Mobile Networks Research Articles

Proposed Explainable Interference Control Technique in 6G Networks Using Large Language Models (LLMs)

After the advent of 5th generation (5G) and 6th generation (6G) cellular networks, the complexity of managing real-time signal interference has increased in dense and dynamic environments. Traditional interference techniques, such as frequency reuse and allocation, while effective, lack robust adaptability and transparency needed to reduce interference in advanced communication networks. This paper introduces a novel approach that fuses large language models (LLMs) and Explainable Artificial Intelligence (XAI) to mitigate interference and enhance interference management in the mathematical foundations of 6G networks. The proposed approach provides accurate interference predictions, which the LLM balances with its complex architecture, necessary to meet the demands of beyond 5G and 6G networks, along with interpretable explanations to ensure transparency in decision-making. The proposed framework has been evaluated across various performance metrics. Interference latency consistently achieves lower rates of 0.95 s, compared to traditional techniques, which average around 1 s. Furthermore, the confidence score of the LLM shows a stable value of 0.87 throughout the system, compared to 0.85 in techniques without LLMs. Overall, the XAI-driven LLM demonstrates the potential of incorporating LLMs and XAI into wireless networks to improve resilience in next-generation networks. This proof of concept introduces a novel framework that offers new dimensions in wireless communication, particularly for interference management, prediction, and mitigation.

A Study of Transmission Point Selection for Multi-Connectivity in Multi-Band Wireless Networks

In 5th generation mobile communication networks, the frequency band of the millimeter band of 30–100 GHz is supported to provide a transmission speed of 20 Gbps or higher. Furthermore, a huge transmission capacity, i.e., up to 1 Tbps, is one of the main requirements for the 6th generation mobile communication networks. In order to meet this requirement, the terahertz band is considered a new service band. Hence, we consider multi-band network environments, serving the sub-6Hz band, mmWave band, and additionally sub-terahertz band. Furthermore, we introduce the transmission point selection criteria with multiple connections for efficient multi-connectivity operation in a multi-band network environment, serving and receiving multiple connections from those bands at the same time. We also propose a point selection algorithm based on the selection criteria, e.g., achievable data rate. The proposed point selection algorithm attains lower computational complexity with a 2-approximation of the optimal solution. Our simulation results, applying the channel environment and beamforming in practical environments defined by 3GPP, show that selecting and serving multiple transmission points regardless of frequency band performs better than services through single-connectivity operation.

Land Use and Land Cover Change Analysis in Brazilian Urban centres: an approach using Cellular Automata and Neural Networks

Abstract. During the 1970s, Brazil experienced most of its population living in cities for the first time. Historically, urbanization has been intensely related to land use and cover changes, amplifying climatic and ecological stress in recently established metropolises. This study aims to analyze, through classified Remote Sensing images and the Cellular Automata and Artificial Neural Networks (CA-ANN) machine learning model, land use and land cover trends in regions that have experienced accentuated demographic growth in the decades 2000 and 2010. The methodology consists of: i) identifying areas with a high density of buildings, ii) defining the variables that drive land use change, and iii) proposing a methodology for predicting changes in the urban area. The results indicate that the urban class prediction presented high precision (≥ 0.74) and recall (≥ 0.86) indices. Forest class also presented a high precision score (≥0.72), showing an elevated prediction hit rate. Furthermore, the proposed methodology improved the results obtained in previous works for the same cities, presenting higher Kappa values in all cases.

Improved DV-Hop algorithm based on geometric Brownian motion model under communication interference

Abstract In a mobile sensor network, a traditional positioning algorithm is unable to locate unknown nodes when losing anchor positions caused by communication interference. To solve this problem, an improved DV-Hop algorithm based on a geometric Brownian motion (GBM) model was proposed including two main stages: location of sink node (LSN) and location of blind node (LBN). In the LSN stage, if the signal transmission of anchors is normal, the GBM model records the moving positions of the anchors. If not, the GBM model predicts the estimated average positions of the anchors using recorded data. Then, the trial count of the GBM model is optimized to further improve the prediction accuracy and computational overhead. In the LBN stage, the unknown nodes’ positions are obtained by the DV-Hop algorithm. In a traditional DV-Hop algorithm, the approximate minimum hop number and average hop distance may lead to huge deviation between true position and estimated position. To improve the positioning accuracy in the LBN stage, the strategies of multi-communication radius and hop distance weighting were adopted. The simulation results demonstrated that the proposed algorithm has the capability to resist communication interference and adaptability at different node speeds , maintaining a relatively high accuracy in locating unknown nodes.

Machine Learning-Based Resource Allocation Algorithm to Mitigate Interference in D2D-Enabled Cellular Networks

Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network poses two major challenges. First, interference management between D2D and cellular users directly affects a system’s performance. Second, achieving an acceptable level of link quality for both D2D and cellular networks is necessary. An optimum resource allocation is required to mitigate the interference and improve a system’s performance. In this paper, we provide a solution to interference management with an acceptable quality of services (QoS). To this end, we propose a machine learning-based resource allocation method to maximize throughput and achieve minimum QoS requirements for all active D2D pairs and cellular users. We first solve a resource optimization problem by allocating spectrum resources and controlling power transmission on demand. As resource optimization is an integer nonlinear programming problem, we address this problem by proposing a deep Q-network-based reinforcement learning algorithm (DRL) to optimize the resource allocation issue. The proposed DRL algorithm is trained with a decision-making policy to obtain the best solution in terms of spectrum efficiency, computational time, and throughput. The system performance is validated by simulation. The results show that the proposed method outperforms the existing ones.

Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio

Mobile communication is ubiquitous in everyday life. The fifth generation of mobile networks (5G) introduced 5G New Radio as a radio access technology that meets current bandwidth, quality, and application requirements. Network steganographic channels that hide secret message transfers in an innocent carrier communication are a particular threat in mobile communications as these channels are often used for malware, ransomware, and data leakage. We systematically analyze the protocol stack of the 5G–air interface for its susceptibility to network steganography, addressing both storage and timing channels. To ensure large coverage, we apply hiding patterns that collect the essential ideas used to create steganographic channels. Based on the results of this analysis, we design and implement a network covert storage channel, exploiting reserved bits in the header of the Packet Data Convergence Protocol (PDCP). the covert sender and receiver are located in a 5G base station and mobile device, respectively. Furthermore, we sketch a timing channel based on a recent overshadowing attack. We evaluate our steganographic storage channel both in simulation and real-world experiments with respect to steganographic bandwidth, robustness, and stealthiness. Moreover, we discuss countermeasures. Our implementation demonstrates the feasibility of a covert channel in 5G New Radio and the possibility of achieving large steganographic bandwidth for broadband transmissions. We also demonstrate that the detection of the channel by a network analyzer is possible, limiting its scope to application scenarios where operators are unaware or ignorant of this threat.

Natural forgetting reversibly modulates engram expression

Memories are stored as ensembles of engram neurons and their successful recall involves the reactivation of these cellular networks. However, significant gaps remain in connecting these cell ensembles with the process of forgetting. Here, we utilized a mouse model of object memory and investigated the conditions in which a memory could be preserved, retrieved, or forgotten. Direct modulation of engram activity via optogenetic stimulation or inhibition either facilitated or prevented the recall of an object memory. In addition, through behavioral and pharmacological interventions, we successfully prevented or accelerated forgetting of an object memory. Finally, we showed that these results can be explained by a computational model in which engrams that are subjectively less relevant for adaptive behavior are more likely to be forgotten. Together, these findings suggest that forgetting may be an adaptive form of engram plasticity which allows engrams to switch from an accessible state to an inaccessible state.

Natural forgetting reversibly modulates engram expression.

Memories are stored as ensembles of engram neurons and their successful recall involves the reactivation of these cellular networks. However, significant gaps remain in connecting these cell ensembles with the process of forgetting. Here, we utilized a mouse model of object memory and investigated the conditions in which a memory could be preserved, retrieved, or forgotten. Direct modulation of engram activity via optogenetic stimulation or inhibition either facilitated or prevented the recall of an object memory. In addition, through behavioral and pharmacological interventions, we successfully prevented or accelerated forgetting of an object memory. Finally, we showed that these results can be explained by a computational model in which engrams that are subjectively less relevant for adaptive behavior are more likely to be forgotten. Together, these findings suggest that forgetting may be an adaptive form of engram plasticity which allows engrams to switch from an accessible state to an inaccessible state.

Targeting AXL cellular networks in kidney fibrosis

IntroductionThe incidence of chronic kidney disease (CKD) is increasing, in parallel with risk factors including obesity and diabetes mellitus. AXL plays a central role in CKD, providing a rationale to evaluate clinical AXL targeting agents.MethodsTo determine the efficacy and underlying molecular mechanisms of AXL inhibition in CKD, we employed a murine unilateral ureteral obstruction (UUO) model preventively treated with a selective AXL kinase inhibitor (bemcentinib) during disease progression. We isolated kidneys at an early (3 days) or late (15 days) timepoint and profiled the cell populations using mass cytometry.ResultsPreventive treatment with bemcentinib significantly attenuated fibrosis in the UUO model. The anti-fibrotic effect correlated with a decrease in mesangial cells and inhibition of innate immune cell infiltration, while the proportion of epithelial cells increased. We mapped AXL expression to a unique network of cells in the kidney: mesangial cells, pericytes, macrophages and dendritic cells.DiscussionWe propose that AXL targeting affects an important cellular interaction network underlying fibrotic progression. These results support the clinical application of AXL targeting agents to treat CKD.

Design and synchronization control application of a new five-dimensional memristor CNN conservative hyperchaotic system

Abstract Incorporating memristors into a cellular neural network (CNN) and introducing chaotic characteristics can generate in highly complex and unpredictable dynamic behaviors. To advance this research area, this paper proposes a new five-dimensional memristor CNN conservative hyperchaotic system and systematically analyzes its dynamic properties. The analysis content includes equilibrium point analysis, Poincaré sections, Lyapunov exponent spectra, bifurcation diagrams, two-parameter Lyapunov exponent spectra, complexity assessment, homogeneous and heterogeneous extreme multistability, etc. In addition, the simulation circuit for the new system is designed and constructed. The digital circuit of the new system is implemented using a microcontroller (MCU). After running simulations, the experimental results from the analog circuit, digital circuit, and numerical simulation are consistent with each other, demonstrating the feasibility of the circuit implementation. Finally, two different synchronization control strategies are employed to achieve synchronization control within a finite time.

An Ala/Glu difference in E1 of Cx26 and Cx30 contributes to their differential anionic permeabilities.

Two closely related connexins, Cx26 and Cx30, share widespread expression in the cochlear cellular networks. Gap junction channels formed by these connexins have been shown to have different permeability profiles, with Cx30 showing a strongly reduced preference for anionic tracers. The pore-forming segment of the first extracellular loop, E1, identified by computational studies of the Cx26 crystal structure to form a parahelix and a narrowed region of the pore, differs at a single residue at position 49. Cx26 contains an Ala and Cx30, a charged Glu at this position, and cysteine scanning in hemichannels identified this position to be pore-lining. To assess whether the Ala/Glu difference affects permeability, we modeled and quantified Lucifer Yellow transfer between HeLa cell pairs expressing WT Cx26 and Cx30 and variants that reciprocally substituted Glu and Ala at position 49. Cx26(A49E) and Cx30(E49A) substitutions essentially reversed the Lucifer Yellow permeability profile when accounting for junctional conductance. Moreover, by using a calcein efflux assay in single cells, we observed a similar reduced anionic preference in undocked Cx30 hemichannels and a reversal with reciprocal Ala/Glu substitutions. Thus, our data indicate that Cx26 and Cx30 gap junction channels and undocked hemichannels retain similar permeability characteristics and that a single residue difference in their E1 domains can largely account for their differential permeabilities to anionic tracers. The higher anionic permeability of Cx26 compared with Cx30 suggests that these connexins may serve distinct signaling functions in the cochlea, perhaps reflected in the vastly higher prevalence of Cx26 mutations in human deafness.

β3-adrenergic agonist counters oxidative stress and Na+-K+ pump inhibitory S-glutathionylation of placental cells: Implications for preeclampsia.

Oxidative stress from placental ischemia/reperfusion and hypoxia/reoxygenation (H/R) in preeclampsia is accompanied by Na+-K+ pump inhibition and S-glutathionylation of its β1 subunit (GSS-β1), a modification that inhibits the pump. β3-adrenergic receptor (β3-AR) agonists can reverse GSS-β1. We examined effects of the agonist CL316,243 on GSS-β1 and sources of H/R-induced oxidative stress in immortalized first trimester human trophoblast (HTR-8/SVneo) and freshly isolated placental explants from normal term pregnancies. H/R increased GSS-β1 and, reflecting compromised α1/β1 subunit interaction, it reduced α1/β1 pump subunit co-immunoprecipitation. H/R increased p47phox/p22phox NADPH oxidase subunit co-immunoprecipitation reflecting membrane translocation of cytosolic p47phox that is needed to activate NADPH oxidase. Fluorescence of O2•--sensitive dihydroethidium increased in parallel. H/R increased S-glutathionylation of endothelial nitric oxide synthase (GSS-eNOS) that uncouples NO synthesis towards synthesis of O2•- and reduced trophoblast migration. Oxidative stress induced by tumor necrosis factor α (TNF-α) increased soluble fms-like tyrosine kinase receptor 1 (sFlt-1) trophoblast release, a marker of preeclampsia, and reduced trophoblast integration into endothelial cellular networks. CL316,243 eliminated H/R-induced GSS-β1 and decreases of α1/β1 subunit coimmunoprecipitation, eliminated NADPH oxidase activation and increases in GSS-eNOS, restored trophoblast migration, eliminated increased sFlt-1 release and restored trophoblast integration in endothelial cell networks. H/R induced GSS-β1, α1/β1 subunit co-immunoprecipitation and NADPH oxidase activation of placental explants reflected effects of H/R for trophoblasts and CL316,243 eliminated these changes. We conclude a β3-AR agonist counters key pathophysiological features of preeclampsia in vitro. β3 agonists already in human use for another purpose are potential candidates for re-purposing to treat preeclampsia.

Formation control of networked mobile robots with weighted directed topology using the Udwadia–Kalaba approach

Formation control of networked mobile robots with weighted directed topology using the Udwadia–Kalaba approach

Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.

Call Blocking Estimation Using Adaptive Channel Reservation for Hand‐Off Calls in Wireless Cellular Network

ABSTRACTThe wireless cellular network (WCN) provides services to many users with limited radio resources. In WCN, resource allocation is important to provide the desired quality of service (QoS). Among the two possible types of calls in WCN, hand‐off calls (HCs) have higher priority than new calls (NCs). In this paper, we developed a mathematical model to estimate call blocking using an optimal number of guard channel (GC) for HCs by considering target hand‐off call dropping probability (HCDP). We named this model the optimal guard channel reservation (OCR) policy. This analytical model assumes the long‐term average of the traffic parameters and so is far from making any correlation with the operation of the existing system. In real, the traffic parameters are dynamic. Further, we proposed an optimal adaptive guard channel reservation (OACR) policy to obtain optimal performance. It adjusts the GCs dynamically w.r.t the traffic conditions to keep HCDP below the target. We have implemented the OACR scheme by using a test bed. Finally, we have applied both mathematical model (i.e., OCR policy) and OACR policy to a wireless cellular network, that is, Global System for Mobile Communication (GSM) 900 system.

BWSAR: A Single-Drone Search-and-Rescue Methodology Leveraging 5G-NR Beam Sweeping Technologies for Victim Localization

Drones integrated with 5G New Radio (NR) base stations have emerged as a promising solution for efficient victim search and localization in emergency zones where cellular networks are disrupted by natural disasters. Traditional approaches relying solely on uplink Sounding Reference Signal (SRS) for localization face limitations due to User Equipment (UE) power constraints. To overcome this, our paper introduces BWSAR, a novel three-stage Search-and-Rescue (SAR) methodology leveraging 5G-NR beam sweeping technologies. BWSAR utilizes downlink Synchronization Signal Block (SSB) for coarse-grained direction estimation, guiding the drone towards potential victim locations. Subsequently, finer-grained beam sweeping with Positioning Reference Signal (PRS) is employed within the identified direction, enabling precise three-dimensional UE coordinate estimation. Furthermore, we propose a trajectory optimization algorithm to expedite the drone’s navigation to emergency areas. Simulation results underscore BWSAR’s efficacy in reducing positioning errors and completing SAR missions swiftly, within minutes.

LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities

The increasing use of mobile networks is an opportunity to collect and model users’ movement data for extracting knowledge about life and health while considering privacy leakage risk. This study aims to approximate the lifestyles of urban residents, employing statistical information derived from their movements among various Points of Interest (PoI). Our investigations comprehend a multidimensional analysis of key urban factors to provide insights into the population’s daily routines, preferences, and characteristics. To this end, we developed a framework called LEAF that models lifestyles by interpreting anonymized cell phone mobility data and integrating it with information from other sources, such as geographical layers of land use and sets of PoI. LEAF presents the information in a vector space model capable of responding to spatial queries about lifestyle. We also developed a consolidated lifestyle pattern framework to systematically identify and analyze the dominant activity patterns in different urban areas. To evaluate the effectiveness of the proposed framework, we tested it on movement data from individuals in a medium-sized city and compared the results with information collected through surveys. The RMSE of 5.167 between the proposed framework’s results and survey-based data indicates that the framework provides a reliable estimation of lifestyle patterns across diverse urban areas. Additionally, summarized patterns of criteria ordering were created, offering a concise and intuitive representation of lifestyles. The analysis revealed high consistency between the two methods in the derived patterns, underscoring the framework’s robustness and accuracy in modeling urban lifestyle dynamics.

Tower Companies vs. Mergers in Mobile Networks

Tower Companies vs. Mergers in Mobile Networks

Comprehensive gene set enrichment and variation analyses identify SUV39H1 as a potential prognostic biomarker for glioblastoma immunorelevance

Glioblastoma (GBM) is the most common intracranial malignancy. SUV39H1 encodes a histone H3 lysine 9 methyltransferase that acts as an oncogene in several cancers; however, its role in GBM remains unknown. We obtained GBM transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database on the UCSC Xena platform to perform differential and enrichment analyses of genes in the SUV39H1 high- and low-expression groups to construct a prognostic risk model. Analysis of SUV39H1 related biological processes in GBM was performed by gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). High- and low-risk subgroup mutation signatures were analyzed using maftools. Immune infiltration was evaluated using IOBR and CIBERSORT algorithms. We analyzed the cell types and intercellular communication networks in glioma stem cells (GSCs) using scRNA-seq. The effects on GBM cells and GSCs after inhibition of SUV39H1 were investigated in vitro. SUV39H1 was significantly overexpressed in GBM and associated with poor prognosis. SUV39H1-related differentially expressed genes were enriched in immune and inflammation related pathways, and GSEA revealed that these genes were significantly enriched in signaling pathways such as IL-18, oxidative phosphorylation, and regulation of TP53 activity. Mutational analysis revealed frequent alterations in TP53 and PTEN expression. In addition, the infiltration abundances of the five immune cell types were significantly different between the high- and low-expression groups. Analysis of cellular communication networks by scRNA-seq revealed a strong interaction between CRYAB-GSC and PTPRZ1-GSC in GSCs. In vitro experiments verified that knockdown of SUV39H1 inhibited the viability and proliferation of U87 and U251 glioblastoma cells and downregulated the expression of stemness markers Nestin and SOX2 in CSC1589 and TS576 GSC lines. Increased SUV39H1 expression is associated with immune cell infiltration and poor prognosis in patients with GBM. Inhibition of SUV39H1 restrains GBM growth and reduces the stem cell properties of GSC. Thus, SUV39H1 might be a prognostic predictor and immunotherapeutic target in patients with GBM.

Identification and validation of CCN family genes to predict the prognosis in gastric cancer

BackgroundGastric cancer (GC) is a deadly malignancy with an ever-increasing incidence worldwide. The cellular communication network (CCN) family serves as matricellular proteins and exerts their various functions via regulating cell proliferation and differentiation. This study aimed to perform an integrated analysis of CCNs to predict the prognosis in GC.MethodsThe microarray datasets were obtained from Gene Expression Omnibus database to identify the differentially expressed genes between GC and non-tumor tissues. Functional enrichment and genetic alteration analysis revealed the biological functions and alteration status associated with CCNs. We analyzed the mRNA and protein expressions of CCN family in GC patients. Furthermore, the prognostic value of distinct CCN family members were analyzed using the Kaplan–Meier plotter database. Finally, the human gastric cancer cell lines were used for in vitro experiments to further validate the role of WISP1.Results26 genes were firstly identified to be significantly highly expressed in gastric tumor tissues. CCN family genes were identified to predict the prognosis in GC. Among the six CCNs, WISP1 is upregulated in GC tissues and its highly expression is associated with poor survival in GC patients. Moreover, a significant correlation is found between the expression of WISP1 and the pathological stage of patients with GC. Additionally, in vitro experiments demonstrated that WISP1 promotes the proliferation and invasive potential of GC cells, suggesting it may be a potential therapeutic target for GC.ConclusionsA comprehensive bioinformatic analysis of CCN genes provides new insights into the potential roles of this family in GC. Importantly, WISP1 may be a good prognostic predictor and a potential therapeutic target for GC.