Mobility Management in 4G Networks

Over the past 25 years, the evolution of the internet and the advances of wireless technologies have made a tremendous impact on lifestyle of people around the world. Together, these two factors have changed the way people communicate, work, and get their entertainment. In order to be always best connected for various applications, the network selection procedure in heterogeneous multi-access environment during vertical handover decision is intended to choose the most suitable network for mobile user. In this paper, a performance study using the fuzzy logic concept is done and the integration of UMTS and WiMAX network is taken as an example to show that the proposed vertical handoff decision algorithm is able to determine when a handoff is required, and selects the best access network that is optimized to network conditions, quality of service requirements, received signal strength, bandwidth requirements and user preferences

Group Mobility Detection and User Connectivity Models for Evaluation of Mobile Network Functions

Group mobility in mobile networks is responsible for dynamic changes in user accesses to base stations, which eventually lead to degradation of network quality of service (QoS). In particular, the rapid movement of a dense group of users intensively accessing the network, such as passengers on a train passing through a densely populated area, significantly affects the perceived network QoS. For better design and operation of mobile network facilities and functions in response to this issue, monitoring group mobility and modeling the access patterns in group mobility scenarios are essential. In this paper, we focus on fast and dense group mobility and mobile network signaling data (control-plane data), which contains information related to mobility and connectivity. Firstly, we develop a lightweight method of group mobility detection to extract train passengers from all users\u27 signaling data without relying on precise location information about users, e.g., based on GPS. Secondly, based on the same signaling data and the results obtained by the detection method, we build connected/idle duration models for train users and non-train users. Finally, we leverage these models in mobile network simulations to assess the effectiveness of a dynamic base station switching/orientation scheme to mitigate QoS degradation with low power consumption in a group mobility scenario. The obtained models reveal that train users consume 3.5 times more resources than non-train users, which proves that group mobility has a significant effect on mobile networks. The simulation results show that the dynamic scheme of base station improves users\u27 perceived throughput, latency and jitter with small amount of additional power consumption in case of a moderate number of train users, but its ineffectiveness with larger number of train users is also shown. This would suggest that group mobility detection and the obtained connection/idle duration models based solely on control-plane data analytics are usable and useful for the development of mobility-aware functions in base stations

Mobility Management and Congestion Control in Wireless Mesh Networks

Today, wireless mesh networks are increasingly popular. In order to be better adapted to the increasing number of offered services in telecommunications, many Quality of Service (QoS) problems are being considered. Some of the important issues are: admission control, congestion control, and handoff management of the network. Our research focuses on those issues individually and combining them together in order to find solutions to enhance the quality of service provided to each user as demanded in their SLA. A novel Markov Decision-based Admission Control and Routing (MDACR) algorithm is proposed. The MDACR algorithm finds a sub-optimal solution using the value iteration method. Admission rate increases for both types of user associations (handoff and new user association request), which is addressed by a proposed multi-homing admission and routing algorithm. This algorithm associates the user with two different access points. This is beneficial in a highly congested network, which permits a new routing metric to assure seamless handoff in the network. When a user is moving, MDACR algorithm finds a maximally jointed route with the old route, which decreases the handoff delay. Another aspect is considered in order to improve the QoS in WMN, which is the congestion control, a novel proactive approach is proposed. Where a Variable Order Markov (VOM) prediction model is introduced to predict the congestion status in each link in the network, a new route is established for the traffic based on the output of the VOM model, and the transmission rate is adjusted based on the link congestion status to increase the overall user satisfaction. Sub-optimal model is introduced and solved using Lagrange method. Based on the predicted link congestion, rerouting algorithm is implemented in order to insure load balancing and to mitigate congestion over WMN network. Our ultimate goal is to improve the QoS in WMN by dealing individually with the issues stated above and try to combine them together and provide QoS framework which deals with many types of services