Handover Metrics Research Articles

Deep reinforcement learning for base station switching scheme with federated LSTM‐based traffic predictions

AbstractTo meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality‐of‐service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short‐term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL‐based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal‐to‐interference noise ratio, handover metrics, and prediction performance.

Heterogeneous Ultradense Networks With Traffic Hotspots: A Unified Handover Analysis

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this paper, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterwards, we derive the expressions of average distances from a typical terminal to different BSs, and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.

Implementing the IMIST-AMBO tool for paramedic to trauma team handovers: a video review analysis

Patient handover betweenparamedics and the trauma team is vulnerable to communication errors that may adversely affect patient care. This study assesses the feasibility of a handover tool, IMIST-AMBO (acronym of categories), implementation in the trauma bay and evaluates the degree to which it improves handover metrics. This is a prospective observational cohort study conducted at Canada's largest level-one trauma center. Feasibility of the tool implementation and improvement in handover metrics were assessed. Strategies for implementation included distribution of an educational video and posters, and point-of-care reminders in the trauma bay. Two reviewers independently assessed video recordings of handovers to evaluate handover metrics. Findings were compared to data obtained during a knowledge gap analysis conducted prior to the initiation of this study at the same institution. Over 13weeks (August to November 2020), 140 videos were recorded, of which 80 used the IMIST-AMBO tool (compliance of 57%). Paramedic adherence to the handover structure occurred in 70.4% of cases, with greater adherence to the IMIST (82.2%) compared to the AMBO (47.1%) section. The mean (± standard deviation) handover duration was shorter (1min:58s ± 0:44s during implementation vs. 2min:47s ± 1:14s pre-implementation, [p < 0.001]). Frequency of parallel conversations and informal handovers improved (61% to 30% and 65% to 13%, [p < 0.001], respectively). Interruptions during the handover decreased from 3.05 (± 1.95) to 1.5 (± 1.7), p < 0.001. The tool was received favorably among study participants. The IMIST-AMBO tool reduced the frequency of interruptions, parallel conversations, and informal handovers during paramedic-trauma team handovers at our institution. The quality and amount of information communicated per handover improved, all with a decrease in handover duration. The IMIST-AMBO tool may be applied to other trauma centers across Canada, or more broadly on an international scale.

Performance analysis of handover management in 5G small cells

In 5G networks, the coverage area of the base stations is smaller and the communications are at higher frequencies. The small cell concept has risen with high mobility and small coverage area. Mobile users can move among the small cells with different service requirements as a result of handover. The frequency of changing the small cell and the considered handover parameters affect the quality of service. In this paper, the handover performance analysis with different metrics and a realistic urban channel model is investigated for 5G small cells. The effect of the traditional handover metrics performance, on the 5G small cell handover procedure, is also shown. This study contributes to the research for developing new procedures on 5G small cell handover.

A Seamless Mobility Management Protocol in 5G Locator Identificator Split Dense Small Cells

Network densification with Small Cells (SCs) has emerged as a key technique to increase the 5G network capacity. However, in a densified network, fast mobile nodes will experience frequent handovers with a high signaling load, handover latency, and packet loss, due to the short cell radius. Indeed, Distributed Mobility Management (DMM) protocols aim to solve the shortcomings of centralized mobility management solutions such as poor scalability and non-optimal routing. However, when the cell residence time is short, DMM protocols might suffer from increased costs and limited performance. This paper proposes a localized mobility management protocol in 5G dense SCs, based on the locator identifier separation protocol, local mobility anchoring, and fast handovers concepts. The proposed scheme divides a local domain into several location service areas, each controlled by a local anchor. We provide the analytical models of several handover metrics, namely the average total signaling cost, the data delivery cost, the handover latency, and the packet loss. Numerical and simulation results show significant cost savings, up to 30 percent in signaling overhead, up to 53 percent of packet loss, and up to 90 percent of processing load reduction at the core of the network compared to the existing lisp mobile node protocol.

GRA‐based handover for dense small cells heterogeneous networks

Ultra-dense small cell (SC) deployment in the future 5G network makes the architecture of the network as heterogeneous networks (HetNets). This is a good solution to boost the capacity of the network and extend its coverage. However, the dense SCs deployment has brought new challenges to the network including interference, frequent unnecessary handovers, and handover failures. Therefore, user equipment will suffer from a degraded quality of service. In this paper, the authors propose a grey rational analysis-based handover (GRA-HO) method in dense SCs HetNet. The proposed method combines the analytical hierarchy process technique to obtain the weight of the handover metrics and the GRA method to rank the available cells for the best handover target. The performance of the proposed method is evaluated and compared with the traditional multiple attribute decision-making methods including simple additive weighting and VIKOR methods. Results show that the GRA-HO method has outperformed the existing methods in terms of reducing the number of frequent handovers and link failures, in addition to enhancing energy efficiency.

Multi-Criteria Handover Using Modified Weighted TOPSIS Methods for Heterogeneous Networks

Ultra-dense small cell deployment in future 5G networks is a promising solution to the ever increasing demand of capacity and coverage. However, this deployment can lead to severe interference and high number of handovers, which in turn cause increased signaling overhead. In order to ensure service continuity for mobile users, minimize the number of unnecessary handovers and reduce the signaling overhead in heterogeneous networks, it is important to model adequately the handover decision problem. In this paper, we model the handover decision based on the multiple attribute decision making method, namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The base stations are considered as alternatives, and the handover metrics are considered as attributes to selecting the proper base station for handover. In this paper, we propose two modified TOPSIS methods for the purpose of handover management in the heterogeneous network. The first method incorporates the entropy weighting technique for handover metrics weighting. The second proposed method uses a standard deviation weighting technique to score the importance of each handover metric. Simulation results reveal that the proposed methods outperformed the existing methods by reducing the number of frequent handovers and radio link failures, in addition to enhancing the achieved mean user throughput.

Network switching strategy for energy conservation in heterogeneous networks.

In heterogeneous networks (HetNets), the large-scale deployment of small base stations (BSs) together with traditional macro BSs is an economical and efficient solution that is employed to address the exponential growth in mobile data traffic. In dense HetNets, network switching, i.e., handovers, plays a critical role in connecting a mobile terminal (MT) to the best of all accessible networks. In the existing literature, a handover decision is made using various handover metrics such as the signal-to-noise ratio, data rate, and movement speed. However, there are few studies on handovers that focus on energy efficiency in HetNets. In this paper, we propose a handover strategy that helps to minimize energy consumption at BSs in HetNets without compromising the quality of service (QoS) of each MT. The proposed handover strategy aims to capture the effect of the stochastic behavior of handover parameters and the expected energy consumption due to handover execution when making a handover decision. To identify the validity of the proposed handover strategy, we formulate a handover problem as a constrained Markov decision process (CMDP), by which the effects of the stochastic behaviors of handover parameters and consequential handover energy consumption can be accurately reflected when making a handover decision. In the CMDP, the aim is to minimize the energy consumption to service an MT over the lifetime of its connection, and the constraint is to guarantee the QoS requirements of the MT given in terms of the transmission delay and call-dropping probability. We find an optimal policy for the CMDP using a combination of the Lagrangian method and value iteration. Simulation results verify the validity of the proposed handover strategy.

Local Anchor Schemes for Seamless and Low-Cost Handover in Coordinated Small Cells

Cellular systems are rapidly evolving from a homogeneous macrocell deployment to a heterogeneous deployment of macrocells overlaid with many small cells. The striking features of small cells include small coverage area, ad-hoc deployment, and flexible backhaul connectivity. These features call for a profound rethinking of traditional cellular concepts including mobility management and interference management among others. Owing to the unique features, coordinated small cells or commonly referred to as network of small cells promises several benefits including efficient mobility management in a rapid and scalable fashion. The problem of handover in a high-density small cell deployment is studied in this work. A novel local anchor-based architecture for a static cluster of small cells is proposed using which new handover schemes are presented. Such clusters are prevalent in the evolving cellular systems involving high-density small cell deployments in urban, residential, and enterprise environments. A mathematical framework is developed using discrete-time-Markov-models to evaluate the proposed schemes. Using this, closed-form expressions for key handover metrics including handover cost and interruption time are derived. Extensive numerical and simulation studies indicate significant savings of over 50 percent in the handover costs and, more importantly, up to 80 percent in the handover interruption time compared to the existing 3GPP scheme for coordinated small cells.

Handover Performance for Elastic Flows in Mobile Cellular Networks

The characterization of flows duration and their handover metrics is of paramount importance for the design and resource management of mobile networks. Elastic flows duration characterization is more involved than for streaming sessions, as it depends on the network load. In this work, the distribution of flows duration is characterized by a phase type distribution. The model is then applied to determine the handover probability under different cell residence time and flow size distributions. Simulation results confirm that the proposed model yields very accurate results under realistic assumptions.