Heterogeneous Wireless Access Networks Research Articles

Model order estimation method for user mobility classification in heterogeneous RAN systems

The performance of modern heterogeneous Radio Access Networks (RANs) can be improved by introducing new methods and algorithms for network resource management. In particular, user mobility information can be used to control RAN resources. To determine user mobility, information about the variation of the Signal to Interference + Noise Ratio (SINR) between base stations and users can be collected and analyzed. These data are multi-linear and can be represented as tensors to extract information from them. However, for the tensors decomposition, it is necessary to know the order of the multi-linear model or the rank of the tensors. Inspired by the LaRGE (LineAr Regression of Global Eigenvalues) method, we propose a novel method for the model order estimation of the multi-linear data based on the analysis of the Complimented Global Eigenvalues (CGE) profile. We have denoted our method as Enhanced LaRGE (EnLaRGE). We have compared the EnLaRGE method with the classical methods AIC, MDL and their multi-linear versions, as well as with the M-EFT and N-D EFT methods. EnLaRGE shows good performance for the tensors with small one of the tensors dimensions. EnLaRGE method is implemented for the automatic model order estimation of multi-linear data received from the Network Simulator 3 with RAN Intelligent Controller (RIC). The results show the high precision of the multi-linear model order estimation in comparison with state-of-the-art methods. The estimated model order can be used for user mobility classification and improving heterogeneous RANs performance. This research was funded by Russian Science Foundation, grant number 23-69-10084, 318, https://rscf.ru/project/23-69-10084/

Heterogeneous RAN slicing resource allocation using mathematical program with equilibrium constraints

Network slicing is considered to be a key feature of the 5th generation mobile networks. It permits multiple tenants, i.e. mobile virtual network operators, to share virtual resources. However, each tenant only considers the individual slice utility, which leads to unfair resource allocation among tenants. To achieve the aim that the infrastructure provider can fairly allocate virtual resources to tenants, a two-layer resource allocation architecture in a heterogeneous radio access network (RAN) is proposed and it is formulated as a mathematical program with equilibrium constraints (MPEC). The existence of the solution in the lower layer is proved via the properties of the quasi-variational problem, indicating that the MPEC is solvable. Combining the two-layer architecture and successive convex approximation method, a fair algorithm is proposed, which provides fair resource allocation strategies for the infrastructure provider. Compared with the existing static slicing and social optimal methods, the analysis and simulation results confirm that the proposed algorithm weighs the utilities of the total network system and each tenant. In addition, regarding their utilities, the gap between the proposed method and the social optimal is within 5%, which outperforms static slicing.

Energy Efficient User Association, Resource Allocation and Caching Deployment in Fog Radio Access Networks

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness.

Modelling and Analysis of Performance Characteristics in a 60 Ghz 802.11ad Wireless Mesh Backhaul Network for an Urban 5G Deployment

With the widespread deployment of 5G gaining pace, there is increasing interest in deploying this technology beyond traditional Mobile Network Operators (MNO) into private and community scenarios. These deployments leverage the flexibility of 5G itself to support private networks that sit alongside or even on top of existing public 5G. By utilizing a range of virtualisation and slicing techniques in the 5G Core (5GC) and heterogeneous Radio Access Networks (RAN) at the edge, a wide variety of use cases can be supported by 5G. However, these non-typical deployments may experience different performance characteristics as they adapt to their specific scenario. In this paper we present the results of our work to model and predict the performance of millimeter wave (mmWave) backhaul links that were deployed as part of the Liverpool 5G network. Based on the properties of the 802.11ad protocol and the physical characteristics of the environment, we simulate how each link will perform with different signal-to-noise ratio (SNR) and Packet Error Rate (PER) values and verify them against real-world deployed links. Our results show good convergence between simulated and real results and provide a solid foundation for further network planning and optimization.

An adaptive algorithm for performance enhancement of long-haul MMW/RoF system for next generation mobile communications

Radio over fibre (RoF) and millimetre wave (MMW) are regarded as the most promising solutions to provide high-capacity, long-distance data transport between base stations and core network in the future heterogeneous radio access networks that support both cloud and edge computing paradigms. In this paper, we propose an adaptive algorithm to optimise and determine a set of important link parameters for a MMW/RoF integrated system based on 10 Gb/s QPSK modulation format and heterodyne coherent detection. We develop analytical models of main components and derive a closed-form expression of system performance such as Q, SNR and BER. The adaptive algorithm is successfully applied to determine important link parameters including laser power, local oscillator power, optical amplifier location and gains so that the overall performance could be enhanced at an acceptable level corresponding to the varied propagation length from 200 km to 300 km. BERs of well below the 10-9.

Smart Real-Time Recommendation of Mobile Services

In this paper, a new vision is presented for highly personalized, customized, and contextualized real-time recommendation of services to mobile users (consumers) by considering the current consumer-, network-, and service context. A smart service recommendation system is elaborated, which builds up and dynamically manages personal profiles of consumers, aiming to facilitate and optimize the service discovery and recommendation process, in support of consumers’ choices, thereby achieving the best quality of experience (QoE) as perceived by those consumers when utilizing different mobile services. The algorithm-driven recommended mobile services, accessible anytime-anywhere-anyhow through any kind of mobile devices via heterogeneous wireless access networks, range from typical telecommunication services (e.g., outgoing voice calls) to Internet services (e.g., multimedia streaming). These algorithms also may be further enriched by their being adapted and expanded to cover more sophisticated services such as helping the consumer’s health and security needs, an example being the finding (with subsequent dynamic changing, if required) of the most 'healthy' or 'secure' driving/biking/jogging/walking route to follow so as to avoid areas posing particular, consumer-specific, health or safety risk.

Energy-Efficient Multi-Access Mobile Edge Computing With Secrecy Provisioning

Thanks to the wide deployment of heterogeneous radio access networks (RANs) in the past decades, the emerging paradigm of multi-access mobile edge computing, which allows mobile terminals to simultaneously offload the computation-workloads to several different edge-computing servers via multi-RANs, has provided a promising scheme for enabling the computation-intensive mobile Internet services in future wireless systems. The broadcasting nature of radio transmission, however, may lead to a potential secrecy-outage during the offloading transmission. In this paper, we thus investigate the energy-efficient multi-access mobile edge computing with secrecy provisioning. Specifically, we first investigate the scenario of one wireless device's (WD's) multi-access offloading subject to a malicious node's eavesdropping. By characterizing the WD's secrecy based throughput in its offloading transmission, we formulate a joint optimization of the WD's multi-access computation offloading, secrecy provisioning, and offloading-transmission duration, with the objective of minimizing the WD's total energy consumption, while providing a guaranteed secrecy-outage during offloading and a guaranteed overall-latency in completing the WD's workload. Despite the non-convexity of this joint optimization problem, we exploit its layered structure and propose an efficient algorithm for solving it. Based on the study on the single-WD scenario, we further investigate the scenario of multiple WDs, in which a group of WDs sequentially execute the multi-access computation offloading, while subject to a malicious node's eavesdropping. Taking the coupling effect among different WDs into account, we propose a swapping-heuristic based algorithm (that uses our proposed single-WD algorithm as a subroutine) for finding the ordering of the WDs to execute the multi-access computation offloading, with the objective of minimizing all WDs’ total energy consumption. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms. The results demonstrate that our algorithms can outperform some conventional fixed offloading scheduling scheme and randomized offloading ordering scheme.

Hybrid Radio Resource Management for Time-Varying 5G Heterogeneous Wireless Access Network

In this paper, we explore radio resource management for a time-varying 5G heterogeneous wireless access network that includes multi-RATs such as 5G new radio (NR) and long-term evolution (LTE). To cope with the practical challenges of a centralized approach such as signalling overhead and computational complexity, we decomposed the process of radio resource management into three parts, 1) RAT selection, 2) optimal radio resource allocation, and 3) congestion control. RAT selection is performed by each user device with network assistance, whereas the problem of radio resource allocation and congestion control is formulated as a stochastic optimization problem. Maintaining network stability, the average throughput utility is maximized subject to admission control and resource allocation. By using Lyapunov optimization, this utility maximization problem is decomposed into two subproblems. Radio resource allocation policy implemented at the central controller node allocates resources at each time slot using the Lagrange dual method, whereas the process of congestion control is carried out at user end based on throughput adaptation according to its current channel conditions. The theoretical and simulation results evaluate the performance of our proposed approach under the assumption of network stability. Simulation results related to individual users throughput and queue length, and performance comparison of equal power and adaptive power allocation techniques, are presented to depict the effectiveness of our proposed scheme. Furthermore, our proposed RAT selection scheme performs better than the traditional centralized and distributive mechanisms.

Joint Resource Allocation at Edge Cloud Based on Ant Colony Optimization and Genetic Algorithm

Both the radio resources in wireless networks and the computational resources in cloud have big impact on the performance of the mobile cloud computing system. In this paper, we study the joint radio and computational resource allocation in a mobile edge cloud system with a heterogeneous radio access network and a close-by edge cloud. The objective of the proposed resource allocation scheme is to maximize the system utility as well as satisfy the diverse quality requirements for the delay-sensitive and computation-intensive applications of mobile users. The requirements for economic cost reduction and energy conservation are considered in the proposed scheme to achieve the balance between the user-centric and network-centric resource allocation. The proposed scheme takes advantage of both ant colony optimization (ACO) and genetic algorithm (GA) to explore and exploit the search space to obtain the near optimal solution at the lower computational complexity. ACO is applied for generating the initial population, and GA operations such as mapping, crossover, and repair are proposed to improve the search ability and avoid premature convergence through the search of solution in a broader search space. Simulation results show that our proposed scheme outperforms the existing schemes in terms of convergence performance and the accuracy of final results. Moreover, the results demonstrate that it can not only achieve significant system utility improvement, but also achieve higher resource utilization as well as remarkably lower average latency.

Analysis of a Decentralized Mobility Management Support for u-City Wireless Network Infrastructure

The number of mobile devices roaming into a ubiquitous city (u-city) continues to rise as wireless systems have been widely deployed. The number of mobile devices also varies with time, and they tend to frequently change their locations. In addition, the available wireless access networks may belong to different domains, administrations, management, and internet service providers (ISPs). A fusion of overlapping heterogeneous wireless access networks (e.g., WiMax, Wi-Fi, LTE, etc.) serves the u-city in order to reach different coverage. In this context, technical challenges arise in mobility management for heterogeneous networks to realize their potential coverage and capacity benefits. This paper deals with the analysis of a decentralized mobility management support for u-City wireless network infrastructure. This scheme takes advantage of the features of the fully-distributed model of networked-based mobility management protocol to alleviate and realize the ubiquitous requirements of a u-City.

Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks

5G heterogeneous networks have become a promising platform to connect a growing number of Internet-of-Things (IoT) devices and accommodate a wide variety of vertical services. IoT has not been limited to traditional sensing systems since the introduction of 5G, but also includes a range of autonomous moving platforms, e.g., autonomous flying vehicles, autonomous underwater vehicles, autonomous surface vehicles as well as autonomous land vehicles. These platforms can be used as an effective means to connect air, space, ground, and sea mobile networks for providing a wider diversity of Internet services. Deep learning has been widely used to extract useful information from network big data for enhancing network quality-of-service and user quality-of-experience. Privacy preservation for user and network data is a burning concern in 5G heterogeneous networks due to various attacks in this environment. In this paper, we conduct an in-depth investigation on how deep learning can cope with privacy preservation issues in 5G heterogeneous networks, in terms of heterogeneous radio access networks (RANs), beyond-RAN networks, and end-to-end network slices, followed by a set of key research challenges and open issues that aim to guide future research.

An Analytical Hierarchy Process Combined with Game Theory for Interface Selection in 5G Heterogeneous Networks

Network convergence is considered as one of the key solutions to the problem of achieving future high-capacity and reliable communications. This approach overcomes the limitations of separate wireless technologies. Efficient interface selection is one of the most important issues in convergence networks. This paper solves the problem faced by users of selecting the most appropriate interface in the heterogeneous radio-access network (RAN) environment. Our proposed scheme combines a hierarchical evaluation of networks and game theory to solve the network-selection problem. Instead, of considering a fixed weight system while ranking the networks, the proposed scheme considers the service requirements, as well as static and dynamic network attributes. The best network is selected for a particular service request. To establish a hierarchy among the network-evaluation criteria for service requests, an analytical hierarchy process (AHP) is used. To determine the optimum network selection, the network hierarchy is combined with game theory. AHP attains the network hierarchy. The weights of different access networks for a service are calculated. It is performed by combining AHP scores considering user’s experienced static network attributes and dynamic radio parameters. This paper provides a strategic game. In this game, the network scores of service requests for various RANs and the user’s willingness to pay for these services are used to model a network-versus-user game. The Nash equilibria signify those access networks that are chosen by individual user and result maximum payoff. The examples for the interface selection illustrate the effectiveness of the proposed scheme.

A new Walrasian mechanism design for optimal pricing and resource allocation in heterogeneous wireless access networks

In this paper, we address joint spectrum allocation and pricing problem in heterogeneous wireless access networks. In contrast to traditional static spectrum allocation approach, we model the spectrum as a market commodity, dynamically arbitrated by a regulatory agent with the aim of maximizing the social welfare. This is of particular importance with the dominance of wireless technologies in access networks and the sharp increase in the number of overlapping wireless technologies and networks. We designed a novel Walrasian mechanism called economic resource allocation and pricing based on a three-stage game model capable of considering multiple networks and clients with individual attributes and preferences. In the first stage, competition among networks for spectrum allocation is handled where the regulatory evaluates the social welfare and takes corrective action. In the bottom two stages, the complex interactions between clients and networks result in bitrate allocations and service pricings. The equilibrium condition is analyzed using the Walrasian market model which is argued to be the Nash equilibrium of the proposed game. The system behavior is analyzed through extensive simulations. The results confirm the effectiveness of the proposed mechanisms in moving the system towards maximal social welfare.

Алгоритмы выделения канального ресурса в гетерогенной сети радиодоступа нового поколения

The article proposes algorithms for allocating channel resources in a heterogeneous radio access network that simultaneously uses the frequency resources of a 5G network and an unlicensed wireless access network. It is shown that the use of a balancer in such network will make it possible to level the problem of the mutual wireless technologies influence, in particular, interference. To implement the algorithms, it is proposed to use the capabilities of the cloud server architecture.

Technology Support for Smart Computational Grid (SCG) in Future Wireless Systems

The communication grid is an emerging infrastructure that will fundamentally change the way we think – and use – computing. Communication grid is used by analogy with the electric power grid, as an infrastructure that provides pervasive access to electricity. Like the computer and a small number of other advances had a dramatic impact on human capabilities and utilization of the scarce frequency resource, it is believed that allowing all components of our information technology infrastructure – computational capabilities, databases, sensors, and people – to be shared flexibly as true collaborative tools, the Smart Computational Grid (SCG) will have a similar transforming effect, allowing new classes of applications to emerge. Improvement in spectrum usage achieved by CR technology and associated collaborative computations on white spaces. Every spectrum hole includes several channels. Implementing collaborative algorithms over the channels help track changing environment parameters and managed to achieve smooth handoffs, which is maximized to offer services ranging from low-data-rate non-rea-time applications to high-speed real-time multimedia applications. With virtualization, the heterogeneous wireless access networks complement each other to deliver maximal signal strength, wider coverage, efficient data rate, low latency and reduced loss rate. Keywords: computation, detection, grid, sensing, virtualization DOI : 10.7176/CEIS/10-7-04 Publication date: November 30th 2019

Online Distributed User Association for Heterogeneous Radio Access Network.

Future-generation radio access networks (RAN) are projected to fulfill the diverse requirements of user equipment (UE) by adopting a heterogeneous network (HetNet) environment. Necessary integration of different radio access technologies (RAT), such as 2G, 3G, 4G, wireless local area network (WLAN), and visible light communication (VLC) is inevitable. Moreover, UEs equipped with diverse requirements will be capable of accessing some or all the RATs. The complex HetNet environment with diverse requirements of UEs will present many challenges. The HetNet is likely to suffer severely from load imbalance among the base stations (BSs) from inheriting the traditional user association scheme such as max-SINR (signal-to-interference-plus-noise ratio)/max-RSSI (received signal strength indicator), unless some sophisticated schemes are invented. In this paper, a novel scheme is devised for a joint-user association for load balancing, where BSs are densely deployed and UEs typically have a certain degree of mobility. Unlike most of the present works, a dynamic network is considered where the position and channel condition of the UEs are not fixed. We develop two complex and distributed association schemes based on probability and d-choices, while carefully considering both loads of the BSs and SINR experienced by the UEs. Numerical results validate the efficiency of the proposed schemes by showing a received data-rate fairness among UEs and an improvement in the UE’s minimum received data rate.

Energy-Aware Resource Management in Heterogeneous Cellular Networks With Hybrid Energy Sources

In this paper, we focus on reducing the on-grid energy consumption in heterogeneous radio access networks (HetNets) supplied with hybrid power sources (grid and renewables). The energy efficiency problem is analyzed over both short- and long-timescales by means of reactive and proactive management strategies. For short-timescale case, a renewable-energy (RE) aware user equipment-base station association is proposed and analyzed for the cases when no storage infrastructure is available. For long-timescale case, a traffic flow method is proposed for load balancing in RE base stations (BSs), which is combined with a model predictive controller (MPC) to include forecast capabilities of the RE source behavior in order to better exploit a Green HetNet with storage support. The mechanisms are evaluated with data of solar measurements from the region of Valle de Aburra, Medellin, Colombia and wind estimations from the Moscow region, Russian Federation. Results show how the green association proposal can reduce on-grid energy consumption in a HetNet by up to 34%, while is able to exceed the savings obtained by other methods, including the best-signal level policy by up to 15%, additionally providing high network efficiency and low computational complexity. For the long-timescale case, MPC attainable savings can be up to 22% with respect to the on-grid only Macro-BS approach. Finally, an analysis of our proposals in a common scenario is included, which highlights the relevance of storage management, although emphasizing the importance of combining reactive and proactive methods in a common framework to exploit the best of each approach.

Computation Offloading Optimization Based on Probabilistic SFC for Mobile Online Gaming in Heterogeneous Network

To support new services targeted by 5G, great efforts have been taken not only on the research work of new waveform design and air interface but also on cloudification and softwarization for future heterogeneous network. As one of the most popular services toward 5G, cloud gaming offloads computation-intensive tasks to the cloud in order to alleviate the computation burden of mobile devices, but it introduces latency which deteriorates user experience especially for the delay-sensitive online game. In order to solve the optimization problem of resource allocation with the quality of experience guarantees and reduce the operational expenditures and capital expenditures of mobile operators for deploying online game, fog computing and network function virtualization are deemed as promising solutions. In this paper, a component-based approach is proposed to model online game based on the probabilistic service function chain. In order to obtain the optimal virtual function placement in the fog-enabled heterogeneous radio access network, the cost minimization of computation offloading on the data plane is formulated as an integer linear programming problem considering the constraints of application maximum tolerable latency, resource limitation, and user behavior. The optimization problem is NP-hard. To solve the problem with low complexity, the heuristic algorithm is proposed called Probabilistic Service function chain Embedding based on Cost Optimization(PSECO). The performances of the two algorithms are evaluated. The simulation results show that the costs are affected mainly by the number of components, the arrival rate of user requests, mobile user behavior, as well as the physical network topology and the number of users. The heuristic algorithm PSECO has optimal results with low complexity and it is suitable for large scale networks.

Review of network integration techniques for mobile broadband services in next generation network

Next Generation Network (NGN) is intended at integrating the existing heterogeneous wireless access networks in order to produce a composite network that provides users with ubiquitous broadband experience. Currently, it has been established that Long Term Evolution (LTE) network, as a backbone network, provides broadband capacity with high efficiency, reduced latency and improved resource provisioning. Resource provisioning on this backbone network is not without its limitation as more mobile broadband services (MBBs) are evolving and users demand for mobility is on the increase. This paper, therefore, reviewed the different integration techniques for the heterogeneous networks that use LTE network as backbone that supports mobile broadband services. http://dx.doi.org/10.4314/njt.v37i2.24

Design and Implementation of the Service-Aware Traffic Engineering (SATE) in the LISP Software- DefinedWireless Network (LISP-SDWN)

Software Defined Wireless Networks (SDWN) have been considered to have a feasible architecture that enables the fast deployment of new services and solutions in response to the explosion in the number of users and network traffic. Currently, the telecommunications sector is ensuring flexibility in network management and configuration. However, fluctuations in traffic are still beyond the control of SDWN providers. This paper suggests ways to manage fluctuations of traffic with service type. We propose the design of a service-aware network management service that achieves the maximum network utilization among heterogeneous Radio Access Networks (RANs) as a form of Traffic Engineering (TE). In this paper, we implement and test the Service-Aware Traffic Engineering (SATE) that distributes the network traffic to RANs according to the service type of traffic in the network layer. The traffic shift latency (e.g., from an LTE RAN to a Wi-Fi RAN) is considered as a performance metric that does not affect the end-to-end latency of some network applications (e.g., VoIP), and it is 3.51ms from our testbed. Therefore, it might not affect the end-to-end latency of the VoIP application in the telecommunications. SATE is implemented using OpenDaylight (ODL) and Ingress/Egress Tunneling Routers (xTRs) running on Vector Packet Processing (VPP).