Multi-tier 5G Research Articles

Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning

This paper proposes a potential enhancement of handover for the next-generation multi-tier cellular network, utilizing two fifth-generation (5G) enabling technologies: multi-access edge computing (MEC) and machine learning (ML). MEC and ML techniques are the primary enablers for enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). The subset of ML chosen for this research is deep learning (DL), as it is adept at learning long-term dependencies. A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Subsequently, edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40–60% improvement in overall throughput. Although the proposed scheme may increase the number of handovers, it is effective in reducing the handover failure (HOF) and ping-pong rates by 30% and 86%, respectively, compared to the current 3GPP scheme.

A Reinforcement Learning Approach to Service Based User Admission in a Multi-Tier 5G Wireless Networks

The expected massive connectivity in 5G wireless network is bound to become a challenge to service providers. Many services over the 5G network will be aligned to a particular radio access network (RAN). As a result admitting a service based user to a particular RAN will depend on the most efficient radio access technology selection (RAT). This is because 5G network will adopt multi-tier radio access networks ranging from high power macro base stations to extremely low power Bluetooth connectivity. Selection of a service oriented RAT is critical because some wireless services have superior quality of service under certain RATs. Maintaining efficient RAT selection by network operators will improve power allocation efficiency, bandwidth allocation efficiency and operation expenditure. The complexity of associating a RAT to service based user while considering network state such as service packet size, the turnaround time, the power allocation has not been fully explored. In this paper we propose a reinforcement learning approach to user admission based on efficient RAT selection considering wireless services in a cross tier wireless radio access network domain. The proposed algorithm is expected to improve RAT selection efficiency while minimizing the computation complexity. We perform extensive simulation using Python dynamic libraries and present our results alongside existing approaches

Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users' needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naive Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs' average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error.

Radio Resource Management in NB-IoT Systems: Empowered by Interference Prediction and Flexible Duplexing

NB-IoT is a promising cellular technology for enabling low cost, low power, long-range connectivity to IoT devices. With the bandwidth requirement of 180 kHz, it provides the flexibility to deploy within the existing LTE band. However, this raises serious concerns about the performance of the technology due to severe interference from multi-tier 5G HetNets. Furthermore, as NB-IoT is based on HD-FDD, the symmetric allocation of spectrum band between the downlink and uplink results in underutilization of resources, particularly in the case of asymmetric traffic distribution. Therefore, an innovative RRM strategy needs to be devised to improve spectrum efficiency and device connectivity. This article presents the detailed design challenges that need to be addressed for the RRM of NB-IoT and proposes a novel framework to devise an efficient resource allocation scheme by exploiting cooperative interference prediction and flexible duplexing techniques.

Energy Efficient and Delay Aware 5G Multi-Tier Network

Multi-tier heterogeneous Networks (HetNets) with dense deployment of small cells in 5G networks are expected to effectively meet the ever increasing data traffic demands and offer improved coverage in indoor environments. However, HetNets are raising major concerns to mobile network operators such as complex distributed control plane management, handover management issue, increases latency and increased energy expenditures. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for reducing energy expenditures. In this paper, a Markov Decision Process (MDP)-based algorithm is proposed to switch between three different power consumption modes of a base station (BS) for improving the energy efficiency and reducing latency in 5G networks. The MDP-based approach intelligently switches between the states of the BS based on the offered traffic while maintaining a prescribed minimum channel rate per user. Simulation results show that the proposed MDP algorithm together with the three-state BSs results in a significant gain in terms of energy efficiency and latency.

CPPHA: Capability-based Privacy-Protection Handover Authentication Mechanism for SDN-based 5G HetNets

Ultra-dense Heterogeneous network (HetNet) technique can significantly improve wireless link quality, spectrum efficiency and system capacity, and satisfy different requirements for coverage in hotspots, which has been viewed as one of the key technologies in fifth Generation (5G) network. Due to the existence of many different types of base stations (BSs) and the complexity of the network topology in the 5G HetNets, there are a lot of new challenges in security and mobility management aspects for this multi-tier 5G architecture including insecure access points and potential frequent handovers among several different types of base stations. In this paper, we integrate user capability and Software Defined Network (SDN) technique, and propose a capability-based privacy protection handover authentication mechanism in SDN-based 5G HetNets. Our proposed scheme can achieve the mutual authentication and key agreement between User Equipments (UEs) and BSs in 5G HetNets at the same time largely reduce the authentication handover cost. We demonstrate that our proposed scheme indeed can provide robust security protection by employing several security analysis methods including the BAN logic and the formal verification tool Scyther. In addition, the performance evaluation results show that our scheme outperforms other existing schemes.

Antenna array techniques for nonuniform cell zooming and adaptive frequency planning

SummaryThis paper proposes the antenna array system and the techniques for the nonuniform cell zooming and adaptive frequency planning which is suitable for the energy‐efficient LTE network and multitier 5G heterogeneous network. The antenna array system is designed for the beam width control and scan control in the azimuth plane. The beam tilt is controlled in the elevation plane to control the cell radius. The first feature of the antenna array system is to perform nonuniform sector size control in order to deal with the nonuniform traffic distribution. The second feature of the antenna array system is to control the beam scan in the azimuth plane for the adaptive frequency planning. The footprint control of the current work is evaluated considering the antenna array's radiation pattern and all the physical parameters. Adaptive frequency planning can mitigate cochannel interference (CCI), which may be caused due to the cell zooming techniques proposed in this work. The footprint simulations confirm that the antenna array system can control the sector size with the highest possible flexibility. The antenna array system due to these features can effectively mitigate the interference. The results also confirm that the current antenna array system outperforms the existing antenna array systems.

Game theoretic approach for multi-tier 5G heterogeneous network optimization based on joint power control and spectrum trading

Multi-tier heterogeneous network (MHN) has recently emerged as a main technology in 5G network. Dense deployment of heterogeneous nodes in 5G MHN will have more density than single-tier networks. In 5G MHN, by increasing the number of small cells, network capacity, spectral efficiency, and data rate will increase. But reducing the dimension of the cell will cause inter/intra-tier interference in both uplink and downlink channels of MHN. High transmit power of Macro Base Station (MBS) downlink channel causes interference to Small cell User Equipment (SUE). Also, neighbor macrocell and small cell users suffer from interference created in both uplink and downlink channels of small cell. Thus interference management and mitigation is the important challenge for 5G MHN. In this paper, in order to mitigate both types of inter/intra-tier interferences, spectrum trading issue and power control are presented based on non-cooperative Stackelberg game under some sub-games through a pricing-based algorithm and convex optimization method. Finally, simulation results show that the performance of our system model such as average utility function of the small cell, SBS, energy efficiency and so on, will be improved through the proposed algorithm.

Energy‐efficient resource allocation and RRH association in multitier 5G H‐CRANs

AbstractThe rapid growth of connected devices and multimedia applications intensify the needs of high data rate and energy‐efficient networks. Multitier heterogeneous cloud radio access networks have been presented as a capable candidate for fifth‐generation networks for much higher data rate and energy efficiency (EE) than fourth generation. Therefore, in this paper, we consider the EE maximization in the uplink of a single‐carrier frequency division multiple access–based multitier heterogeneous cloud radio access network. We formulate a joint subchannel assignment, power allocation, and remote radio head association problem for EE maximization. Single‐carrier frequency division multiple access introduces subchannel exclusivity and subchannel adjacency constraints. These constraints, respectively, ensure the allocation of a subchannel to a single user at a time and the allocation of multiple subchannels to a user only if they are adjacent. In addition, we put constraints on each user's minimum data rate and maximum transmit power. Due to the simultaneous consideration of these four constraints, the solution of the formulated problem becomes harder. Therefore, a three‐step algorithm is developed, which solves this problem in an iterative manner. Simulation results verify that our proposed algorithm significantly improve the EE of the network.

Joint RRH-Association, Sub-Channel Assignment and Power Allocation in Multi-Tier 5G C-Rans

Multi-tier cloud-radio access networks (C-RANs) have been suggested as a promising network model in fifth generation wireless networks to provide high data rate, high spectral efficiency, and high energy efficiency at low cost. However, to achieve the potential benefits of multi-tier C-RANs, it is important to design efficient resource allocation algorithms provided that the data rate of users is guaranteed. Therefore, in this paper, we considered the joint optimization of remote-radio-heads (RRH) association, sub-channel assignment, and power allocation for network sum-rate maximization in single-carrier frequency division multiple access (SC-FDMA)-based multi-tier C-RAN. Due to the exclusivity and adjacency constraints of SC-FDMA, the resource allocation problem becomes harder to solve, thus, the optimal solution is difficult for reasonably sized network. Therefore, we propose an iterative algorithm that solves this non-linear mixed-integer problem in two steps wherein the first step, power allocation and subchannel assignment are carried out, while the second step of the proposed algorithm is concerned with the RRH-association. The iterative algorithm converges to efficient solution. The simulation results verify the effectiveness of our proposed algorithm.

Energy-Efficient Power Allocation in Multitier 5G Networks Using Enhanced Online Learning

The multitier heterogeneous structure of 5G with dense small cells deployment, relays, and device-to-device (D2D) communications operating in an underlay fashion is envisioned as a potential solution to satisfy the future demand for cellular services. However, efficient power allocation among dense secondary transmitters that maintains quality of service (QoS) for macro (primary) cell users and secondary cell users is a critical challenge for operating such radio. In this paper, we focus on the power allocation problem in the multitier 5G network structure using a noncooperative methodology with energy efficiency consideration. Therefore, we propose a distributive intuition-based online learning scheme for power allocation in the downlink of the 5G systems, where each transmitter surmises other transmitters power allocation strategies without information exchange. The proposed learning model exploits a brief state representation to account for the problem of dimensionality in online learning and expedite the convergence. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant improvement in system energy efficiency.

Capacity Improvement for Multi-Tier 5G Networks

Capacity Improvement for Multi-Tier 5G Networks

Analytical Study on Multi-Tier 5G Heterogeneous Small Cell Networks: Coverage Performance and Energy Efficiency.

In this paper, we investigate the coverage performance and energy efficiency of multi-tier heterogeneous cellular networks (HetNets) which are composed of macrocells and different types of small cells, i.e., picocells and femtocells. By virtue of stochastic geometry tools, we model the multi-tier HetNets based on a Poisson point process (PPP) and analyze the Signal to Interference Ratio (SIR) via studying the cumulative interference from pico-tier and femto-tier. We then derive the analytical expressions of coverage probabilities in order to evaluate coverage performance in different tiers and investigate how it varies with the small cells’ deployment density. By taking the fairness and user experience into consideration, we propose a disjoint channel allocation scheme and derive the system channel throughput for various tiers. Further, we formulate the energy efficiency optimization problem for multi-tier HetNets in terms of throughput performance and resource allocation fairness. To solve this problem, we devise a linear programming based approach to obtain the available area of the feasible solutions. System-level simulations demonstrate that the small cells’ deployment density has a significant effect on the coverage performance and energy efficiency. Simulation results also reveal that there exits an optimal small cell base station (SBS) density ratio between pico-tier and femto-tier which can be applied to maximize the energy efficiency and at the same time enhance the system performance. Our findings provide guidance for the design of multi-tier HetNets for improving the coverage performance as well as the energy efficiency.

Backhauling 5G small cells: A radio resource management perspective

Developing efficient backhauling solutions for small cells is considered as one of the most significant challenges in the rollout of the upcoming 5G cellular systems where massive numbers of small cells will be deployed. In this article, we first review small cell evolutions and the consequent radio access network architecture evolution toward heterogeneity and centralized baseband processing. The backhaul solution, which is mainly determined by the availability of existing backhaul infrastructures and service demand, will lead to a heterogeneous backhaul network architecture. Radio resource management issues including cell association, and interference management and scheduling with inter-node coordination, as well as how they are related to backhaul solutions are discussed. Based on the existing RRM solutions and their limitations in multi-tier 5G systems with heterogeneous backhaul network architecture, backhaul- aware RRM strategies are envisioned. Results from a simple case study of joint cell association and bandwidth allocation with a massive MIMO-based in-band wireless backhauling framework are presented to demonstrate the performance gain with backhaul-aware RRM in a multi-tier cellular RAN. Several related open research issues are highlighted, and possible solution approaches are briefly discussed.