A Handover Prediction Mechanism Based on LTE-A UE History Information

[[abstract]]In response to the rapidly developing of wireless communication technology, the deployed of eNB is denser and more complex. The research of how to handover accurately and fast in LTE-A are discussed much in recent years .In 3GPP Release 8, the UE History Information recorded by eNB was first proposed, it's proposed to provide eNB to judge the target eNB when handover. The history information includes the Cell ID and Time UE stayed in cell. We proposed an advance UE history information, reducing the handover failure rate and ping-pong handover rate by using the history information like Region-Domain, Time-Domain and Time To Trigger.[[sponsorship]]Tamkang University[[incitationindex]]EI[[conferencetype]]國際[[conferencetkucampus]]台北校園[[conferencedate]]20150902~20150903[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

VIRTUALIZED BASEBAND UNITS CONSOLIDATION IN ADVANCED LTE NETWORKS USING MOBILITY- AND POWER-AWARE ALGORITHMS

Virtualization of baseband units in Advanced Long-Term Evolution networks and a rapid performance growth of general purpose processors naturally raise the interest in resource multiplexing. The concept of resource sharing and management between virtualized instances is not new and extensively used in data centers. We adopt some of the resource management techniques to organize virtualized baseband units on a pool of hosts and investigate the behavior of the system in order to identify features which are particularly relevant to mobile environment. Subsequently, we introduce our own resource management algorithm specifically targeted to address some of the peculiarities identified by experimental results

Energy Efficiency and Quality of Services in Virtualized Cloud Radio Access Network

Cloud Radio Access Network (C-RAN) is being widely studied for soft and green fifth generation of Long Term Evolution - Advanced (LTE-A). The recent technology advancement in network virtualization function (NFV) and software defined radio (SDR) has enabled virtualization of Baseband Units (BBU) and sharing of underlying general purpose processing (GPP) infrastructure. Also, new innovations in optical transport network (OTN) such as Dark Fiber provides low latency and high bandwidth channels that can support C-RAN for more than forty-kilometer radius. All these advancements make C-RAN feasible and practical. Several virtualization strategies and architectures are proposed for C-RAN and it has been established that C-RAN offers higher energy efficiency and better resource utilization than the current decentralized radio access network (D-RAN). This project studies proposed resource utilization strategy and device a method to calculate power utilization. Then proposes and analyzes a new resource management and virtual BBU placement strategy for C-RAN based on demand prediction and inter-BBU communication load. The new approach is compared with existing state of art strategies with same input scenarios and load. The trade-offs between energy efficiency and quality of services is discussed. The project concludes with comparison between different strategies based on complexity of the system, performance in terms of service availability and optimization efficiency in different scenarios

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

Predictive and core-network efficient RRC signalling for active state handover in RANs with control/data separation

Frequent handovers (HOs) in dense small cell deployment scenarios could lead to a dramatic increase in signalling overhead. This suggests a paradigm shift towards a signalling conscious cellular architecture with intelligent mobility management. In this direction, a futuristic radio access network with a logical separation between control and data planes has been proposed in research community. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This approach enables signalling efficient HO procedures, since the control plane remains unchanged when the users move within the footprint of the same umbrella. Considering this configuration, we propose a core-network efficient radio resource control (RRC) signalling scheme for active state HO and develop an analytical framework to evaluate its signalling load as a function of network density, user mobility and session characteristics. In addition, we propose an intelligent HO prediction scheme with advance resource preparation in order to minimise the HO signalling latency. Numerical and simulation results show promising gains in terms of reduction in HO latency and signalling load as compared with conventional approaches

A Moving Direction and Historical Information Assisted Fast Handover in LTE-A

Handover is one of the critical features in mobility management of Long Term Evolution Advanced (LTE-A) wireless systems. It allows the User Equipment (UE) to roam between LTE-A wireless networks. LTE-A is purely on hard handover, which may cause loss data if the handover is not fast. In this paper, an advanced technique proposed which combined between the current UE moving direction and its history information. Our proposed tracks the UE positions to discover its direction. When the UE is being near to handover area the UE starts searching in its history to return back the target cell. If the UE trajectory does not exist in its history then the UE and its serving cell start searching for target cell through using cosine function in order to select target cell.  Our proposed technique is expected to increase the throughput, reduce the packet delay and loss, and reduce the frequent handovers

Regressive Prediction Approach to Vertical Handover in Fourth Generation Wireless Networks

The over increasing demand for deployment of wireless access networks has made wireless mobile devices to face so many challenges in choosing the best suitable network from a set of available access networks. Some of the weighty issues in 4G wireless networks are fastness and seamlessness in handover process. This paper therefore, proposes a handover technique based on movement prediction in wireless mobile (WiMAX and LTE-A) environment. The technique enables the system to predict signal quality between the UE and Radio Base Stations (RBS)/Access Points (APs) in two different networks. Prediction is achieved by employing the Markov Decision Process Model (MDPM) where the movement of the UE is dynamically estimated and averaged to keep track of the signal strength of mobile users. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The performances of various handover approaches influenced by different metrics (mobility velocities) were evaluated. The results presented demonstrate good accuracy the proposed method was able to achieve in predicting the next signal level by reducing the total handover latency

Spectrum Sensing with VSS-NLMS Process in Femto/Macro-cell Environments

Handover is a process that allows a mobile node to change its attachment point. A mobile node connected to a network can, in order to improve the quality of service, have the need to leave it to connect to a cell either of the same network or of a new network. The present paper introduce three techniques using adaptive Variable Step-Size Least Mean Square (VSSLMS) filter combined with spectrum sensing probability method to detect the triggering of handover in heterogeneous LTE networks. These techniques are Normalized LMS (NLMS), Kwong-NLMS and Li-NLMS. The simulation environment is composed of two femtocells belonging to a macrocell. Five User Equipements (UEs) are positioned in one femtocell and are assumed closest to its circumference. Simulation results show that sensing probability with Li-NLMS algorithm has a better performance compared with classical NLMS and Kwong-NLMS

Cell Selection Mechanism Based on Q-learning Environment in Femtocell LTE-A Networks

Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased