Future Heterogeneous Networks Research Articles

Joint resource allocation for dynamic cellular‐enabled UAVs communication

Emerging cellular-enabled unmanned aerial vehicles (UAVs) communication poses both opportunities and challenges to the current research of UAV applications. On the one hand, the advanced cellular technologies and authentication mechanisms make significant performance improvements of reliability, security, coverage, and throughput of UAVs possible. On the other hand, the considerably dynamic characteristics of the new aerial users bring some complicated and open issues to the future heterogeneous network. To throw some light on this field, the authors study the joint power allocation and channel reuse problem of uplink transmission in a cellular-enabled UAVs network with full consideration of the rapid channel variations resulting from high mobility of UAVs. Given the diverse requirements of terrestrial cellular users (CUs) and UAVs, i.e. large capacity for CU links and ultra-reliability for UAV links, they formulate the problem as optimising the uplink capacities of CUs with reliable transmission constraint of UAVs. By decoupling the intricate problem into two sub-problems, a joint resource allocation algorithm is proposed, which relies only on the statistical information of dynamic channels to implement, hence is robust. Finally, numerical results are provided to corroborate the anticipated performances of the proposed scheme.

Research on Energy Internet Power Line Communication Technology Based on Block Chain

With the continuous development of energy Internet technology, its requirements for communication systems are getting higher and higher. As for the energy Internet, it is based on the concept of the Internet and the existing smart grid. Its communication system also needs to rely on the existing power communication system. In the energy Internet, the use of block chain and intelligent contract technology to meet credible and automatic point-to-point energy transactions can improve the overall operating efficiency of the system, reduce management costs, and make the energy Internet a self-organizing and self-regulating structure. In essence, energy Internet and block chain technology must be built on the Internet of Things, a common intelligent device. This paper takes the power line communication technology of the Internet in the current power system as the research object, analyzes the main challenges and problems in the construction of energy Internet based on blockchain, and then provides the corresponding research basis for the future heterogeneous network integration.

Real-Time Software-Reconfigurable Hybrid In-House Access With OFDM-NOMA

In this paper, a real-time software-reconfigurable orthogonal frequency division multiplexing based non-orthogonal multiple access (OFDM-NOMA) scheme with dynamic power allocation is proposed and experimentally demonstrated for hybrid in-house access over single-mode fiber (SMF) and visible light communication (VLC). Such hybrid access can provide both wired and wireless transmission, which can accommodate the increasingly heterogeneous in-home network and realize seamless coverage. The fiber link is used to provide high-speed and stable transmission for a wired user whilst the VLC link can support flexible and secured wireless access. With the help of power-domain multiplexing based NOMA, the power ratio between both the fiber user and the VLC user can be adjusted in a software-reconfigurable manner, which can satisfy various user demands and also ensure user fairness. Experimental results validate that an aggregate data rate of 1.84 Gbit/s can be achieved in a 1-GSa/s hybrid access system. In addition, it is demonstrated that the power ratio between the fiber user and the VLC user can be adjusted in real-time, facilitating the optimal overall system bit error rate (BER) performance and hence the balanced user fairness. The proposed real-time software-reconfigurable dynamic power allocation scheme for OFDM-NOMA is a promising scheme for the future heterogeneous in-house network, as it allows the flexible power allocation according to users’ demands and enables better user fairness.

Enhancing the Credibility of the Optical Performance Monitor With Adversarial Training

The existing optical performance monitoring (OPM) scheme based on deep neural network has no selection capability of the input data. They always accept and process all, which may result in serious monitoring errors and reduce the credibility of the monitoring system. Because the transmitted data in the future heterogeneous fiber-optic networks are diverse, and it's likely to exceed the scope of the monitoring system. We propose an unsupervised generative adversarial network (GAN) as the judgement module in the new OPM framework to select the legal data within the scope of the monitoring system. The generator consists of encoder-decoder-encoder (EDE) sub-network, jointly learns the image and latent feature distribution of the legal data. And the training data for the network in the new added judgement module is the same as the OPM analyzer network's, therefore, no extra data are collected, which is low-cost. In the simulation, four modulation formats under two bit-rates are taken into account to verify the model performance in the judgement module. When 60 Gbps 64QAM signal is selected as illegal data, the max value of the area under the curve (AUC) is 0.942. The judgement time for single image is about 12 ms. Moreover, the influence of the task weights and the latent feature shape on the judgement performance are investigated. The new added judgement module largely increases the credibility and safety of the existing OPM scheme.

Two-Level Game Based Spectrum Allocation Scheme for Multi-Flow Carrier Aggregation Technique

Nowadays, the number of mobile devices have increased rapidly, and require different amounts of spectrum with the limited carrier resource. As an emerging technique, multi-flow carrier aggregation (MCA) has been recently proposed to improve the capacity of 5G heterogeneous network systems. In this paper, we present an effective MCA control scheme for the future heterogeneous network (HetNet). With the MCA-enabled HetNet, we aim at maximizing the system throughput while considering each mobile device's benefit. To strike an appropriate performance balance between network operators and mobile users, our proposed scheme is designed as a two-level game model. The upper and lower level games are developed as different type game models, and work together interactively to reach a fair-efficient spectrum allocation solution. Our hierarchical game approach can achieve reciprocal advantages, and clarifies what is the best solution for the MCA-enabled system. Using extensive simulations, we compare the performance and confirm the superiority of our game-based approach. Finally, the conclusions are presented, and some research challenges are identified for the future study.

Heterogeneous Network Bandwidth Control Scheme for the Hybrid OMA-NOMA System Platform

The emerging non-orthogonal multiple access (NOMA) has been considered as a promising technology to reach the goals of 5G cellular systems. Due to its superior spectral efficiency, the NOMA has attracted a lot of attention recently, and could play a vital role in improving the capacity of future Heterogeneous networks (HetNets). In this paper, we present a joint bandwidth control scheme, which incorporates both NOMA and orthogonal multiple access (OMA) modes into one unified scheme. For the effective collaboration of different multiple access methods, we adopt the ideas of cooperative bargaining solution concepts. In the proposed scheme, the bandwidth allocation process for base stations and channels follows the OMA mode, and mobile devices within a same channel form a group. The power level selection process for mobile devices in each group follows the NOMA mode. In the OMA mode, traditional Nash bargaining solution is adopted to allocate bandwidth resources. In the NOMA mode, groups of individual devices bargain both within and across groups based on the group bargaining solution. Under the dynamic changing HetNet environments, our joint bargaining approach takes various benefits in a rational way while maximizing the bandwidth efficiency. Comprehensive numerical experiments are provided to show that the performance of our proposed scheme is superior to existing HetNet spectrum control protocols.

Edge on Wheels With OMNIBUS Networking for 6G Technology

In recent years, both the scientific community and the industry have focused on moving computational resources with remote data centres from the centralized cloud to decentralised computing, making them closer to the source or the so called “edge” of the network. This is due to the fact that the cloud system alone cannot sufficiently support the huge demands of future networks with the massive growth of new, time-critical applications such as self-driving vehicles, Augmented Reality/Virtual Reality techniques, advanced robotics and critical remote control of smart Internet-of-Things applications. While decentralised edge computing will form the backbone of future heterogeneous networks, it still remains at its infancy stage. Currently, there is no comprehensive platform. In this article, we propose a novel decentralised edge architecture, a solution called OMNIBUS, which enables a continuous distribution of computational capacity for end-devices in different localities by exploiting moving vehicles as storage and computation resources. Scalability and adaptability are the main features that differentiate the proposed solution from existing edge computing models. The proposed solution has the potential to scale infinitely, which will lead to a significant increase in network speed. The OMNIBUS solution rests on developing two predictive models: (i) to learn timing and direction of vehicular movements to ascertain computational capacity for a given locale, and (ii) to introduce a theoretical framework for sequential to parallel conversion in learning, optimisation and caching under contingent circumstances due to vehicles in motion.

IEEE Access Special Section Editorial: New Waveform Design and Air-Interface for Future Heterogeneous Network Towards 5G

Unprecedented levels of spectral and energy efficiency are expected from next-generation wireless networks to achieve ubiquitous communications between anybody, anything, and at any time. Fifth-generation (5G) radio access technology is expected to take a huge leap compared to the previous radio generations by supporting cognitive radio, machine type communication, and the Internet of Things, besides traditional mobile broadband access. The necessity of supporting new services targeted by 5G has made researchers to question the suitability of the orthogonal frequency-division multiplexing (OFDM) waveform as adopted in Long-Term Evolution (LTE). However, the demerits of OFDM are recognized to be its large out-of-band emissions, which affect the coexistence of asynchronous services or devices, as well as its sensitivity to phase noise, Doppler frequency shift, and large peak-to-average power ratio (PAPR). Significant research effort is currently being spent on the design and analysis of alternative solutions aimed at overcoming the inefficiencies of present waveforms. Candidate multicarrier solutions rely on different degrees of filtering to obtain lower sidelobes than OFDM, thus improving robustness to asynchronous transmission. For example, in filter bank multicarrier (FBMC) approaches, universal filter multicarrier (UFMC), and generalized frequency-division multiplexing (GFDM). Even though the mentioned solutions offer undeniable advantages in terms of spectrum containment, their suitability as 5G waveforms when considering complexity constraints, as well as realistic impairments, is still disputable. In particular, the extension to multiple-input multiple-output (MIMO) antenna transmission with multiple streams seems rather cumbersome. Thus, this will lay down an interdisciplinary research agenda that combines energy and time-efficient spectrum schemes into waveform design.

Loss weight adaptive multi-task learning based optical performance monitor for multiple parameters estimation.

A loss weight adaptive multi-task learning based artificial neural network (MTL-ANN) is applied for joint optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI). We conduct an experiment of polarization division multiplexing (PDM) coherent optical system with 5 km standard single mode fiber (SSMF) transmission to verify this monitor. A group of modulation schemes including nine modulation adaptive M-QAM formats are selected as the transmission signals. Instead of circular constellation, signals' amplitude histograms after constant module algorithm (CMA) based polarization de-multiplexing are selected as input features for our proposed monitor. The experimental results show that the MFI accuracy reaches 100% in the estimated OSNR range. Furthermore, when treated as regression problem and classification problem, OSNR estimation with a root mean-square error (RMSE) of 0.68 dB and an accuracy of 98.7% are achieved, respectively. Unlike loss weight fixed MTL-ANN, loss weight adaptive MTL-ANN could search the optimal loss weight ratio automatically for different link configurations. Besides that, the number of estimated parameters can be easily expanded, which is attractive for multiple parameters estimation in future heterogeneous optical networks.

Power Allocation and User Assignment Scheme for beyond 5G Heterogeneous Networks

The issue of spectrum scarcity in wireless networks is becoming prominent and critical with each passing year. Although several promising solutions have been proposed to provide a solution to spectrum scarcity, most of them have many associated tradeoffs. In this context, one of the emerging ideas relates to the utilization of cognitive radios (CR) for future heterogeneous networks (HetNets). This paper provides a marriage of two promising candidates (i.e., CR and HetNets) for beyond fifth generation (5G) wireless networks. More specifically, a joint power allocation and user assignment solution for the multiuser underlay CR-based HetNets has been proposed and evaluated. To counter the limiting factors in these networks, the individual power of transmitting nodes and interference temperature protection constraints of the primary networks have been considered. An efficient solution is designed from the dual decomposition approach, where the optimal user assignment is obtained for the optimized power allocation at each node. The simulation results validate the superiority of the proposed optimization scheme against conventional baseline techniques.

TCO Planning Game for 5G Multitenant Virtualized Mobile BackHaul (V-MBH) Network

Raising density and ever-increasing traffic demand within future 5G Heterogeneous Networks (HetNets) will result in huge deployment, expansion and operating costs for upcoming Mobile Backhaul (MBH) networks. Multitenancy and network slicing based on virtualized resources are promising solutions to satisfy MBH network greediness while reducing related expenditures. Nevertheless, there is no appropriate model that fairly distributes costs over multiple virtual operators, and also optimizes physical resource planning. In this article, we introduce a new model of 5G multitenant MBH costs (CapEx and OpEx). Then, we drive a novel pay-as-you-grow and optimization model called Virtual-Backhaul-as-a-Service (VBaaS) as a planning tool optimizing the Project Profit Margin (PPM) while considering the Total-Cost-of-Ownership (TCO) and the yearly generated Return-on-Investment (ROI). We also formulate an MNO pricing game (MPG) for TCO optimization to calculate the optimal Pareto-Equilibrium pricing strategy for offered Tenant Service Instances (TSI). Finally, we compare CapEx, OpEx, TCO, ROI and PPM for a specific use-case known in the industry as CORD project using Traditional MBH (T-MBH) versus Virtualized MBH (V-MBH) as well as using randomized versus Pareto-Equilibrium pricing strategies. Numerical results show more than three times increase in network profitability using our proposed solutions compared with Traditional MBH (T-MBH).

Application Research of Multi-Mode Relay in Future Heterogeneous Networks

The fast increase of users in existing mobile networks requires more base stations (BSs) to bear more communication traffic. Future heterogeneous network is considered to be a promising candidate architecture to meet the demands of wireless networks under scarcity of radio frequency (RF) resources. In this paper, we present a multi-mode relay (MMR) model based on two-way relay technology, which is applied to heterogeneous hierarchical wireless networks (HHWN), and set up a system model of HHWN with 3 tiers, 2 users between the macrocell, and the picocell as the multi-mode relay (MMR). Specifically, we consider the new system with unequal relay emission power situation, which is usually researched in the traditional literature with equal relay emission powers. Based on this idea, we define the two-way SINR ratio, derive the mathematical formulas of outage error probability with channel estimation errors, and verify theoretical expressions by data simulations. For further comparison, several experiments are implemented to illuminate the effect on outage probability among different levels of relay emission power, noise power, and signal power. Furthermore, several conclusions are obtained, which have some meanings for implementing MMR in future heterogeneous networks.

Adaptive Traffic Management in Heterogeneous Wireless Networks

The adoption of wireless technologies is due to user requirement, small size of devices with multiple networking interfaces. The current issues which are perform major role of mobility, reassembly, routing, and security. Consistent mobility is one of the main challenges in the existing and future heterogeneous wireless networks. Consistent mobility in heterogeneous wireless network needs integration of different network technologies. Most of currently available communication and computing systems uses more system time for completing the job. Processing power and time limit for processing a task/query, a system is required that offers device mobility (BS mobility) and computational mobility as per the requirement of the applications. We have evaluated the MAC-SF for network selection, traffic management, load balancing, and network delay.

Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives.

Narrowband internet of things (NB-IoT) is a recent cellular radio access technology based on Long-Term Evolution (LTE) introduced by Third-Generation Partnership Project (3GPP) for Low-Power Wide-Area Networks (LPWAN). The main aim of NB-IoT is to support massive machine-type communication (mMTC) and enable low-power, low-cost, and low-data-rate communication. NB-IoT is based on LTE design with some changes to meet the mMTC requirements. For example, in the physical (PHY) layer only single-antenna and low-order modulations are supported, and in the Medium Access Control (MAC) layers only one physical resource block is allocated for resource scheduling. The aim of this survey is to provide a comprehensive overview of the design changes brought in the NB-IoT standardization along with the detailed research developments from the perspectives of Physical and MAC layers. The survey also includes an overview of Evolved Packet Core (EPC) changes to support the Service Capability Exposure Function (SCEF) to manage both IP and non-IP data packets through Control Plane (CP) and User Plane (UP), the possible deployment scenarios of NB-IoT in future Heterogeneous Wireless Networks (HetNet). Finally, existing and emerging research challenges in this direction are presented to motivate future research activities.

A Study of the Effects on QoS in WiFi-Cellular Offloading Scenarios

Nowadays the mobile data usage has been significantly increased by an unprecedented amount with the wide spread of smart devices, which is known as the explosion of data traffic. The rapid growth in mobile data traffic leads to a deficiency of cellular network capacity. To solve this problem, readily available Wi-Fi networks are used to offload the data traffic from cellular networks. The Wi-Fi offloading must ensure guaranteed throughput and delay performance for the users. However, if the user doesn’t meet any Wi-Fi network during the download period, the quality of experience gets degraded. Quality of experience can be improved with the help of various techniques such as resource allocation, scheduling, and handoff schemes. To know the effect of the offloading process, some key parameters are identified in this paper and the effect of offloading on these parameters is studied. Here, in this paper a study of various parameters like download time, number of users, data size on the throughput, delay and packet loss is done in the cellular network -WiFi offloading scenarios. This study highlights the need for an efficient QoS mechanism in future heterogeneous networks. It can be considered as a research aspect in upcoming integrated networks.

Intelligent optical performance monitor using multi-task learning based artificial neural network.

An intelligent optical performance monitor using multi-task learning based artificial neural network (MTL-ANN) is designed for simultaneous OSNR monitoring and modulation format identification (MFI). Signals' amplitude histograms (AHs) after constant module algorithm are selected as the input features for MTL-ANN. The results obtained from simulation and experiment of NRZ-OOK, PAM4 and PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI simultaneously with higher accuracy and stability compared with single-task learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% for the three modulation formats under consideration. Furthermore, OSNR monitoring with mean-square error (MSE) of 0.12 dB and accuracy of 100% is achieved while regarding it as regression problem and classification problem, respectively. In this intelligent optical performance monitor, only a single MTL-ANN is deployed, which enables reduced-complexity optical performance monitor (OPM) devices for multi-parameters estimation in future heterogeneous optical network.

An Adaptive Coverage Enhancement Scheme Based on mmWave RoF for Future HetNets

For future heterogeneous networks, network coverage is identified as one of the main challenges. This paper focuses on the spectrum sharing and energy consumption problems with millimeter wave radio over fiber (mmWave RoF) utilized to enhance the coverage of the system. The proposed adaptive scheme includes the following steps. First, the system identifies the users as licensed users that primarily own the specific spectrum and unlicensed users that have to purchase the spectrum. Based on the game theory, the spectrum resource is shared among the eNB and low power nodes (LPNs) with a relatively high benefit. Second, an adaptive sleep schedule scheme is performed to reduce the power cost of some LPNs that are scored under the threshold. Third, an adaptive power allocation scheme is utilized to further reduce the power assumption of the system and does not affect the system demand satisfaction. The simulation results finally show that the proposed resource allocation scheme can achieve improved coverage of the network.

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.

Joint Optical Performance Monitoring and Modulation Format/Bit-Rate Identification by CNN-Based Multi-Task Learning

A novel technique is proposed for joint multi-impairment optical performance monitoring (OPM) with bit-rate and modulation format identification (BR-MFI) in next-generation heterogeneous optic communication networks by convolution neural network (CNN)-based deep multi-task learning (MTL) on asynchronous delay-tap sampling phase portraits. Instead of treating the monitoring and identification tasks as separate problems, a novel MTL technique is used to joint optimization of them utilizing the ability of feature extraction and feature sharing. Compared with principal component analysis-based pattern recognition algorithm, CNN-based MTL achieves the better accuracies and has a shorter processing time (∼56 ms). The combination signals of three modulation formats and two bit rates under various impairments are used in numerical simulation. For OPM, the results show monitoring of optical signal-to-noise ratio, chromatic dispersion, and differential group delay with root-mean-square error of 0.73 dB, 1.34 ps/nm, and 0.47 ps, respectively. Similarly, for BR-MFI, even in the case of limited training data, 100% accuracies can be achieved. Additionally, the effects of training data size, task weights, and model structure on CNN-based MTL performance are comprehensively studied. The proposed technique can intelligently analyze the signals of future heterogeneous optic communication networks, and the analysis results are helpful for better management of optical networks.

Optimization of Resource Mining in Distributive Sharing (ORMDS) Scheme for Future Heterogeneous Network

Optimization of Resource Mining in Distributive Sharing (ORMDS) Scheme for Future Heterogeneous Network