Mobility Management In Heterogeneous Networks Research Articles

Reinforcement learning-based unmanned aerial vehicle trajectory planning for ground users’ mobility management in heterogeneous networks

The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets.

Pedestrian mobility management for heterogeneous networks

<p>Pending the arrival of the next generation of 5G which is not yet deployed in<br />some countries like Algeria, 4G LTE remains one of the main mobile networks to ensure adequate quality services. Mostly, the deployment of femtocells to support the macrocell structure is crucial in the handover decision process. This paper presents a new approach called the epsilon Kalman Filter with normalized least-mean-square (ϵKFNLMS) to realize the handoff triggering in two-tier long-term evolution networks to ensure communication continuity to the pedestrian UE and improve mobility management. ϵKFNLMS uses a two-step process: a tracking process and a prediction process, to produce an optimal future state estimate at ”t+p”, where ”p” is the prediction footstep. The tracking process is performed by the Kalman filter, known for its precision in the state of the signal at time ”t”. It perfectly reduces the estimation error, injected afterward in the variable step-size NLMS algorithm (VSS-NLMS). While the prediction<br />process is performed by the VSS-NLMS algorithm, an adaptive filter<br />known for its prediction of the future state at ”t+p”. Thus, the goal is to achieve a faster convergence with a steady-state. ϵ value provides a precise setting of the handover trigger. Through different numerical simulations in several indoor environments, the results show that the performance and effectiveness of the proposed approach (ϵKFNLMS) provide lower mean square error (MSE), stable physical appearance in the prediction process (convergence with a steady-state), and excellent speed of convergence compared to the classical Normalized LMS (NLMS) and Li-NLMS adaptive filters.</p>

Handover Count Based MAP Estimation of Velocity With Prior Distribution Approximated via NGSIM Data-Set

In this paper, we propose a maximum-a-posteriori probability (MAP) based velocity estimation technique in which the prior distribution is defined by current location of the user. Motivation of this work is to improve accuracy of the existing velocity estimation techniques which are either solely based on cellular network measurements or location specific information. Our objective is to exploit both cellular measurements and location information in Bayesian sense; thus, to jointly address the critical applications of mobility management in Heterogeneous-Networks (HetNets), and intelligent transportation system. Here we assume that the Next Generation Simulation (NGSIM) data set for velocity is available at the current location and can be utilized to approximate the prior distribution. Additional information in form of prior distribution function is then exploited to improve the minimum variance unbiased (MVU) estimate of velocity which is based on handover count measurements. Since MVU estimate is a random variable, we first formulate its density function parameterized over the actual velocity. Next, we follow Bayesian approach to accommodate both prior distribution and parametric density function in deriving posterior density function of velocity. Finally, we derive expression of the MAP estimator considering various standard distribution functions which best fit to the density function obtained from NGSIM data set. In order to quantify the quality of estimate, we derive its variance and the corresponding Cramer-Rao-bound (CRB) on the minimum error variance. Numerical results demonstrate that the proposed estimator which incorporates NGSIM data set is asymptotically efficient and outperforms other classical handover count based estimation techniques.

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.

Sojourn Time-Based Velocity Estimation in Small Cell Poisson Networks

Due to the increasing density of small cells, mobility management in heterogeneous networks has become a challenging task. One key challenge facing the development of advanced mobility management techniques is the accurate estimation of the users' velocity. One simple way to estimate a user's velocity is via the use of sojourn time samples. In this letter, the Cramer-Rao lower bound (CRLB) for the sojourn time-based velocity estimation is analyzed. Stochastic geometry is used for the spatial modeling of small cells, and the CRLB is derived using the tools from estimation theory. An asymptotically unbiased velocity estimator is also derived. Our analysis shows that the sojourn time-based velocity estimation exhibit a lower CRLB compared to the CRLB of classical velocity estimation using handover count.

Impact of Packet Size and Mobility on Heterogeneous Wireless Network

Most of the real life networks are heterogeneous which always combine the best features of two or more networks. An integration of available wireless networks viz: Cellular and Wireless Local Area Network on expandable and flexible platform is called as radio resource management for heterogeneous networks. Along with resource utilization, end to end delay and throughput are also important parameters for analysis of heterogeneous networks. The existing research work has focused on the effect of packet size and mobility management independently on heterogeneous networks. This paper presents algorithms developed using Ad-hoc on Demand Distance Vector routing protocol to analyze the combined effect of packet size along with mobility management for heterogeneous networks. The developed algorithm considers common radio resource management scheme using packet size of 256, 512, 1024 bit. The two mobility pattern pause 5 and pause 60 are created with log movement to nodes. The heterogeneous network created with 21 nodes along with MAC protocol 802.11b and data rate of 11 Mbps. The simulation is done for 50 s. The simulation results show that End-to-End Delay, Throughput and Bandwidth Utilization of the heterogeneous networks are improved by packet size and mobility management as a combined task. Secondly, the developed priority based vertical handoff technique shows improvement in Bandwidth utilization, Packet Delivery Ratio (PDR) and Energy Consumption for heterogeneous networks. The Network Simulator is used to demonstrate the functionality and evaluate the performance of the algorithms developed.

A Comparative Performance Analysis on NEMO-QoS and MIPv6-QoS in Heterogeneous Environments

Mobility management in heterogeneous networks is an important area since the mobile network may be moving between multiple types of access networks which involve several service providers such as 802.11b WLAN, 802.16 WiMAX and UMTS Networks. In crowded environments, the bottleneck is often between the access link and the mobile network. Being able to prioritize the traffic and allocate a minimum bandwidth guarantee for each user is crucial in this environment. This study presents a novel dynamic QoS provisioning mechanism for network mobility in heterogeneous networks integrated with IEEE 802.21 Media Independent Handover (MIH) protocol. Network Mobility (NEMO) Basic Support protocol has three important nodes Mobile Network Nodes (MNNs), Mobile Router (MR) and Home Agent (HA). The dynamic QoS provisioning mechanism which consists of traffic marking, prioritizing and scheduling are implemented at the mobile network node domain. While the admission control module is implemented at a mobile router domain. Several experiments are conducted to evaluate the mechanism. The results have shown the importance of less packet loss and delay and higher packet forwarding rate.