Cell Selection Scheme Research Articles

Assessment of the adaptive ability of regenerant and original oat genotypes to soil stressors

The aim of the research is a comparative analysis of the response to soil stressors according to the biochemical and productive characteristics of the initial genotypes of oats (line 2h15) and regenerative forms (RA, RAAl, RAMn, RACd) in the framework of evaluating the effectiveness of applied cell selection schemes. In the vegetation experiment, the effect of soil stressors on plants was studied – increased acidity (pH = 4.3), toxicity of manganese ions (65.2 mg/kg, pH = 5.2) and cadmium (2.87 mg/kg, pH = 5.2). The Arkhan variety was used as a standard. The control was soil with a neutral pH (7.2). Regenerants were previously obtained in vitro on artificial media without stress (RA) and with selective agents: alumina acid (RAAl), increased Mn2+ (RAMn) and Cd2+ (RACd). On soil with stressors, the pigment content in regenerant leaves exceeded the baseline values by 1.4…1.6 times. In control, the differences between RA and the baseline are unreliable. All regenerative lines, regardless of growing conditions, were characterized by significantly lower levels of polyphenols in grain (11.2…12.4 mg/g dry weight), compared with the baseline and the standard by 1.2…1.3 times. On backgrounds with artificial addition of metals, an excess of the manganese level in the grain was noted: 1.7 times in RAMn (256.1 mg/kg), compared with the baseline, and there were no differences between them in the cadmium content in RACd (1.82…1.67 mg/kg). On the control background, RA and the baseline significantly lagged behind the standard in terms of grain weight from the plant: in regenerants – 1.29 g; baseline – 1.38 g; standard – 1.65 g. With increased acidity and cadmium, all genotypes decreased productivity relative to the control, but to a lesser extent regenerants (1.6 and 1.4 times, respectively), the baseline – 2.8 and 2 times, the standard – 2.4 and 1.9 times.

Techno-Economic Suitability of Batteries for Different Mobile Applications—A Cell Selection Methodology Based on Cost Parity Pricing

Rapid advancements in lithium-ion battery (LIB) technology have paved the way for the electrification of diverse applications, with continuous improvements in performance, substantial cost reductions, and the emergence of new manufacturers, formats, and cell chemistries. However, this diversity poses challenges in identifying the most suitable battery cells for specific applications. Here, we present a high-level techno-economic framework for cell selection, leveraging an extensive database of over 500 real-world cells, techno-economic analyses of emerging applications, and a Python-based modeling approach. We apply this method to three electrifiable mobile applications with distinct characteristics: battery electric cars, industrial forklifts, and regional passenger trains. Our results emphasize substantial variations in technical requirements, from power capability to energy density or longevity. We observe no particular differentiation according to cell formats, but tendencies for most suitable chemistries per application. No cell is suitable for all applications, particularly regarding the required maximum cell costs to ensure profitability, ranging from a few to several hundred Euros per kWh to achieve cost parity with a state-of-the-art reference technology. These findings highlight the importance of tailored cell selection strategies for decision makers to optimize performance and cost-effectiveness across different applications.

Increasing resistance of lawn grasses to zinc and copper pollution.

Coppe and zinc are priority pollutants in city soils. Copper and zinc are the limiting factors for lawn grasses. Cell selection can increase the resistance of lawn grasses to zinc and copper. The effect of zinc on the morphogenic ability of the callus was determined. The results of this study showed that zinc is less toxic to calli than copper. The method of obtaining lawn grass resistant to zinc has been developed. The results were used to develop the cell selection technology for obtaining plants resistant to the complex effect of zinc and copper. Concentrations of Copper (75mg/l) and zinc (150mg/L) were selected as selective. The author developed the cell selection scheme for obtaining plants resistant to the complex effect of Cu and Zn. The regenerants showed increased resistance to copper and zinc.

Traffic rate agnostic end-to-end delay optimization using receiver-based adaptive link scheduling in 6TiSCH networks

Link scheduling is a key component in 6TiSCH networks. It mainly specifies a node is allowed to transmit packets through which link at what time and in which channel. There exist many link scheduling schemes in the literature for 6TiSCH networks. Among the distributed schemes, the Self-healing distributed scheduling for end-to-end delay optimization (Stratum) was specifically devised to minimize end-to-end latency. However, it performs worse in case of higher traffic rates. On the other hand, the de-facto link scheduling standard Minimal Scheduling Function (MSF) allocates cells (i.e. timeslot and channel) randomly in a slotframe for scheduling transmissions, and thus its end-to-end delay performance is not good under all traffic rates. Therefore, this paper proposes a traffic rate agnostic distributed link scheduling function (RTDS) for 6TiSCH networks. The RTDS follows a receiver-based cell selection and allocation strategy to reduce the waiting time of a packet in the buffer of receiving node before forwarding the packet towards final destination. Unlike MSF, the RTDS uses lesser number of control packet exchanges to adapt the schedule of a link based on the present network condition and traffic rate. The proposed RTDS scheme minimizes end-to-end latency under any traffic rate. RTDS is implemented in the 6TiSCH simulator. Simulation results show that RTDS can significantly improve the end-to-end latency compared to the benchmark schemes. For example, in an 80-node network, the RTDS reduces end-to-end latency by 58% and 43% in low and high traffic rates, respectively, compared to that in MSF. Further, it outperforms Stratum by 77% and 75% under the low and high traffic rates, respectively.

HIGH ANTIOXIDATION-CAPACITY STEM CELLS ENHANCE CARTILAGE REGENERATION

Stem cells are known to have low levels of intracellular reactive oxygen species (ROS) and high levels of glutathione. ROS are thought to interact with several pathways that affect the transcription machinery required for stem cell differentiation, and are critical for maintaining stem cell function. In this study, we are developing a new fluorescent probe that rapidly and reversibly reacts with glutathione (GSH), the most abundant non-protein thiol in living cells that acts as an antioxidant and redox regulator.Multipotent perivascular progenitor cells derived from human ESCs (hESC-PVPCs): Differentiated ESCs as embryoid bodies in the presence of BMP4 to induce mesoderm differentiation followed by a simple cell selection strategy using attachment of single cells onto collagen-coated dishes. Differential gene expression profiling was performed among H9 hESCs, EBs induced by BMP4 and naturally selected CD140B+CD44+ population at Day 7 (PVPCs). Colony-forming assay: GSHhigh and GSHlow PVPCs were plated on 10-cm tissue culture-treated polystyrene dishes in triplicate in growth medium and cultured for 14 days. Transwell migration assay: GSHhigh and GSHlow PVPCs at passage 4 were resuspended at 1 × 106/mL in the migration medium and seeded in the upper chamber. The following human recombinant SDF-1 and PDGF-AA proteins were used as chemoattractants in the lower compartment.Probe-GSH conjugate shows shifts in fluorescence excitation and emission spectra that enables ratiometric measurement of GSH levels. Using these properties, stem cells can be purified by FACS-based technology according to intracellular GSH level. We are developing a protocol both for comparing GSH level in stem cell from different culture conditions and for preparing stem cells with high-GSH level . Our results reveal that GSHhigh PVPC purified by FACS show increased colony forming ability compared with that GSHlow PVPC, indicating that intracellular GSH contributes to the maintenance of stemness. Moreover, transplantation of GSHlow PVPC is more effective than that of GSHlow PVPC for cartilage regeneration in osteochondral defect.This technique enable FACS-based sorting of stem cells according to intracellular GSH levels and thus investigation of functional role of GSH (high antioxidant capacity) in the stem cell maintenance and chondrogenic differentiation.

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Heterogeneous ultra-dense networks (HUDNs) are one of the key enabling technologies for the fifth-generation (5G) networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection is a challenging issue in HUDNs, due to the different characteristics of base stations (BSs) and the existence of a large number of them. Thus, the traditional cell selection scheme is not applicable in such a network. In this paper, a novel adaptive cell selection strategy is proposed, called adaptive two-tier based on adaptive boosting (A2T-Boost). It can adapt to the various characteristics of base stations, as well as the different movement features of mobile stations such as vehicles and pedestrian. It is a software-defined networking (SDN)/machine learning (ML)-based scheme. A real-world case is considered in the downtown of Los Angeles city. Simulation results demonstrate that A2T-Boost achieves high prediction performance and it outperforms other related schemes in terms of average number of handovers (HOs) by up to 50%. Moreover, it enhances the average achievable downlink sum-rates and network energy efficiency achieved by vehicles by up to 33.76%. Furthermore, the average packet delay is decreased using the proposed scheme by up to 12.87%.

Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach.

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.

Ornamental plants adapted to urban ecosystem pollution: lawn grasses tolerating deicing reagents.

Deicing reagents are priority soil pollutants in urban ecosystems. Sodium chloride is one of the priority deicing reagents. Sodium chloride is limiting the spread of lawn grass. We first showed the possibility of using environmental biotechnology in urban greening to obtain lawn grasses tolerant of sodium chloride. We have developed a cell selection technology to obtain salt-tolerant lawn grasses. A cell selection scheme with 1% sodium chloride was used. Most of the tested regenerants were more tolerant to NaCl than original plants. The descendants of the studied regenerants demonstrated the preservation of salt resistance. Most of the descendants of the regenerants Agrostis stolonifera retained high decorative qualities under salinity conditions. The tolerance remained in the next five generations. The descendants of the most salt-tolerant clones Agrostis stolonifera demonstrated resistance to 1% sodium chloride concentration in soil. These plants can serve as the basis for the creation of new salt-tolerant varieties.

Cell Selection Mechanism Based on Q-learning Environment in Femtocell LTE-A Networks

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.

Trustworthy and Cost-Effective Cell Selection for Sparse Mobile Crowdsensing Systems

Cell selection is a critical issue in sparse mobile crowdsensing (MCS) systems. However, the sensing cost heterogeneity among different cells (subareas) has long been ignored by existing works. Moreover, the data provided by participants are not always trustworthy, and some malicious participants may intend to launch data positioning attacks, which raises a new challenge for cell selection. In this paper, to address these issues, we propose a trustworthy and cost-effective cell selection (TCECS) framework that takes cell heterogeneity and malicious participants into consideration simultaneously. To this end, we first offer to utilize an iterative statistical spatial interpolation technique to identify trustworthy participants with the help of a small portion of dedicated sensors. Furthermore, we employ the regularized mutual coherence (RMC) in compressive sensing (CS) theory to characterize the contribution to inference accuracy of measurements submitted by different trustworthy participants. Finally, the cell selection strategy, which consumes the least sensing cost while satisfying a given sensing quality, is determined via an RMC-constrained optimization problem. Extensive experiments on a real-world taxi GPS dataset demonstrate that the proposed approach can mitigate the adverse effects of malicious participants and outperforms the baselines with less sensing cost for the same required sensing quality.

An Adaptive Cell Selection Scheme for 5G Heterogeneous Ultra-Dense Networks

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.

Applying a Dwell Time-Based 5G V2X Cell Selection Strategy in the City of Los Angeles, California

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%.

Outage-aware Matching Game Approach for Cell Selection in LTE/WLAN Multi-RAT HetNets

Heterogeneous networks (HetNets) which are consisting of multiple radio access technologies (Multi-RATs), offer a promising architecture for enhancing the mobile network capacity and improving the users’ quality of experience (QoE). However, the main challenge in such network architecture is the development of an efficient cell selection solution. In this paper, a distributed algorithm for the cell selection process is proposed to consider the impact of fast fading channel on the system performance. The cell selection process is formulated as an optimization problem, which is combinatorial, for minimizing the average outage probability and thus improving the system performance. Since there are many services that require a low outage probability, i.e. more sensitive to the system degradation, we are focusing in our paper on Long term evolution (LTE) wireless local area network (WLAN) aggregation (LWA) technology and how it may be used to enhance the outage performance of the system. Therefore, based on well-formulated utility functions for users and different stations in the multi-RAT HetNet, a matching game-based approach is proposed to provide a solution for the cell selection problem. Simulation results show the superiority of the proposed matching scheme compared to other cell selection schemes especially in terms of the achieved average outage probability with a very close performance to the optimal heuristic search solution. Also, the performance improvement as a result of deploying LWA stations in the network is shown. The reason for this improvement is that the aggregation enhances the achieved throughput significantly, which in turn improves the outage performance.

Charting the Complexity of the Marine Microbiome through Single-Cell Genomics

Marine bacteria and archaea play key roles in global biogeochemistry. To improve our understanding of this complex microbiome, we employed single-cell genomics and a randomized, hypothesis-agnostic cell selection strategy to recover 12,715 partial genomes from the tropical and subtropical euphotic ocean. A substantial fraction of known prokaryoplankton coding potential was recovered from a single, 0.4mL ocean sample, which indicates that genomic information disperses effectively across the globe. Yet, we found each genome to be unique, implying limited clonality within prokaryoplankton populations. Light harvesting and secondary metabolite biosynthetic pathways were numerous across lineages, highlighting the value of single-cell genomics to advance the identification of ecological roles and biotechnology potential of uncultured microbial groups. This genome collection enabled functional annotation and genus-level taxonomic assignments for >80% of individual metagenome reads from the tropical and subtropical surface ocean, thus offering a model to improve reference genome databases for complex microbiomes.

Developing E. coli-E. coli co-cultures to overcome barriers of heterologous tryptamine biosynthesis

Tryptamine is an alkaloid compound with demonstrated bioactivities and is also a precursor molecule to many important hormones and neurotransmitters. The high efficiency biosynthesis of tryptamine from inexpensive and renewable carbon substrates is of great research and application significance. In the present study, a tryptamine biosynthesis pathway was established in a metabolically engineered E. coli-E. coli co-culture. The upstream and downstream strains of the co-culture were dedicated to tryptophan provision and conversion to tryptamine, respectively. The constructed co-culture was cultivated using either glucose or glycerol as carbon source for de novo production of tryptamine. The manipulation of the co-culture strains’ inoculation ratio was adapted to balance the biosynthetic strengths of the pathway modules for bioproduction optimization. Moreover, a biosensor-assisted cell selection strategy was adapted to improve the pathway intermediate tryptophan provision by the upstream strain, which further enhanced the tryptamine biosynthesis. The resulting biosensor-assisted modular co-culture produced 194 ​mg/L tryptamine with a yield of 0.02 ​g/g glucose using shake flask cultivation. The findings of this work demonstrate that the biosensor-assisted modular co-culture engineering offers a new perspective for conducting microbial biosynthesis.

Fully Closed, Large-Scale, and Clinical Grade Cell Sorting of Hematopoietic Stem Cell (HSC)-Enriched CD90+ Cells for Transplantation and Gene Therapy

Introduction: Hematopoietic stem cell (HSC) gene therapy/editing is a viable treatment option for various hematological diseases and disorders including hemoglobinopathies and HIV/AIDS. Most if not all currently available approaches target CD34-enriched cell fractions, a heterogeneous mix of mostly committed progenitor cells and only very few true HSCs with long-term multilineage engraftment potential. As a consequence, gene therapy/editing approaches are currently limited in their HSC targeting efficiency, very expensive consuming huge quantities of modifying reagents, and can lead to unwanted side-effects in non-target cells. We recently described a novel HSC-enriched CD34 subset (CD90+CD45RA-) that is exclusively responsible for rapid recovery onset, robust long-term multilineage engraftment, as well as entire reconstitution of the bone marrow stem cell compartment in the nonhuman primate (NHP) stem cell transplantation and gene therapy model (Radtke et al. 2017, STM). Most importantly, we demonstrate that this CD34 subset reduces the number of target cells, modifying reagents and costs by more than 10-fold without compromising the long-term efficiency of gene-modification in the NHP (Humbert and Radtke et al. 2019, STM). Here, we aimed to develop a clinical protocol to reliably purify and efficiently gene-modify human HSC-enriched CD90+ cell fractions. Methods: Large-scale enrichment of CD34+ cells from GCSF-mobilized leukapheresis products was initially performed on the Miltenyi CliniMACS Prodigy according to previously established protocols (Adair et al. 2017, Nat. Comm.). Yield, purity, quality, and feasibility of CD90 sorting was then comprehensively tested on two different commercially available cell sorting systems comparing the jet-in-air sorter FX500 from Sony and the cartridge-based closed-system sorter MACSQuant Tyto from Miltenyi Biotech with our clinically approved gold-standard CD34-mediated gene therapy approach. Sorted CD90+ and bulk CD34+ cells were transduced with a clinical-grade lentivirus encoding for GFP and the multilineage differentiation as well as engraftment potential tested using in vitro assays and the NSG mouse xenograft model, respectively. Results: Flow-cytometric sort-purification of CD90+ cells was similarly efficient in purity and yield using either the FX500 or Tyto (Figure A,B). Both approaches reliably reduced the overall target cell count by 10 to 15-fold without impacting the cells viability and in vitro colony-forming cell potential. Unexpectedly, the transduction efficiency of sort-purified CD90+ cells was significantly improved compared to bulk-transduced CD34+ cells and especially the CD34+CD90+ subset (Figure C). All cell fractions demonstrated robust mouse xenograft potential (Figure D). Most importantly, significantly higher levels of GFP+ expression in the peripheral blood, bone marrow, spleen and thymus were observed after transplantation of gene-modified CD90+ compared to bulk CD34+ cells in NSG mice (Figure E). Conclusion: Here, we show that sort-purification of our HSC-enriched CD34+CD90+ cell subset is technically feasible and highly reproducible in two different systems. Purification of human CD90+ cell fractions significantly increased the gene-modification efficiency of primitive human HSCs with multilineage mouse engraftment potential. These findings should have important implications for currently available as well as future HSC gene therapy and gene editing protocols. Isolation of an HSC-enriched phenotype will allow more targeted gene modification and thus likely reduce unwanted off target effects. Our approach further reduced the overall costs for gene modifying reagents, can be combined with a closed transduction system, increase the portability and ultimately make HSC gene therapy GMP-facility independent and affordable. Finally, this stem cell selection strategy may also allow efficient and effective depletion of donor T cells in the setting of allogeneic stem cell or organ transplantation. Figure: A) Purity and B) yield of CD90+ cells after sort-purification. C) Transduction efficiency of bulk-transduced CD34+CD90+ cells and sort-purified CD90+ cells. Frequency of D) human chimerism and E) GFP+ human CD45+ cells in the peripheral blood (PB), bone marrow, spleen and thymus after transplantation of gene-modified bulk CD34+ or sort-purified CD90+ cells. Figure Disclosures Kiem: CSL Behring: Consultancy; Rocket Pharma: Consultancy, Equity Ownership; Homology Medicines: Consultancy, Equity Ownership; Magenta Therapeutics: Consultancy.

De novo phenol bioproduction from glucose using biosensor-assisted microbial coculture engineering.

Microbial biosynthesis has been extensively adapted for the production of commodity chemicals using renewable feedstocks. This study integrated metabolite biosensors into rationally designed microbial cocultures to achieve high-efficiency bioproduction of phenol from simple carbon substrate glucose. Specifically, two sets of E. coli-E. coli cocultures were first constructed for accommodation of two independent phenol biosynthesis pathways via 4-hydroxybenzoate (4HB) and tyrosine (TYR), respectively. Biosensor-assisted microbial cell selection mechanisms were subsequently incorporated into the coculture systems to address the insufficient pathway intermediate provision that limited the overall bioproduction. For the 4HB- and TYR-dependent pathways, this approach improved the phenol production by 2.3- and 3.9-fold, respectively, compared to the monoculture controls. Notably, the use of biosensor-assisted cell selection strategy in monocultures resulted in reduced phenol production, highlighting the advantage of coculture engineering for coupling with biosensing. After stepwise optimization, the phenol bioproduction yield of the engineered coculture's reached 0.057 g/g glucose. Furthermore, the coculture biosynthesis was successfully scaled up at both shake flask and bioreactor levels. Overall, the findings of this study demonstrate the outstanding potential of coupling biosensing and modular coculture engineering for advancing microbial biosynthesis of valuable molecules from renewable carbon substrates.

Service Characteristics-Oriented Joint ACB, Cell Selection, and Resource Allocation Scheme for Heterogeneous M2M Communication Networks

Heterogeneous machine-to-machine communication networks (HMCNs) are expected to provide ubiquitous connectivity for machine type communication devices (MTCDs) with reduced human intervention. Although various types of services will be supported for MTCDs, diverse quality of service requirements and network characteristics pose challenges and difficulties to the resource allocation, cell selection, and random access schemes of the HMCNs. In this paper, we propose a joint access class barring (ACB), cell selection, and resource allocation algorithm for HMCNs. To achieve efficient utilization of the resource blocks (RBs) in heterogeneous cells, we first propose a virtual clustering-based joint ACB and RB allocation sub-algorithm. Partitioning the MTCDs into virtual clusters, we formulate the joint ACB and RB allocation problem of the virtual clusters as RB utilization maximization problem. Through solving the optimization problem, we obtain the optimal ACB factor and RB allocation strategy. Given the optimal strategy, we then formulate the joint cell selection and power allocation problem of the MTCDs as a utility function maximization problem. As the formulated problem is a mixed nonlinear optimization problem, which cannot be solved conveniently, we transform the problem using relaxation method and solve it through applying an iterative algorithm and the Lagrange dual method. Simulation results demonstrate the effectiveness of the proposed algorithm.

QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity

Dual connectivity (DC) was first proposed in 3GPP Release 12 which allows one piece of user equipment (UE) to connect to two base stations in heterogeneous networks (HetNet) at the same time, to increase the flexibility of resource utilization. DC has been further extended to multiple connectivity in 5G New Radio (NR). On the other hand, different UE tends to have different bandwidth requirements. Thus, in DC, one of the challenging issues is how to integrate resources from two base stations to enhance the quality of service (QoS) as well as the data transfer rate of each UE. In this paper, we proposed novel resource management mechanisms to improve the QoS of UE in the co-channel dual connectivity network. In terms of resource allocation, we designed the (MTS) which, in principle, allocates a resource block to the UE with the best channel quality while considering the issues of intercell resource allocation and the QoS requirement of each UE. In order to balance the load of different cells, we designed a novel cell selection scheme based on the HetNet Congestion Indicator (HCI) which considers not only the signal quality of UE but also the remaining resources of each base station. To improve the QoS of cell edge UE, cell range expansion (CRE) and the Almost Blank Subframe (ABS) were proposed in 3GPP. In this paper, based on Q-learning, we designed an adaptive mechanism which dynamically adjusts the ABS ratio according to the network condition to improve resource utilization. Our simulation results showed that our MTS scheduler was able to achieve a 31.44% higher data rate than the Proportional Fairness Scheduler; our HCI cell selection scheme yielded a 2.98% higher data rate than the signal-to-interference plus noise ratio (SINR) cell selection scheme; the QoS satisfaction rate of our Q-learning dynamic ABS scheme was 4.06% higher than that of the Static ABS scheme. Finally, for the cell edge users who often suffer poor data transfer rate, by integrating the mechanisms of DC, CRE, and ABS, our experimental results showed that the QoS satisfaction ratio of cell edge UEs could be improved by 10.76% as compared to the single connectivity and no ABS situation.

Hybrid multi-independent mmWave MNOs assessment utilising spectrum sharing paradigm for 5G networks

Spectrum sharing paradigm (SSP) has recently emerged as an attractive solution to provide capital expenditure (CapEx) and operating expenditure (OpEx) savings and to enhance spectrum utilization (SU). However, practical issues concerning the implementation of such paradigm are rarely addressed (e.g., mutual interference, fairness, and mmWave base station density). Therefore, in this paper, we proposed ultra-reliable and proportionally fair hybrid spectrum sharing access strategy that aims to address the aforementioned aspects as a function of coverage probability (CP), average rate distributions (ARD), and the number of mmWave base stations (mBSs). In this strategy, the spectrum is sliced into three parts (exclusive, semi-pooled, and fully pooled). A typical user that belongs to certain operator has the right to occupy a part of the spectrum available in the high and low frequencies (28 and 73 GHz) based on an adaptive multi-state mmWave cell selection scheme (AMMC-S) which associates the user with the tagged mBS that offers a highest SINR to maintain more reliable connection and enrich the user experience. Numerical results show that significant improvement in terms of ARD, CP, fairness among operators, and maintain an acceptable level of mBSs density.