5G HetNets Research Articles

Reinforcement learning-based joint self-optimisation method for the fuzzy logic handover algorithm in 5G HetNets

5G heterogeneous networks (HetNets) can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macrosystem. However, the large-scale deployment of small BSs significantly increases the complexity and workload of network maintenance and optimisation. The current handover (HO) triggering mechanism A3 event was designed only for mobility management in the macrosystem. Directly implementing A3 in 5G-HetNets may degrade the user mobility robustness. Motivated by the concept of self-organisation networks (SON), this study developed a self-optimisation triggering mechanism to enable automated network maintenance and enhance user mobility robustness in 5G-HetNets. The proposed method integrates the advantages of subtractive clustering and Q-learning frameworks into the conventional fuzzy logic-based HO algorithm (FLHA). Subtractive clustering is first adopted to generate a membership function (MF) for the FLHA to enable FLHA with the self-configuration feature. Subsequently, Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower the FLHA with a self-optimisation function. The FLHA with SON functionality also overcomes the limitations of the conventional FLHA that must rely heavily on professional experience to design. The simulation results show that the proposed self-optimisation FLHA can effectively generate MF and fuzzy rules for the FLHA. The proposed approach can minimise the HO, ping-pong HO, and HO failure ratios while improving network throughput and latency by comparing with conventional triggering mechanisms.

Scalable video traffic offloading for streaming services in 5G HetNets

Scalable video traffic offloading for streaming services in 5G HetNets

Coverage Analysis in Two-tier 5G Hetnet Based on Stochastic Geometry with Interference Coordination Strategy

Coverage Analysis in Two-tier 5G Hetnet Based on Stochastic Geometry with Interference Coordination Strategy

Interference Mitigation and Power Minimization in 5G Heterogeneous Networks

Macro cells’ (MCs) densification with small cells (SCs) is one of the promising solutions to cope with the increasing demand for higher data rates in 5G heterogeneous networks (HetNets). Unfortunately, the interference that arises between these densely deployed SCs and their elevated power consumption have caused huge problems facing the 5G HetNets. In this paper, a new soft frequency reuse (SFR) scheme is proposed to minimize the interference and elevate the network throughput. The proposed scheme is based on on/off switching the SCs according to their interference contribution rate (ICR) values. It solves the interference problem of the densely deployed SCs by dividing the cell region into center and edge zones. Moreover, SCs on/off switching tackles the elevated power consumption problem and enhances the power efficiency of the 5G network. Furthermore, our paper tackles the irregular nature problem of 5G HetNets and compares between two different proposed shapes for the center zone of the SC: circular, and irregular shapes. Additionally, the optimum radius of the center zone, which maximizes the total system data rate, is obtained. The results show that the proposed scheme surpasses the traffic and the random on/off switching schemes, as it decreases the outage probability and enhances the total system data rate and power efficiency. Moreover, the results demonstrate the close performance of both the irregular and circular shapes for the center zone.

Embedding Security Awareness for Virtual Resource Allocation in 5G Hetnets Using Reinforcement Learning

In the 5G era, heterogeneous networks (Het-Nets) are designed for achieving data rates and customized service demands. To realize this, virtualization technologies are widely accepted as enablers for implementing 5G HetNets, aimed at managing and scheduling virtualized physical resources in a flexible manner. However, the major focus of the existing research lies in the effective allocation of virtual resources and maximizing the number of implemented network services, ignoring the virtual resource security issues. However, the security threats and vulnerabilities due to the complexity of virtualization can lead to major performance outbreaks and information leakage. Therefore, this article attempts to tackle the security issues in 5G HetNets virtual resource allocation. The article starts from modeling the major security attacks for virtual resource allocation through comprehensive discussion on the typical types of security attacks. Following the attack model, a novel secure framework (VRA-RL-SecAwa) based on the emerging reinforcement learning approach, is presented. The proposed VRA-RL-SecAwa framework works in different phases, 1. Reinforcement-learning-based preliminary security preparation 2. Greedy-approach-based secure virtual node resource allocation embedding 3. Secure and shortest path virtual link resource allocation scheme 4. Network reconfiguration and update The proposed VRA-RL-SecAwa framework is evaluated through extensive simulations in order to demonstrate its efficiency and effectiveness. The results obtained validate the superiority of the proposed framework in contrast to existing variants of its category.

Robust and Universal Seamless Handover Authentication in 5G HetNets

The evolving fifth generation (5G) cellular networks will be a collection of heterogeneous and backward-compatible networks. With the increased heterogeneity and densification of 5G heterogeneous networks (HetNets), it is important to ensure security and efficiency of frequent handovers in 5G wireless roaming environments. However, existing handover authentication mechanisms still have challenging issues, such as anonymity, robust traceability and universality. In this paper, we address these issues by introducing RUSH, a Robust and Universal Seamless Handover authentication protocol for 5G HetNets. In RUSH, anonymous mutual authentication with key agreement is enabled for handovers by exploiting the trapdoor collision property of chameleon hash functions and the tamper-resistance of blockchains. RUSH achieves universal handover authentication for all the diverse mobility scenarios, as exemplified by the handover between 5G new radio and non-3GPP access regardless of the trustworthiness of non-3GPP access and the consistency of the core network. RUSH also achieves perfect forward secrecy, master key forward secrecy, known randomness secrecy, key escrow freeness and robust traceability. Our formal security proofs based on the BAN-logic and formal verification based on AVISPA indicate that RUSH resists various attacks. Comprehensive performance evaluation and comparisons show that RUSH outperforms other schemes in both computation and communication efficiencies.

Power management in green FFR-based heterogeneous cellular networks

The growth of mobile devices and their traffic volume make it necessary for mobile network operators to provide higher capacity. As traditional macrocell-based cellular networks could not support a massive number of mobile users, Heterogeneous Networks (HetNets) are welcomed as a coexistence of macro-cells and small-cells in 5G mobile networks. The main challenge of 5G HetNets is the management of co-tier and cross-tier interference. Fractional Frequency Reuse (FFR) is a suitable technique to mitigate the interference along with improving spectrum efficiency. On the other side, energy consumption has attracted much attention in 5G green cellular networks. Efficient power allocation and cell zooming/cell switch-off techniques are of the most important research directions in this area. However, the impact of cell zooming/switch-off on FFR-based HetNets has not been attended adequately. In this paper, a solution is presented which exploits dynamic adjustment of cell-center radius and efficient power allocation in FFR-based HetNets when conducting a cell switch-off/cell zooming. A heuristic algorithm is presented for cell-center radius adjustment problem. Also, for efficient power allocation, a multi-objective optimization problem is formulated and solved using NSGA-II algorithm Simulation results show that the proposed method reduces energy consumption compared to the baseline method and diminishes the overall interference in the system.

Federated Learning Empowered End-Edge-Cloud Cooperation for 5G HetNet Security

The distributed and heterogeneous framework in the 5G heterogeneous network (Het-Net) makes it vulnerable to attacks of different kinds. Nodes for improving the network security are therefore important to eliminate such critical threats. Without cooperation or with limited cooperation, these nodes are substantially restricted in their protecting capacity due to specific characteristics such as heterogeneity, hierarchy, and wide range in the 5G HetNet. In this article, we propose a federated learning empowered end-edge-cloud cooperation-based framework for enhancing 5G HetNet security. In this framework, nodes equipped with attack detection mechanisms are distributed in the end, edge, and cloud of the 5G HetNet. We then design cooperative training schemes to realize the full potential of these nodes in detecting attacks. Illustrative results demonstrate the superior performance of our proposed scheme compared to three different benchmark schemes.

Deep Reinforcement Learning for Energy-Efficient Multi-Channel Transmissions in 5G Cognitive HetNets: Centralized, Decentralized and Transfer Learning Based Solutions

Energy efficiency (EE) constitutes a key target in the deployment of 5G networks, especially due to the increased densification and heterogeneity. In this paper, a Deep Q-Network (DQN) based power control scheme is proposed for improving the system-level EE of two-tier 5G heterogeneous and multi-channel cells. The algorithm aims to maximize the EE of the system by regulating the transmission power of the downlink channels and reconfiguring the user association scheme. To efficiently solve the EE problem, a DQN-based method is established, properly modified to ensure adequate QoS of each user (via defining a demand-driven rewarding system) and near-optimal power adjustment in each transmission link. To directly compare different DQN-based approaches, a centralized (C-DQN), a multi-agent (MA-DQN) and a transfer learning-based (T-DQN) method are deployed to address whether their applicability is beneficial in the 5G HetNets. Results confirmed that DQN-assisted actions could offer enhanced network-wide EE performance, as they balance the trade-off between the power consumption and achieved throughput (in Mbps/Watt). Excessive performance was observed for the MA-DQN approach (>5 Mbps/Watt), since the decentralized learning supports low-dimensional agents to be coordinated with each other through global rewards. In further comparing the T-DQN against MA-DQN solutions, T-DQN presents beneficial usage for very low or very high inter-cell distances, whereas the usage of MA-DQN is preferred (by a factor of ~1.3) for intermediate inter-cell distances (100-600m), where the power savings are feasible towards achieving increased EE. Furthermore, T-DQN scheme guarantees good EE solutions (above 2 Mbps/Watt), even for densely-deployed macro-cells, with effortless training and memory requirements. On the contrary, MA-DQN offers the best EE solutions at the expense of massive training resources and required training time.

Deep-Learning-Empowered Digital Forensics for Edge Consumer Electronics in 5G HetNets

The upcoming 5G heterogeneous networks (HetNets) have attracted much attention worldwide. Large amounts of high-velocity data can be transported by using the bandwidth spectrum of HetNets, yielding both great benefits and several concerning issues. In particular, great harm to our community could occur if the main visual information channels, such as images and videos, are maliciously attacked and uploaded to the Internet, where they can be spread quickly. Therefore, we propose a novel framework as a digital forensics tool to protect end users. It is built based on deep learning and can realize the detection of attacks via classification. Compared with the conventional methods and justified by our experiments, the data collection efficiency, robustness, and detection performance of the proposed model are all refined. In addition, assisted by 5G HetNets, our proposed framework makes it possible to provide high-quality real-time forensics services on edge consumer devices such as cell phone and laptops, which brings colossal practical value. Some discussions are also carried out to outline potential future threats.

A Game Theoretic Scheme for Collaborative Vehicular Task Offloading in 5G HetNets

The 5G heterogeneous networks (HetNets) are capable of providing real-time computing services for autonomous vehicles (AVs) by deploying edge computing devices (ECDs) at macro cell base stations (MCBSs) and small cell base stations (SCBSs). With the imbalanced distribution and fast moving AVs contending intensely for computing services, how to efficiently exploit cooperations among participants in 5G HetNets to improve the service performance is therefore challenging. In this paper, we develop a game theoretic scheme for collaborative vehicular task offloading to facilitate the computing services in 5G HetNets. Specifically, we propose a two-stage vehicular task offloading mechanism to promote the cooperation among participants with the target of improving the task completion rate and the utilities of the participants, where the mechanism jointly considers the network architecture of the HetNets, the imbalanced distribution of AVs and the reuse of task results. In the first stage, an auction model is designed to help the MCBS select the optimal SCBS to execute the offloaded task based on the requirement of the task and the available computing resources of SCBSs. According to the task execution cost declared by the selected SCBS, the MCBS then bargains with the AV for the agreement of the task offloading service to maximize their utilities in the second stage. Using simulations, we show that the proposed collaborative task offloading scheme can achieve a higher task completion rate for the task offloading service and bring higher utilities to all participants than conventional schemes.

Coordinated Framework for Spectrum Allocation and User Association in 5G HetNets With mmWave

Dense deployment of small cells operating on different frequency bands based on multiple technologies provides a fundamental way to face the imminent thousand-fold traffic augmentation. This heterogeneous network (HetNet) architecture enables efficient traffic offloading among different tiers and technologies. However, research on multi-tier HetNets where various tiers share the same microwave spectrum has been well-addressed over the past years. Therefore, our work is targeted towards novel multi-tier HetNets with disparate spectrum (microwave and millimeter wave). In fact, despite the huge capacity brought by millimeter-wave technology, the latter will fail to provide universal coverage, especially indoor, and so mmWave will inevitably co-exist with a traditional sub-6GHz cellular network. In this work, we propose a coordinated user association and spectrum allocation by resorting to non-cooperative game theory. In fact, in such an arduous context, efficient distributed solutions are imperative. Extensive simulation results show the precedence of our coordinated approach in comparison with state-of-the-art heuristics. Moreover, we evaluate the impact of various network parameters, such as mmWave density, cell load, and user distribution and density, offering valuable guidelines into practical 5G HetNet design. Finally, we assess the benefit brought by massive MIMO for mmWave in such a highly heterogeneous setting.

UAV Assisted 5G Het-Net: A Highly Supportive Technology for 5G NR Network Enhancement

Recently, unmanned aerial vehicles (UAVs) have grabbed impressive attention from industry and research network, because of the fastergrowth in a wide scope of applications. Especially, UAVs are being utilized to ensure a better solution to a dependable and cost-effective wireless communication network from the sky. The adoption of UAVs has been considered as an alternative supplement of existing cellular networks, to accomplish better transmission efficiency with enhanced coverage and network capacity. This article has represented some wireless communication scenarios where UAVs can be utilized to ensure better coverage, enhancement of efficiency and capacity. After that, to establish the effectiveness of UAVs in wireless communication thepaper has included several performance scenarios (path loss, attenuation, and data rate) after utilizing UAVs in the wireless network. Finally concluded with discussing several challengesfor UAV assisted wireless networks and the scopes of further enhancement of such kind of networks. This article might be informative to those who are engaged in research regarding UAV assisted wireless communication.

Deep Reinforcement Learning for Dynamic Uplink/Downlink Resource Allocation in High Mobility 5G HetNet

Recently, the 5G is widely deployed for supporting communications of high mobility nodes including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the main components for constructing the wireless heterogeneous network (HetNet). To further improve the radio utilization, the Time Division Duplex (TDD) is considered to be the potential full-duplex communication technology in the high mobility 5G network. However, the high mobility of users leads to the high dynamic network traffic and unpredicted link state change. A new method to predict the dynamic traffic and channel condition and schedule the TDD configuration in real-time is essential for the high mobility environment. In this paper, we investigate the channel model in the high mobility and heterogeneous network and proposed a novel deep reinforcement learning based intelligent TDD configuration algorithm to dynamically allocate radio resources in an online manner. In the proposal, the deep neural network is employed to extract the features of the complex network information, and the dynamic Q-value iteration based reinforcement learning with experience replay memory mechanism is proposed to adaptively change TDD Up/Down-link ratio by evaluated rewards. The simulation results show that the proposal achieves significant network performance improvement in terms of both network throughput and packet loss rate, comparing with conventional TDD resource allocation algorithms.

Fractional frequency reuse based frequency allocation for 5G HetNet using master–slave algorithm

Frequency allocation in small cell-based green heterogeneous mobile networks is a demanding research domain nowadays. Femtocells are the essential components of small cell networks. In this paper, we propose a low power micro-femtocell network using the master–slave algorithm. In the master–slave algorithm, the master node allocates work to the slave nodes. When a slave ends its task given by a master node, it informs the master node, and it is being assigned a new workload. Slave nodes do not communicate with each other. In our approach, the microcell is divided into three sectors, and each sector is further categorized into three regions: inner region, outer region, and most-outer region. Femtocells are allocated in these three regions. According to the duty cycle, several femtocells are chosen as master femtocells. The rest of the femtocells are assigned under the supervision of the master femtocells. These femtocells are referred to as slave femtocells. The master femtocells communicate with the microcell, and the slave femtocells communicate with the corresponding master femtocell. Frequency allocation for this micro-femtocell network is proposed based on Fractional Frequency Reuse (FFR). The power consumption, signal-to-interference-plus-noise ratio (SINR), and spectral efficiency for the proposed network are calculated. The simulation results exemplify that the proposed scheme reduces the power consumption of the network by approximately 44%–80% than the conventional heterogeneous network. The simulation results also demonstrate that the proposed network has better SINR and spectral efficiency than the existing micro-femtocell network. For experimental analysis, vector signal generator (VSG) and vector signal analyzer (VSA) are used. The experimental results also show that the proposed network is greener compared to the existing micro-femtocell network.

A Green Converged TWDM-PON and 5G HetNet Catering Applications Demanding Low Latency

Abstract Integration of Passive Optical Network (PON) and wireless network technologies can offer a high data rate with ubiquitous Internet access to the end-users. Due to the growing importance of reducing the carbon footprint of telecommunication networks, energy-saving mechanisms are dispensed in the networking equipment. One of the commonly used approaches to reduce energy consumption is the “sleep mode” in which a network equipment periodically turns off some of its components so as to reduce energy consumption. Note that, the current PON and wireless access network technologies have their own power-saving modes. In order to gain power-saving and meet latency of applications in an integrated PON and wireless access network technologies, both optical and wireless access technologies need to have consent on how traffic management (bandwidth allocation, traffic forwarding path), as well as power-saving modes, should be managed in the network. That is, there needs to have synchronization of these two network segments. Otherwise, the end-users would experience longer delay and lower throughput. In this paper, we introduce a Delay-Aware Integrated Sleep Mode (DISM) solution designed considering 5G Heterogeneous Networks (HetNets) architecture which connected with core networks through Time and Wavelength Division Multiplexed Passive Optical Network (TWDM-PON). In particular, we propose a power saving mechanism using sleep mode for TWDM-PON taking into account packet forwarding delay from the optical network termination point to the end-users’ mobile terminal so as to maximize energy-saving in TWDM-PON while meeting latency requirements of applications with stringent latency requirements. Results obtained through simulations impart that our proposed scheme can significantly satisfy the delay requirement of the traffic while it can still reduce energy consumption in TWDM-PON. Furthermore, the results provide insights on how power saving mechanism in PON should be managed for such 5G HetNet and optical integrated networks in order to satisfy the latency requirement of the traffic.

Features detection based blind handover using kullback leibler distance for 5G HetNets systems

<p>The Fifth Generation of Mobile Networks (5G) is changing the cellular network infrastructure paradigm, and Small Cells are a key piece of this shift. But the high number of Small Cells and their low coverage involve more Handovers to provide continuous connectivity, and the selection, quickly and at low energy cost, of the appropriate one in the vicinity of thousands is also a key problem. In this paper, we propose a new method, to have an efficient, blind and rapid handover just by analysing Received Signal probability density function instead of demodulating and analysing Received Signal itself as in classical handover. The proposed method exploits KL Distance, Akaike Information Criterion (AIC) and Akaike weights, in order to decide blindly the best handover and the best Base Station (BS) for each user</p>

Features detection based blind handover using kullback leibler distance for 5G HetNets systems

Features detection based blind handover using kullback leibler distance for 5G HetNets systems

Smart Mode Selection Using Online Reinforcement Learning for VR Broadband Broadcasting in D2D Assisted 5G HetNets

As an emerging broadband service pattern in the 5G era, VR broadcasting needs a considerable amount of bandwidth and strict quality of service (QoS) control. The traditional eMBMS or enTV transmission mode in HetNets consisting of macro cells and small cells cannot bring about a good trade-off between broadband performance and resource utilization for VR broadcasting service. D2D multicasting applied to VR broadcasting can improve the performance of edge users and resource utilization. Motivated by the rapid development of AI techniques, this paper proposes a novel hybrid transmission mode selection based on online reinforcement learning to address this problem. Each VR broadband user can be associated by one of the three modes: macrocell broadcasting, mmWave small cell unicasting and D2D multicasting. This paper first models this intelligent mode decision process as a problem to pursue the optimal system throughput. Then, an online machine learning-based method is proposed to solve this problem, which consists of a fast D2D clustering module based on unsupervised learning and a smart mode selection module based on reinforcement learning. The simulation results verify that the WoLF-PHC and Nash Q-learning perform better than other algorithms in large-scale scenarios and small-scale scenarios, respectively. The proposed intelligent transmission mode selection can also achieve larger VR throughput than traditional broadcasting strategies with a good balance between broadband performance and resource utilization.

Interference Management, Backhaul Routing & Switching off in 5G Hetnets with Mmwave Backhaul Links: A Relative Perspective

Today most of the next generation wireless communication systems(5G) faces the challenge of supporting the demands for higher data rates growing exponentially and ensuring to provide a stable quality of service (QoS) throughout the network. Over the next 2 years meeting up these requirements need to maximize the capacity of the network by a factor which will multiply it by thousands. Moreover, the most important societal as well as economical concerns include the power usage of data and communication technology industry and pollution mostly energy-related. On 5G two contrary requirements of providing capacity to support higher cellular networks along with consuming less energy needs to be focused upon. Due to the scarcity of spectral sources, a wide consensus can only be achieved by increasing significantly the number of antennas operating per unit area. Here a heterogeneous network named HetNet is analysed, comprising a macro base station (BS) along with several antennas and an overlaid dense tier possessing SCAs(small cell access points)through a wireless backhaul to get data traffic. The SCAs mostly associate with static as well as low mobility user equipment whereas macro BS serve the medium-to-high mobility. This work analyses the methods of the ultra-dense wireless 5G heterogeneous networks considering the interference management along the mm-Wave backhaul links to utilize the spectrum and network densification to operate mm-Wave 5G HetNet. The study reviews several literature works, their drawbacks and developing a joint model by combining base station switch-off technique with interface mitigation. This study further throws light on the scope of managing the backhaul-aware interference which in turn enhances potential capacity of the system and depending on the suitable backhaul the capacity is improved. Millimetre wave(mm-Wave) has proved to be a remarkable candidate to overcome the issue of ensuring a broad bandwidth having secure link transmission. The ultra-dense heterogeneous networks are discussed along with discussing the role interference management algorithms to minimize energy consumption. The importance of interference management is discussed along with discussing issues related. The research problem is formulated following a comparative analysis.