Heterogeneous Network Architecture Research Articles

Railway network delay evolution: A heterogeneous graph neural network approach

Accurate delay evolution prediction plays a pivotal role in train rescheduling decision-making for the railway network. Existing studies on delay prediction predominantly centered around predicting delays for each train in the subsequent stations (i.e., following a train-oriented perspective). Furthermore, train operations in the railway network involve different types of entities (stations, trains, etc.), making the current graph/network models with homogenous nodes (i.e., the same kind of nodes) incapable of effectively capturing the interactions between the entities. This paper develops a network-oriented model to investigate the train delay evolution on railway networks, by predicting the delays of running trains in the network after a given time interval. The proposed model combines the GraphSAGE graph neural network (GNN) and the heterogeneous graph neural network (HetGNN) architecture, thus called SAGE-Het, enabling it to capture interactions between heterogeneous nodes (i.e., different types of nodes) based on different edges (e.g., edges between trains, trains, and stations). Additionally, SAGE-Het allows for flexible inputs in contrast to conventional machine learning techniques, whose inputs must meet the consistent dimension requirement (e.g., in the form of rectangular or grid-like arrays). The performance and robustness of the suggested SAGE-Het model are assessed in experiments on the data from two sub-networks of the China railway network. The experimental results demonstrate that SAGE-Het outperforms the existing delay prediction methods and some advanced HetGNNs used for prediction tasks in other domains; SAGE-Het demonstrates excellent scalability, capable of handling various types of nodes; the predictive performances of SAGE-Het under different prediction time horizons (10/20/30 min ahead) all exhibit better performance over other baselines; the accuracies are over 90 % under the permissible 3-minute errors for the three prediction time horizons. Specifically, the impact of train interactions on delay evolution is investigated based on the flexible input characteristic of the proposed model. The results illustrate that train interactions become subtle with the increase of train headways. This finding directly contributes to decision-making in situations where conflict resolution or train-canceling actions are needed.

Voronoi diagrams and tree structures in HRP-EE: Enhancing IoT network lifespan with WSNs

Within the domain of the Internet of Things (IoT), wireless sensor networks (WSNs) play a pivotal role, facilitating advancements in sectors such as smart urban infrastructures, intelligent healthcare systems, and residential automation. Despite their versatility, WSNs grapple with challenges like limited energy reserves and constrained computational capacities, making energy conservation a paramount concern for IoT deployments underpinned by WSNs. This study introduces the Hybrid Routing Protocol for Efficient Energy (HRP-EE) designed to enhance the operational efficiency of WSNs. The HRP-EE protocol unfolds over three meticulous stages: the selection of cluster heads, the formation of clusters, and the establishment of routing pathways. Initially, the protocol evaluates nodes based on their residual energy and their Euclidean distance to the central sink (or gateway) device to designate apt cluster heads. Subsequently, these chosen cluster heads are integrated into the Voronoi diagram as central nodes to orchestrate cluster architectures. In the terminal stage, an innovative hybrid algorithm is instituted. This algorithm amalgamates the principles of the Minimum Spanning Tree for structuring intra-cluster communication trees and Dijkstra’s algorithm to ascertain the most efficient paths for inter-cluster data transmission from cluster heads to the sink device. The primary objective of this protocol is the judicious utilization of the sensor nodes’ energy, thereby optimizing the overall network longevity. To assess the proficiency of HRP-EE, we executed a series of simulations using NS2. Comparative analyses reveal that HRP-EE outperforms existing protocols such as LEACH-VA, PEGCP, and TBC, delivering superior energy efficiency, extended network lifespan, and enhanced throughput across both homogeneous and heterogeneous network architectures.

Stable matching with evolving preference for adaptive handover in cellular-connected UAV networks

Unmanned Aerial Vehicles (UAVs) can effectively and reliably complete aerial tasks in various fields. However, the high-speed mobility of UAVs and the 6G ultra-dense heterogeneous network architecture in future will lead to frequent handover of UAVs during aerial tasks in cellular networks, increasing the potential service interruption rate. Therefore, there is an urgent requirement to address the lack of service adaptability in the overall time-space dimension. To achieve the seamless handover in highly dynamic and stochastic scenarios, we firstly transform the handover problem into a stable matching model. We then extend the stable matching of single time slot to the overall time-space dimension, and transform the dynamically changing time-space information into the preference relations evolution. By flexibly adapting the evolution of the preference lists to current network topology, we propose a Dynamic Stable Matching based Adaptive Handover (DSMAH) algorithm to find stable matching efficiently in a dynamic environment. Simulation results show that the proposed algorithm can achieve a better trade-off between communication quality, handover frequency, and convergence speed and significantly improves the stability of the cellular-connected network as compared to benchmark schemes. The proposed scheme not only effectively addresses the challenges posed by frequent handovers and ping-pong effect, but also shows its notable advantages in dynamic environments.

Cluster stability-driven optimization for enhanced routing in heterogeneous vehicular networks

The new era of the Internet of Things is promoting the evolution of self-driving vehicles into connected and autonomous vehicles (CAVs). The deployment of CAVs in smart cities is highly dependent on the performance of their underlying networks known as vehicular networks. Designing an effective clustering approach is of great importance in such dynamic networks as it can significantly improve the reliability and scalability of the routing protocols. In this paper, we consider a heterogeneous vehicular network architecture that supports vehicle-to-vehicle and vehicle-to-infrastructure communications based on IEEE 802.11p and cellular networks (LTE/5G) with direct communications, namely cellular vehicle-to-everything (C-V2X) technologies. We introduce a novel clustering scheme for real-time routing based on the proposed network architecture. We formulate the problem of optimal clustering for connected and autonomous vehicles (OCCAV) and show that the problem is NP-hard. We then develop a clusters' stability maximization algorithm (CSM), which utilizes the stability degree of vehicles over a prediction horizon to efficiently solve the optimization problem in real-time. The algorithm is used within a rolling horizon framework for continuously solving the problem, making the resulting clusters adaptive to future traffic dynamics. We propose a hybrid routing protocol based on our clustering scheme, aiming to improve the packet delivery ratio and reduce the average delivery delay. For evaluation purposes, we use extensive realistic simulations based on mobility scenarios validated using real vehicular trajectories. The results demonstrate that our clustering scheme improves the alternative algorithms in terms of the average cluster head duration, cluster head change rate, cluster member duration, and overall stability by 61%, 62%, 44%, and 52%, respectively. CSM also outperforms clustering overhead by 54%. Compared to the other cluster-based routing, our scheme also achieves a higher packet delivery ratio by up to 30%, and a lower average delay by up to 52%. To provide an in-depth analysis of the optimality of our scheme and its alternatives, we also use the Gurobi optimizer to find an optimal solution to the OCCAV problem. The results suggest that our scheme can achieve near-optimal cluster stability.

Network Calculus Theory and Application: A New Graduate Course for Engineering Students

With the development of modern networks, the quality of service guarantee has become even more important than before. However, various new types of traffic and heterogeneous network architecture make it quite challenging to evaluate the quality of service, such as delay and reliability. Therefore, a novel analysis method is needed to solve this situation. Network calculus is a theory used to analyze the queueing problems in networks, which is the main content to be taught in this graduate course. Before network calculus theory is introduced, the classical queueing theory will be taught. Then, the mathematical fundamentals of network calculus, such as min-plus algebra and cumulative arrival/service process, will be introduced. Next, two branches of network calculus, i.e., deterministic network calculus and stochastic network calculus, will be introduced separately, including the definition of deterministic/stochastic arrival curve and service curve, as well as important theorems, such as delay theorem, flow aggregation theorem, server concatenation theorem. After the theoretical knowledge, practical content will be taught in two parts. First, students are encouraged to establish system models based on their graduate research topics, analyze the service guarantee of their models, and present their work in class. Second, a traffic data collection platform will be taught, and students will collect various traffic data and analyze traffic characteristics based on network calculus theory. Based on the feedback from students during the past three years, students are satisfied with this new course because it focuses on frontier technology and the teaching activities are well-organized.

An efficient energy saving scheme using reinforcement learning for 5G and beyond in H-CRAN

Maximizing the energy saving is one of the most important metrics in 5G and Beyond (B5G) cellular mobile networks. In order to satisfy the diverse requirements of 5G/B5G in dynamic environments, Reinforcement Learning (RL) is proven as a viable approach for solving resource management problems, especially for 5G energy resources. In this paper, we propose to apply the Q-Learning (QL) Reinforcement technique in the Heterogeneous Cloud 5G Radio Access Network (H-CRAN) architecture in order to optimize the energy efficiency in 5G/B5G networks. We compare its results with the Genetic Algorithm variant using Transformation (TGA) and Particle Swarm Optimization (PSO) under high and low traffic demands. The experimental results reveal the efficiency of RL compared to TGA and PSO techniques in terms of energy efficiency and system capacity.

Pre-Impact Firefighter Fall Detection Using Machine Learning on the Edge

Falling is one of the primary causes of firefighter casualties. Therefore, early fall detection, followed by reliable and timely notification, is vital to ensure the survival of firefighters. However, due to complex and diverse firefighting scenarios, current fall detection systems are inadequate, especially in terms of accuracy and real-time applications. Hence, in this study, a wearable pre-impact fall detection system for firefighters is proposed. We also evaluated the feasibility of using machine learning and ensemble learning methods deployed on the edge node to simultaneously improve the real-time performance and accuracy. Moving thresholding method is introduced to overcome the class-imbalanced issue due to fewer samples acquired during the pre-impact phase. Based on 14 firefighters’ data collection, the experimental results revealed that a Decision Tree classifier with optimized parameters (DT-ED4) outperformed other machine learning and ensemble learning methods in a trade-off between processing time and accuracy. The results also showed that our system can detect falls before the impact with an average lead time of 447.9 ms, sensitivity of 95.10%, and specificity of 97.99%. The system’s heterogeneous IoT network architecture facilitated remote monitoring and alerts that enabled the incident commander to execute the rescue mission immediately upon notification. This study enhances the practicability of PIFDS and extends the application range of PIFDS from health-care to firefighting.

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.

Dimension of heterogeneous network architecture formed in emulsion cross‐linking copolymerization

AbstractThe mean‐square radius of gyration Rg2 and the graph diameter D of highly heterogeneous network polymers formed in emulsion vinyl/divinyl copolymerization are investigated to find a linear relationship, Rg2 = a D. The proportionality coefficient, a, is dominated by the cycle (circuit) rank, kc, or the number of intramolecular cross‐links. The magnitude of a is slightly larger than that for the random cross‐linked network polymers, but the functional form of a (kc) is simply proportional to that for the random networks proposed earlier. This relationship makes it possible to determine Rg2 based on D and kc, enabling quick estimation of Rg2 for large network polymers. It is found that the contraction factor g of the present heterogeneous network polymer is larger than that for the homogeneous networks formed through random cross‐linking of polymer chains. The ratio of these two contraction factors is kept constant, irrespective of the magnitude of kc.

Secured gateway based communication in heterogeneous SM –neighbourhood area network in smart grid using FPGA design

Usage of the gateway in smart Grid (SG) makes the Grid more flexible, Making Sure the best interoperability in heterogeneous network architecture. The purpose of this gateway in SG will be to handle two communicating standards. In this paper, an FPGA layout for gateway has been presented that ensures the safety and manages heterogeneous Smart meters (SMs) in the Neighbourhood area of the smart Grid. The proposed work solves the Integration of two distinct communication standard Kinds of the smart meter (SM), PRIME and 6LoWPAN, which can be wired and wireless type Criteria. The integration of the SMs was performed using a multiplexer (2:1) and De-multiplexer (1:2) architecture in the SM to Data Concentrator Unit (DCU), and the hardware is executed through Xilinx RTL Simulation. The two MUX which is a wired SM type. The next input is just 6 LoWPAN (IPv6 Low power. The gateway architecture was created, including a Security algorithm AES-128,to provide data security for your 2 SMs data. Simulations are conducted MR-OFDM under distinct constellation sizes are compared to 6LoW PAN protocol, and the results promise a good performance in terms of hardware utilization, delay, and power. Comparison of this work provides better performance concerning hardware utilization. The Proposed architecture introduces a delay of the five ns time delay for its Procedure. The system mentioned above description is verified in the target device xc5vlx330-2ff1760 in the Xilinx ISE14.2. Hence the suggested gateway architecture is proved as an acceptable design for heterogeneous SMs to DCU communication in SG.

Self-Evolving Integrated Vertical Heterogeneous Networks

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight into network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.

A relation-aware heterogeneous graph convolutional network for relationship prediction

Most real-world networks are heterogeneous and consist of different types of nodes and edges. Relationships (edges) between nodes of different types carry different semantics, hence relationship prediction in heterogeneous networks aims to predict the existence of a relation between nodes of different types. In this paper, we propose a relation-aware Heterogeneous Graph Convolutional Network architecture for temporal heterogeneous network relationship prediction. In particular, we first present a continuous-time temporal heterogeneous neighbour generation algorithm to capture the continuous-time interactions in the context of a specific node type. The algorithm collects temporal heterogeneous neighbours with K-hops of a node at a recent temporal distance. Moreover, it adds a decaying exponential to ensure that the fewer hops and the closer temporal distances, the more nodes are sampled. Thus, it can capture the evolutionary pattern of interactions in the recent past and collect strongly correlated heterogeneous neighbours for each node. Then the proposed relation-aware Heterogeneous Graph Convolutional Network is used to learn the most relevant relationship to the target relationship to model the heterogeneous graph neural network for predicting target relationships. Our experimental results on three real-world temporal heterogeneous networks indicate significant advances in prediction accuracy and efficiency compared to the state-of-art approaches.

Energy Optimization With Multi-Sleeping Control in 5G Heterogeneous Networks Using Reinforcement Learning

The massive deployment of small cells in 5G networks represents an alternative to meet the ever increasing mobile data traffic and to provide very-high throughout by bringing the users closer to the Base Stations (BSs). This large increase in the number of network elements demands a significant increase in the energy consumption and carbon footprint followed by complex interference management. In order to address these challenges, we consider multi-level Sleep Mode (SM) where BS components with similar activation/deactivation times can be put to sleep. The deeper and higher energy efficient the SM is, the longer it will take the BS to activate, which might impose degradation in the Quality of Service (QoS). While this adds operational flexibility to the BS, it brings complex management to the operator. In this paper, we consider a heterogeneous network architecture where small cells can switch to different SM levels to save energy and reduce dropping rate. We propose a reinforcement learning algorithm for small cells that adapts their activities subject to service delay constraint. In this regard, the algorithm intelligently learns from the environment based on the co-channel interference, the cell buffer size and the expected cell throughput in order to decide the best SM policy. Numerical values show that important energy savings can be obtained with an acceptable dropping rate. Moreover, we show that while offloading users to the macro cell can significantly reduce their delay, dropping rate and the cluster energy consumption, it comes at a cost of decreasing the network energy efficiency up to 5 times compared with the case of no offload.

Nanoscale TEM Imaging of Hydrogel Network Architecture.

In this work, the authors succeed in direct visualization of the network structure of synthetic hydrogels with transmission electron microscopy (TEM) by developing a novel staining and network fixation method. Such a direct visualization is not carried out because sample preparation and obtaining sufficient contrast are challenging for these soft materials. TEM images reveal robust heterogeneous network architectures at mesh size scale and defects at micro-scale. TEM images also reveal the presence of abundant dangling chains on the surface of the hydrogel network. The real space structural information provides a comprehensive perspective that links bulk properties with a nanoscale network structure, including fracture, adhesion, sliding friction, and lubrication. The presented method has the potential to advance the field.

Autonomous Collaborative Authentication with Privacy Preservation in 6G: From Homogeneity to Heterogeneity

The emerging collaborative authentication schemes are capable of outperforming the conventional isolated methods as a benefit of their multi-dimensional data/information gleaned, but they face new challenges in the sixth generation (6G) wireless networks owing to their increased overhead, limited flexibility and autonomy. Moreover, they may also be vulnerable to the privacy leakage of individual entities. These challenges are mainly due to the complex heterogeneous network architecture, owing to the distributed nature of the devices and information involved as well as the diverse security requirements of the 6G-aided vertical systems. As a remedy, we introduce autonomous collaborative authentication for achieving security enhancement through the situation-aware cooperation of different security mechanisms, of heterogeneous security information/context, and of heterogeneous devices and networks. For this purpose, a federated learning-based collaborative authentication scheme capable of privacy-preservation is developed, where cooperative peers observe and locally analyze heterogeneous information of the authenticating device, and afterwards update their authentication models locally. By sharing their authentication models rather than directly sharing the observed authentication information, privacy preservation can be achieved based on the proposed scheme. Moreover, given the time-varying heterogeneous network environment and the wide range of quality-of-service (QoS) requirements, the membership of the group collaborating in support of distributed authentication is updated based on the situation-dependent conditions. To further reduce the communication overhead, a locally collaborative learning process is further developed, where both the updated parameters and observed authentication information are stored and processed locally at the cooperative peers. Finally, a smart contract is designed for achieving collaborative security combined with privacy preservation and for providing accountable services.

Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing.

Multiservice cellular in Radio Access Network (RAN) Slicing has recently attained huge interest in enhancing isolation and flexibility. However, RAN slicing in heterogeneous networks (HetNet) architecture is not adequately explored. This study proposes a pairing-network slicing (NS) approach for Multiservice RAN that cares about quality of service (QoS), baseband resources, capacities of wireless fronthaul and backhaul links, and isolation. This intriguing approach helps address the increased need for mobile network traffic produced by a range of devices with various QoS requirements, including improved dependability, ultra-reliability low-latency communications (uRLLC), and enhanced broadband Mobile Services (eMBB). Our study displays a unique RAN slicing framework for user equipment (UE) for joint user-association. Multicell non-orthogonal multiple access (NOMA)-based resource allocation across 5G HetNet under successive interference cancelation (SIC) is seen to achieve the best performance. Joint user-slice pairing and association are optimization problems to maximize eMBB UE data rates while fulfilling uRLLC latency and reliability criteria. This is accomplished by guaranteeing the inter- and intra-isolation property of slicing to eliminate interferences between eMBB and uRLLC slices. We presented the UE-slice association (U-S. A) algorithm as a one-to-many matching game to create a stable connection between UE and one of the base stations (BSs). Next, we use the UE-slice pairing (U-S. P) algorithm to find stable uRLLC-eMBB pairs that coexist on the same spectrum. Numerical findings and performance analyses of the submitted association and pairing technique show they can all be RAN slicing criteria. We prove that the proposed algorithm optimizes system throughput while decreasing uRLLC latency by associating and pairing every uRLLC user in mini slots.

QoS-based Packet Scheduling Algorithms for Heterogeneous LTE-Advanced Networks: Concepts and a Literature Survey

The number of LTE (Long-Term Evolution) users and their applications has increased significantly in the last decade, which increased the demand on the mobile network. LTE-Advanced (LTE-A) comes with many features that can support this increasing demand. LTE-A supports Heterogeneous Networks (HetNets) deployment, in which it consists of a mix of macro-cells, remote radio heads, and low power nodes such as Pico-cells, and Femto-cells. Embedding this mix of base-stations in a macro-cellular network allows for achieving significant gains in coverage, throughput and system capacity compared to the use of macrocells only. These base-stations can operate on the same wireless channel as the macro-cellular network, which will provide higher spatial reuse via cell splitting. Also, it allows network operators to support higher data traffic by offloading it to smaller cells, such as Femto-cells. Hence, it enables network operators to provide their growing number of users with the required Quality of Service (QoS) that meets with their service demands. In-order for the network operators to make the best out of the heterogeneous LTE-A network, they need to use QoS-based packet scheduling algorithms that can efficiently manage the spectrum resources in the HetNets deployment. In this paper, we survey Quality of Service (QoS) based packet scheduling algorithms that were proposed in the literature for the use of packet scheduling in Heterogeneous LTE-A Networks. We start by explaining the concepts of QoS in LTE, heterogeneous LTE-A networks, and how traffic is classified within a packet scheduling architecture for heterogeneous LTE-A networks. Then, by summarising the proposed QoS-based packet scheduling algorithms in the literature for Heterogeneous LTE-A Networks, and for Femtocells LTE-A Networks. And finally, we provide some concluding remarks in the last section.

Quality enhancement in a mm-wave multi-hop, multi-tier heterogeneous 5G network architecture

Millimetre-wave ultra-dense high capacity networks are an important component of future 5G and 6G cellular systems since they are providing extremely high network capacity and speed to the end users. However, disparate types of users coexist in such scenarios, which can make the heterogeneous network unfair in terms of allocation of resources to various users based on their requirements. Therefore, a mechanism is required for effective spectrum sharing and to achieve overall system fairness. In this paper, an analytical model is suggested, which is based on a two-dimensional Markov state-transition diagram, to help set the parameter values to control the issuance of resources in coexistence layouts. A restriction approach is further implemented to gain a fair balance of the Grade-of-Service (GoS) for both user groups using the User Admission Control (UAC) mechanism. The developed mechanism restricts access to various channel resources for users with complete choice to give a greater probability of access to different users with limited resource options. Various levels of restriction are investigated in order to offer a balanced low-blocking probability performance to both user groups in order to improve the overall network fairness. Also, the proposed approach could provide a precise level of Grade-of-Service guarantee for both the user groups if sufficient flexibility is available within the whole network. Our simulations results along with the analytical model demonstrate that approximately 30% to 45% enhancement, in terms of grade of service (GoS), could be achieved in high to medium loads by restricting some of the users’ flexibility. From the analytical model, it is concluded that the blocking of shadowed users are significantly reduced from 7% to 4.5% at high traffic loads. Moreover, the obtained results and findings are verified using a number of case studies and large-scale simulations.

Reliability Enhancement for VR Delivery in Mobile-Edge Empowered Dual-Connectivity Sub-6 GHz and mmWave HetNets

The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz networks. In this paper, we propose a dual-connectivity sub-6 GHz and mmWave heterogeneous network architecture empowered by mobile edge capability. The core idea of the proposed architecture is to utilize the complementary advantages of sub-6 GHz links and mmWave links to conduct a collaborative edge resource design, which aims to improve the reliability of VR delivery. From the perspective of stochastic geometry, we analyze the reliability of VR delivery and theoretically demonstrate that sub-6 GHz links can be used to enhance the reliability of VR delivery despite the large mmWave bandwidth. Based on our analytical work, we formulate a joint caching and computing optimization problem with the goal to maximize the reliability of VR delivery. By analyzing the coupling caching and computing strategies at HMDs, sub-6 GHz and mmWave base stations (BSs), we further transform the problem into a multiple-choice multi-dimension knapsack problem. A best-first branch and bound algorithm and a difference of convex programming algorithm are proposed to obtain the optimal and sub-optimal solution, respectively. Numerical simulations demonstrate the performance improvement using the proposed algorithms, and reveal that caching more monocular videos at sub-6 GHz BSs and more stereoscopic videos at mmWave BSs can improve the VR delivery reliability efficiently.

Measurement of the Spectral Efficiency of a Heterogeneous Network Architecture of the NG-PON Type for a Quasilinear Propagation Regime.

The objective of future optical fiber networks is to provide an efficient infrastructure capable of supporting an increasing and variable number of data traffic generated by the diversification of applications with different speed requirements that the current legacy Line Speed networks Single Line Rate (SLR), with predefined modulation formats, cannot supply, because they do not offer enough flexibility to meet the requirements of the demands with such a wide range of granularities. Therefore, next-generation optical networks will be highly heterogeneous in nature, incorporating mixed modulation formats and Mixed Line Rates (MLR). In this work, an analysis of the measurement of the spectral efficiency of a heterogeneous network architecture of the next-generation passive optical network (NG-PON) type is reported for a quasilinear propagation regime through the use of the equation adapted from Shannon’s information theory and developed by the group from the GNTT Research of the University of Cauca, where it was found that it is better to transmit channels of 10 Gbps and 40 Gbps with robust modulations in MLR networks to make an improvement in the spectral efficiency of the network, achieving the same amount of information in a smaller bandwidth or more information in the same bandwidth.