. In this report, we evaluate bead-based cell isolation considerations, approaches, and results across a subset of commercially available magnetic beads (Dynabeads FlowComps, Dynabeads CELLection, GE Healthcare Sera-Mag SpeedBeads streptavidin-blocked magnetic particles, Dynabeads M-270s, Dynabeads M-280s) to compare and contrast both capture-specific traits (i.e., purity, capture efficacy, and contaminant isolations) and endpoint compatibility (i.e., protein localization, fluorescence imaging, and nucleic acid extraction). We identify specific advantages and contexts of use in which distinct bead products may facilitate experimental goals and integrate into downstream applications.

A LTE-advanced heterogeneous network consists of many macrocells, and within each macrocell, there can have many picocells and user equipments (UEs). A UE within a macrocell can request resource blocks (RBs) from either the macrocell or a picocell within the macrocell. In this paper, two optimal problems are defined for the association of UEs, RBs, and heterogeneous cells. We show that both problems are equivalent to a 0/1 knapsack problem and, therefore, can be solved in pseudo-polynomial time by dynamic programming. Based on above two optimal problems, we, then, propose a cell selection and resource allocation (CSRA) scheme to solve the problem of allocating a UE to its macrocell or to a picocell within the macrocell. The design goal of picocells is to unload the traffic for the macrocell. When there are available RBs and UEs within the picocell, the CSRA scheme offloads UEs to the picocell to release the most number of RBs for the macrocell. Only when there are not enough RBs within a picocell and there are available RBs within the macrocell, for load balancing, the CSRA scheme moves some UEs to the macrocell. The CSRA scheme stops whenever the picocell is not overloaded or the macrocell does not have available RBs. Simulation results show that our proposed scheme performs better than the cell range expansion scheme in terms of goodput, loss rate, and delay.

The densification of small cells in heterogeneous networks (HetNets) causes huge energy consumption and severe network interference. To fully exploit the potential of new network architecture, the cell selection (CS) in such HetNets should couple with reducing power consumption and network interference. To this end, we jointly perform cell activation and selection (CAS) to maximize the network energy efficiency (EE) under users’ long-term rate constraints. The formulated problem is in a mixed-integer fractional form and hard to tackle. We need to transform it into a parametric subtractive form, by which we reach its solution through a three-layer iterative algorithm. The first layer searches an EE parameter using a bisection method; the second layer alternately optimizes CAS indices; the third layer solves CS and cell activation (CA) problems using dual decomposition and fixed point iteration, respectively. At last, we give some complexity and convergence analyses for the designed algorithm, and investigate the impacts of different network parameters on system performance. The simulation results show that the CA introduced in CS is a good option to reduce energy consumption and network interference.

A critical issue in many wireless networks is how to establish the best possible quality connections between users to base stations. This is in particular challenging when users are randomly located over a geographical region, and each covered by a number of heterogeneous base stations. To this end, we design a smart and efficient cell selection mechanism to improve the user-base station connection in heterogeneous wireless networks. We formulate the cell selection problem as an asymmetric congestion game with consideration of users’ heterogeneity in their locations and their data rates to various cells. We show the existence of pure Nash equilibria (PNE) and propose a concurrent distributed learning algorithm to converge to them. In the algorithm, we allow users to perform random error-tolerant updates synchronously, and guarantee them to reach one or multiple PNE with the largest utilities. In addition, we do a systematical investigation on the implementation of the algorithm in practical networks. Simulation results show that the algorithm can achieve satisfactory performance with acceptable convergence rate.

International audience; Sparse Mobile Crowdsensing (MCS) is a novel MCS paradigm which allows us to use the mobile devices to collect sensing data from only a small subset of cells (sub-areas) in the target sensing area while intelligently inferring the data of other cells with quality guarantee. Since selecting sensed data from different cell sets will probably lead to diverse levels of inference data quality, cell selection (i.e., choosing which cells in the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i.e., data collection costs) for ensuring a certain level of data quality. To address this issue, this paper proposes the reinforcement learning-based cell selection algorithm for Sparse MCS. First, we model the key concepts in reinforcement learning including state, action, and reward, and then propose a Q-learning based cell selection algorithm. To deal with the large state space, we employ the deep Q-network to learn the Q-function that can help decide which cell is a better choice under a certain state during cell selection. Then, we modify the Q-network to a deep recurrent Q-network with LSTM to catch the temporal patterns and handle partial observability. Furthermore, we leverage the transfer learning techniques to relieve the dependency on a large amount of training data. Experiments on various real-life sensing datasets verify the effectiveness of our proposed algorithms over the state-of-the-art mechanisms in Sparse MCS by reducing up to 20% of sensed cells with the same data inference quality guarantee.

Ultra-Dense Network (UDN) is a clear trend for future 5G. However, using exciting small cell selection (SCS) schemes, which are totally based on downlink measurement, dense deployment of small cells would bring about frequent measurement, intolerable ping-pong (PP) rate and handover failure (HOF) rate. In this paper, an uplink measurement based SCS scheme is proposed for a user-centric networks based on the analysis of mobility management challenges of UDN, and the handover performance of the proposed SCS scheme is evaluated. In the proposed SCS scheme, the small cells measure uplink reference signals of users and then user-centric new radio (NR) cells would be established, which reduces the measurement process that the user feedbacks measurement reports to small cells that have transmitted downlink reference signals to the user. The relationship between key handover parameters and mobility performance in user-centric network is illustrated to look for the optimal configuration of Time-to-Trigger (TTT) and A3 Offset and achieve low PP rate and HOF rate. Besides, the impact of NR cell size on handover performance is evaluated to provide basis for the forming/reforming of user-centric NR cell. Simulation results show that compared with traditional downlink measurement based SCS scheme, the proposed SCS scheme can reduce the PP rate and HOF rate by up to 21% and 56%, respectively.

We report and discuss cell selection and handover measurements for an aerial drone connected to an LTE-A network in a suburban environment. Our experiments show how the handover frequency increases with increasing flight altitude: A drone flying at a typical height of 150 meters is expected to experience five cell changes per minute compared to only one change for ground users moving at the same speed. This behavior can be explained by the differences between ground and aerial devices in terms of cell selection. It is concluded that revised handover techniques and the consideration of drones in the planning and operation of 4G and 5G radio access networks are required.

The Femto-Macro heterogeneous network is a promising solution to improve the network capacity and coverage in mobile network. However interference may rise due to femtocell deployment nearby to macro user equipment (MUE) within macrocell network coverage. Femtocell offers main priority in resource allocation to its subscribed femto user equipment (FUE) rather than unsubscribed MUE. MUEs will suffer severe interference when they are placed near or within the femtocell area range especially at the cell edge. This phenomenon occurs due to the distance is far from its serving macro base station (MBS) to receive good signal strength. This paper presents a design of cell selection scheme for cell-edge MUE to select an optimal femto base station (FBS) as its primary serving cell in physical resource block allocation. In this study, the proposed cell selection consists of four main elements: measuring the closest FBS distance, Signal to Interference-plus- Noise-Ratio (SINR), physical resource block (PRB) availability and node density level for the selected base station. The main goal is to ensure celledge MUE has priority fairly with FUE in physical resource block allocation per user bandwidth demand to mitigate interference. Hence, the cell-edge MUE has good experienced on receiving an adequate user data rate to improve higher network throughput.

This letter aims at providing a cell selection scheme to improve the system energy efficiency of small cell networks under both user equipment and backhaul link data rate constraints. The system energy efficiency optimization problem is formulated as a mixed integer linear fractional problem, which is NP-hard. To reduce the complexity, a stable sub-optimal solution named energy efficient cell selection (EECS) is proposed. The minimum achievable maximum system energy efficiency in EECS is proved to converge into the state that has no incentive to shut down or switch on more small cells. Simulation results indicate that shutting down as many as possible small cells is not always beneficial to system energy efficiency, and EECS is an effective scheme to improve the system energy efficiency.

In LTE-A (long term evolution-advanced) networks, relaying is seen as a cost-effective solution to increase coverage and improve system capacity. Relays are deployed on the coverage of existing macrocells, so the resource sharing among different nodes and links and the inter-cell interference coordination (ICIC) are challenging issues that need to be addressed. In real scenarios, users are not uniformly distributed on cells, so the resource allocation has to adapt to changes in the traffic load distribution. This paper focuses on Type 1 half-duplex inband relaying. We propose a dynamic resource allocation scheme to adjust the frequency partitions to heterogeneous load conditions. In addition, we also propose a cell selection method which tries to connect users to the node that entails a better use of frequency resources, and at the same time, to guarantee the QoS requirements of users.

openaire: EC/H2020/722424/EU//SECRET To satisfy the high data demands in future cellular networks, an ultra-densification approach is introduced to shrink the coverage of base station (BS) and improve the frequency reuse. The gain in capacity is expected but at the expense of increased interference, frequent handovers (HOs), increased HO failure (HOF) rates, increased HO delays, increase in ping pong rate, high energy consumption, increased overheads due to frequent HO, high packet losses and bad user experience mostly in high-speed user equipment (UE) scenarios. This paper presents the general concepts of radio access mobility in cellular networks with possible challenges and current research focus. In this article, we provide an overview of HO management in long-term evolution (LTE) and 5G new radio (NR) to highlight the main differences in basic HO scenarios. A detailed literature survey on radio access mobility in LTE, heterogeneous networks (HetNets) and NR is provided. In addition, this paper suggests HO management challenges and enhancingtechniques with a discussion on the key points that need to be considered in formulating an efficient HO scheme.

As the commercialization of the 5th generation mobile networks is approaching globally, 5G NR standard is deemed as a competitive candidate who abstracts a mass of concern from the industry. However, there are still some blurry conceptions and issues regarding real network deployment needed to be further studied. 5G NR standardization claims there are non-standalone (NSA) and standalone (SA) network deployment schemes, respectively. No matter how it would be chosen, the UE cell selection mechanism is an inevitable fundamental process for end-to-end communication service, which needs to be further concerned. For the purpose of network operators’ daily optimization & management in the future stage, this paper briefly analyzed UE cell selection process for NSA and SA networking scheme, respectively.

The users in the ultra dense networks are faced with the problem of how to choose the right small cell to access. Reasonable choices can not only give the users a better quality of service, but also enhance the effectiveness of the overall system. A novel first-price sealed-bid auction based distributed cell selection algorithm is proposed in this paper, and it can be used to solve the problem existed in ultra dense networks. And it can be concluded from the results that the proposed algorithm achieves better performance in terms of user throughput and reception probability compared to the random access mechanisms.

The proliferation of access to the network through smart phones and wireless devices has become a major issue with the need of high data capacity and frequency spectrum. Long Term Evolution Advanced (LTE-A) has accommodated this need by expanding the capacity and efficiency spectrum usage over a single carrier frequency, which is called heterogeneous co-channel deployment. The heterogeneous networks combining large with small cells give the opportunity to the users to connect to the high performing cell with high data rates. There are there cell selection techniques, namely SINR, RSRP and RSRQ proposed in LTE. In this work, all these three cell selections are investigated using Matlab based on the actual parameter values. The CQI (Channel Quality Indicator) Mapping and Scheduling are proposed based on the realistic scenario. The success ratio of network accessing, throughput and interference are evaluated. Additionally, the average regional SINR, RSRP and RSRQ are analyzed. According to the simulation results, it is obvious that the cell selection based on SINR and RSRQ methods provides the better performance than RSRP for all performance evaluation metrics. For instance, the success ratios of cell selection method based on SINR and RSRQ are more than 95%, which are much higher than that of RSRP and almost identical for all cells.

In this paper, a new cell selection technique for millimeter-wave (mmWave) cellular systems considering a hybrid beamforming structure in data transmission stage is proposed. Using the proposed technique with cell and beam synchronization signals (CBSS) transmitting simultaneously from multiple beams, the processing time required for channel estimation and cell selection can be reduced significantly. It is shown by simulation that additional gains in channel capacity can be achieved as compared to conventional technique.

The amount of different access networks rises. Although the networks have been designed for different purposes, they all may serve different needs of moving devices. For the moving devices, the choice of the best available access network determines the quality of the transmission. Besides the network parameters, which should not only be considered as standardized values but also with real measurement data, the user preferences are also important for the acceptance of the choice. Depending on the communication needs, mobile users may prefer a slower but more secure connection or a faster but less reliable one. Therefore we designed algorithm considering network parameters and user preferences. This can easily be extended to further networks as soon as they are emerging.

LTE-Advanced HetNets deployment addresses issues of growing quality of service (QoS) demand, high data rates, and expanding coverage. Among the core challenges is load balancing due to user equipments (UEs) offloading for association using received signal received power (RSRP). The low-power tier is under-utilized whereas, UEs having poor channel quality consume significant amount of available resources in the network. The proposed combined channel gain and quality of service (QoS)-based access-aware (CCGQA) cell selection scheme emphasizes on the importance of combined metrics to achieve improved performance through load balancing in HetNets. The QoS requirements of the high priority UEs (HPUs) can be satisfied through association with the tier giving maximum channel gain. The association for non-QoS traffic, low priority UEs (LPUs) is by the joint metrics (channel gain, channel access probabilities of LPUs and HPUs), while the remaining resources allocated to the LPUs. The CCGQA scheme has 1.72 times UEs connected to PeNBs, 8% enhanced loadbalancing fairness compared with the RSRP and RSRP+6dB bias cell selections. The average UE throughput of LPUs has improved by 73.7% using the proposed CCGQA after satisfying the QoS requirements of HPUs.

We present a method for parallel dynamic cell selection and link scheduling in order to simultaneously activate a subset of non- interfering transmitter-receiver links to manage the interference in an ultra-dense network setting. We show how we can utilize a practical feedback method for periodic reporting of channel state information measured at the users back to the network to enable implementing our interference management scheme. Through simulation results, we demonstrate the superiority of our proposed interference management scheme over several benchmark schemes in terms of sum-rate and network coverage.

International audience; Spreadsheets on tablet, based on multitouch interaction is tedious and raises multiple interaction issues such as screen size or limited tactile interaction. To answer this limitations, we propose the use of the stacking paradigm, which consists in laying one edge of a smartphone on a tablet screen. It offers the double benefit of augmenting the input vocabulary and extending the display surface. We built a prototype based on a 3D printed shell augmented with copper to detect the smartphone on the tablet screen. We also developed a simplified spreadsheet app on the tablet in which users can select cells using three stacking-based interaction techniques.

Downlink (DL) data rate for a MS is influenced by not only the signal to interference and noise ratio (SINR) but also the amount of radio resources allocated to the MS. Therefore, when a MS uses SINR to select a cell to associate with, it cannot receive the fastest DL data rate all the time if it associates with a congested cell. Moreover, the SINR-based cell selection may result in cell loads unbalance, which decreases the efficiency of a network. To address the issue, we propose a novel cell selection method by considering not only SINR but also a cell load which are combined into two cell selection criteria. One is the maximum achievable data rate and the other is the minimum outage probability. The simulation results show that the cell selection based on the maximum achievable data rate is superior to the SINR-based method and the method using the minimum outage probability in terms of the system efficiency and the fairness in cell loads while the cell selection method based on the minimum outage probability is superior to the others in terms of the outage probability of a MS.

A handover (HO) is an important procedure used to maintain connectivity in mobile networks. Nevertheless, it is not sufficient when a user equipment associates with a certain base station for a very short period. In this letter, we propose an estimated time-of-stay-based cell selection (ETCS) scheme to minimize the number of unnecessary HOs that adversely affect the performance of heterogeneous mobile networks. We analyzed the performance of the proposed ETCS scheme and compared it with that of the conventional cell selection scheme. The results show that the ETCS scheme significantly reduces the number of unnecessary HOs. Therefore, we can expect reduced network overhead for system information acquisitions, HO preparations, and mobility managements using the proposed scheme.

Recently, the deployment of small-cell with overlay coverage has emerged as a reliable solution for 5G heterogeneous network (HetNets). While they provide useful properties, these architectures bring several challenges in network management, including interference alignment, extensive back-hauling, and cell selection within HetNets. In this work, we model the cell selection paradigm in 5G HetNets using a non-cooperative game-theoretic framework, and we show that it admits an equilibrium using mixed strategy Nash Equilibrium (NE) method.

As a major concern of mobile network operator, unnecessary functional Base Stations (BSs) in low traffic time period will increase the operating expense (OPEX) and cause the profit reduction. The low traffic Femto Cells (FCs) can be switched off to improve Energy Efficiency (EE), the switched off FCs user can be served by neighbour BSs. This paper propose a algorithm for cell selection, which is use to switch off FCs when they are under a load threshold. The cell selection problem will turn into a problem that which kind of FC combination can obtain the highest EE. Thousands of FCs are deployed in the network, and total number of possible FC combination will be difficult to calculate. Simulated Annealing (SA) is applied in this paper to estimated the EE when different FC combination is selected. In each iteration, SA will compare each solution?s EE and gradually approach the best solution. The switched off FCs will cause weak received signal strength. Therefore, Macro Cells (MCs) need to reduce transmission power to cause less interference to FCs, but users requirement should be guaranteed. Simulation result will shows EE with different FCs combination and significant EE improvement when MCs transmission power is reduced.

The growing concern on the exposure of users to the electromagnetic field (EMF) has recently brought new challenges to the mobile research community. In this letter, we propose a novel cell association framework for heterogeneous cellular networks (HetNets), which aims to balance the load amongst heterogeneous cells so as to improve the resource usage and to increase the user satisfaction in terms of both data rate and EMF exposure. We model the cell selection problem as a General Assignment Problem (GAP) and we present two heuristic algorithms, which solve it with limited complexity. Our analysis shows that the proposed solutions lead to notable improvements with respect to legacy association schemes. This papers reports work undertaken in the context of the project LEXNET. LEXNET is a project supported by the European Commission in the 7th Framework Programme (Grant Agreement n. 318273).

Femtocell technology is a promising solution for offloading high volume cellular data traffic to low-powered indoor base stations. It allows users to perform a handoff (HO) to a femto base station (FBS) and get high data rate. Despite the importance of selecting a proper target femtocell in open-access femtocell networks, effects of attacks on the target cell selection have not been paid sufficient attention. Unlike traditional cellular networks, attackers can get root access to FBSs and perform a number of attacks utilizing these FBSs. Unfortunately, existing target cell selection and HO decision algorithms are not sufficient to detect or avoid these attacks. Moreover, existing solutions to avoid these attacks in femtocell networks cannot be directly used in the HO scenario. In this paper, we first discuss possible attacks and their effects on femtocell networks during an HO process. In addition, we propose a target cell selection and HO-decision scheme which helps a user to select a trustworthy target femtocell during an HO. As the attacks are unalike in nature, it is hard to come up with a single solution which can avoid all these attacks we consider. We use both received signal strength indicator and location database of FBSs to select a target cell in a way that the probability of HOs to a malicious femtocell is reduced without increasing the HO signaling cost significantly. Additionally, simulation results exhibit significant improvement of the femtocell utilization. To the best of our knowledge, this is the first work that considers attacks on FBSs during HOs in open-access femtocell networks.

Ultra-Dense Networks (UDNs) is a clear trend for future 5G while dense deployment of small cells (SCs) would result in unnecessary handovers (UHOs) due to a shorter sojourn time in the target SC and thus user experience is degraded. The main reason for this problem is that existing small cell selection (SCS) schemes ignore the importance of sojourn time. Therefore, in this paper, a SCS scheme based on sojourn time estimation is proposed. The Cumulative Distribution Function of the sojourn time depending on the user velocity and SC intensity is presented firstly. Furthermore, the instantaneous sojourn time is calculated by estimating the chord length of user trajectory in the coverage of candidate SC, in which user direction is obtained by applying a direction prediction scheme meanwhile the coverage of SC is determined by Poisson-Voronoi Tessellations (PVTs). In addition, the optimal sojourn time threshold is determined for the purpose of lower handover failures and those candidate SCs whose sojourn time are shorter than the predefined threshold would be skipped. Simulation results show that compared with a typical parameters setting (TTT=320ms, Offset=3dB) in LtE, the proposed scheme can reduce the Ping Pong (PP) rate and UHO rate by up to 50% and 60%, respectively. Moreover, the handover failures can be reduced by up to 30%.

Recent development of wireless communications has resulted to the augmentation of small cell to traditional macrocell networks. This constitutes an improved coverage and system capacity of the networks. However, it consequently results to interference and high blockage rate, which are the problems we are treating in this paper. This work proposes two cell selection schemes that adopt the enhanced inter-cell interference coordination technique developed by 3GPP in Rel-10 for LTE- Advanced networks. Initial results are provided as a proof-of-concept,where the two proposed algorithm's results are provided.

In a Distributed Virtual Environment (DVE) system, a multi-server infrastructure is often used to improve scalability and responsiveness. With this infrastructure, every client needs to be mapped to a proper server, and the mapping result is crucial because it affects the overall system performance and the users' interactive experiences. Existing mapping methods primarily consider the load balancing problem and the communication delay constraint between clients and servers but ignore the influence of client migrations at run-time. In this study, we propose a novel dynamic mapping algorithm which transfers cells between servers according to a cell selection scheme based on comprehensive factor computations. Experimental results show that our algorithm can decrease the probability of migrations and improve the users' degrees of satisfaction effectively in DVE systems.