Location management in cellular networks using soft computing algorithms

The enormous increase in mobile subscribers in recent years has resulted in exploitation of wireless network resources, in particular, the bandwidth available. For the efficient use of the limited available bandwidth and to increase the capacity of the network, frequency re-use concept is adopted in cellular networks which led to increased number of cells in the network. This led to difficulty in finding the location of a mobile user in the network and increase in the signalling cost. Location management deals with keeping track of an active mobile terminal in a specific area while minimizing the cost incurred in finding the mobile terminal. The existing location management is done by grouping the cells based on subscriber density. Location management strategies are based on user mobility and incoming call arrival rate to a mobile terminal, which implies that the location management cost comprises of location update cost and paging cost. Reporting cell planning is an efficient location management scheme wherein few cells in the network as assigned as reporting cells, which take the responsibility of managing the location update and paging procedures in the network. Therefore, the need of the hour is to determine an optimal reporting cell configuration where the location management cost is reduced and thereby maintaining a trade-off between location update and paging cost. The reporting cell discrete optimization problem is solved using genetic algorithm, swarm intelligence technique and differential evolution. A comparative study of these techniques with the algorithms implemented by other researchers is done. It is observed that binary differential evolution outperforms other optimization techniques used for cost optimization. The current work can be extended to dynamic location management to assign and manage reporting cells in real-time implementable fashion

Learning-based tracking area list management in 4G and 5G networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMobility management in 5G networks is a very challenging issue. It requires novel ideas and improved management so that signaling is kept minimized and far from congesting the network. Mobile networks have become massive generators of data and in the forthcoming years this data is expected to increase drastically. The use of intelligence and analytics based on big data is a good ally for operators to enhance operational efficiency and provide individualized services. This work proposes to exploit User Equipment (UE) patterns and hidden relationships from geo-spatial time series to minimize signaling due to idle mode mobility. We propose a holistic methodology to generate optimized Tracking Area Lists (TALs) in a per UE manner, considering its learned individual behavior. The k -means algorithm is proposed to find the allocation of cells into tracking areas. This is used as a basis for the TALs optimization itself, which follows a combined multi-objective and single-objective approach depending on the UE behavior. The last stage identifies UE profiles and performs the allocation of the TAL by using a neural network. The goodness of each technique has been evaluated individually and jointly under very realistic conditions and different situations. Results demonstrate important signaling reductions and good sensitivity to changing conditions.This work was supported by the Spanish National Science Council and ERFD funds under projects TEC2014-60258-C2-2-R and RTI2018-099880-B-C32.Peer ReviewedPostprint (author's final draft

Design of personalized location areas for future Pcs networks

In Global Systems for Mobile Communications (GSM), always-update location strategy is used to keep track of mobile terminals within the network. However future Personal Communication Networks (PCS) will require to serve a wide range of services (digital voice, video, data, and email) and also will have to support a large population of users. Under such demands, determining the exact location of a user by traditional strategies would be difficult and would result in increasing the signaling load imposed by location-update and paging procedures. The problem is not only in increasing cost, but also in non-efficient utilization of a precious resource, i.e., radio bandwidth; In this thesis, personalized Location Areas (PLAs) are formed considering the mobility patterns of individual users in the system such that the signaling due to location update and paging is minimized. We prove that the problem in this formulation is of NP complexity. Therefore we study efficient optimization techniques able to avoid combinatorial search. Three known classes of optimization techniques are studied. They are Simulated Annealing, Tabu Search and Genetic Search. Three algorithms are designed for solving the problem. Modeling does not assume any specific cell structure or network topology that makes the proposal widely applicable. The behavior of mobile terminals in the network is modeled as Random Walk with an absorbing state and the Markov chain is used for cost analysis; Numeric simulation carried out for 25 and 100 hexagonal cell networks have shown that Simulated Annealing based algorithm outperforms other two by indicators of the runtime complexity and signaling cost of location management. The ID\u27s of cells populating the calculated area are provided to the mobile terminal and saved in its local memory every time the mobile subscriber moves out its current location area. Otherwise, no location update is performed, but only paging. Thus, at the expense of small local memory, the location management is carried more efficiently

Location and resource management for quality of service provisioning in wireless/mobile networks

Wireless communication has been seen unprecedented growth in recent years. As the wireless network migrates from 2G to 2.5G and 3G, more and more high-bandwidth services have to be provided to wireless users. However, existing radio resources are limited, thus quality-of-service (QoS) provisioning is extremely important for high performance networKing In this dissertation, we focus on two problems crucial for QoS provisioning in wireless networks. They are location and resource management. Our research is aimed to develop efficient location management and resource allocation techniques to provide qualitative services in the future generations of wireless/mobile networks. First, the hybrid location update method (HLU) is proposed based on both the moving distance and the moving direction of mobile terminals. The signaling cost for location management is analyzed using a 2D Markov walk model. The results of numerical studies for different mobility patterns show that the HLU scheme outperforms the methods employing either moving distance or moving direction. Next, a new dynamic location management scheme with personalized location areas is developed. It takes into account terminal\u27s mobility characteristics in different locations of the service area. The location area is designed for each individual mobile user such that the location management cost is minimized. The cost is calculated based on a continuous-time Markov chain. Simulation results acknowledge a lower cost of the proposed scheme compared to that of some known techniques. Our research on the resource management considers the dynamic allocation strategy in the integrated voice/data wireless networks. We propose two new channel de-allocation schemes, i.e., de-allocation for data packet (DASP) and de-allocation for both voice call and data packet (DASVP). We then combine the proposed de-allocation methods with channel re-allocation, and evaluate the performance of the schemes using an analytic model. The results indicate the necessity of adapting to QoS requirements on both voice call and data packet. Finally, a new QoS-based dynamic resource allocation scheme is proposed which differentiates the new and handoff voice calls. The scheme combines channel reservation, channel de-allocation/re-allocation for voice call and packet queue to adapt to QoS requirements by adjusting the number of reserved channels and packet queue size. The superiority of the propose scheme in meeting the QoS requirements over existing techniques is proved by the experimental studies

Energy-efficient wireless communication

In this chapter we present an energy-efficient highly adaptive network interface architecture and a novel data link layer protocol for wireless networks that provides Quality of Service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations in bandwidth scheduling and error control are necessary to achieve energy efficiency and an acceptable quality of service. In our approach we apply adaptability through all layers of the protocol stack, and provide feedback to the applications. In this way the applications can adapt the data streams, and the network protocols can adapt the communication parameters

Enhanced distance-based location management of mobile communication systems using a cell coordinates approach

In managing the locations of mobile users in mobile communication systems, the distance-based strategy has been proven to have better performance than other dynamic strategies, but is difficult to implement. In this paper, a simple approach is introduced to implement the distance-based strategy by using the cell coordinates in calculating the physical distance traveled. This approach has the advantages of being independent of the size, shape, and distribution of cells, as well as catering for the direction of movement in addition to the speed of each mobile terminal. An enhanced distance-based location management strategy is proposed to dynamically adjust the size and shape of location area for each individual mobile terminal according to the current speed and direction of movement. It can reduce the location management signaling traffic of the distance-based strategy by half when mobile terminals have predictable directions of movement. Three types of location updating schemes are discussed, namely, Circular Location Area, Optimal Location Area, and Elliptic Location Area. Paging schemes using searching techniques such as expanding distance search based on the last reported location and based on the predicted location, and expanding direction search are also explored to further reduce paging signal traffic by partitioning location areas into paging areas.published_or_final_versio

Managing terminals mobility for personal communication systems.

by Lee Ying Kit.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 79-[83]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview of Personal Communication Systems --- p.1Chapter 1.2 --- Design issues on PCS --- p.2Chapter 1.2.1 --- Channel allocation --- p.2Chapter 1.2.2 --- Multiple Access --- p.3Chapter 1.2.3 --- Handoffs --- p.4Chapter 1.2.4 --- Location management --- p.6Chapter 1.3 --- Motivation of this thesis --- p.9Chapter 1.4 --- The theme of this thesis --- p.10Chapter 1.4.1 --- Methodology --- p.10Chapter 1.4.2 --- The system model and assumptions --- p.12Chapter 1.4.3 --- Outline of the thesis --- p.13Chapter 2 --- Overview of the traditional location update schemes --- p.15Chapter 2.1 --- Why do we need location registration? --- p.15Chapter 2.2 --- Location registration by Geographic and Time based methods --- p.16Chapter 2.2.1 --- Geographic Based Registration Schemes --- p.16Chapter 2.2.2 --- Time Based Registration Scheme --- p.20Chapter 2.3 --- Peformance Analysis of protocols --- p.20Chapter 2.3.1 --- Analytical Results --- p.22Chapter 2.3.2 --- A Numerical Study --- p.23Chapter 2.4 --- Summary of the results for time and geographic based location update protocol --- p.24Chapter 3 --- The Implementation of Bloom filter on location registration --- p.27Chapter 3.1 --- Introduction --- p.27Chapter 3.2 --- The Implementation of Bloom filter on location registration --- p.29Chapter 3.2.1 --- Location Update by Bloom filter --- p.29Chapter 3.2.2 --- Paging algorithm --- p.29Chapter 3.2.3 --- An example --- p.30Chapter 3.3 --- Performance evaluation of the Bloom filter based location update scheme --- p.32Chapter 3.4 --- Summary of the results for Bloom filter based scheme --- p.35Chapter 4 --- One-Bit-Reply protocol --- p.36Chapter 4.1 --- Introduction --- p.36Chapter 4.2 --- One-Bit-Reply protocol --- p.37Chapter 4.2.1 --- Grouping of MU's --- p.38Chapter 4.2.2 --- The Update Procedure --- p.39Chapter 4.2.3 --- Paging algorithm --- p.40Chapter 4.3 --- Performance evaluation of the OBR protocol --- p.42Chapter 4.3.1 --- Analytical Results --- p.42Chapter 4.3.2 --- A Simulation Study --- p.43Chapter 4.4 --- Comparison of the location registration schemes - A numerical study --- p.45Chapter 4.5 --- Summary --- p.46Chapter 5 --- A case study - Implementing the OBR protocol on GSM sytems --- p.49Chapter 5.1 --- Introduction --- p.49Chapter 5.2 --- The Architecture of Global System for Mobile Communicaitons (GSM) --- p.50Chapter 5.3 --- Location Update Procedure of GSM --- p.51Chapter 5.4 --- Implementing OBR protocol on GSM --- p.52Chapter 5.5 --- Influence of the OBR on the VLR's and HLR --- p.55Chapter 5.5.1 --- Analysis of traditional method --- p.57Chapter 5.5.2 --- Analysis of OBR --- p.58Chapter 5.6 --- Summary --- p.59Chapter 6 --- Conclusion --- p.61Chapter 6.1 --- Summaries of Results --- p.61Chapter 6.1.1 --- Cost functions --- p.61Chapter 6.1.2 --- Optimization of the cost functions --- p.62Chapter 6.1.3 --- Implementation of OBR into GSM --- p.64Chapter 6.2 --- Suggestions for further researches --- p.64Appendix --- p.65Chapter A --- Derivation of cost functions --- p.66Chapter A.1 --- Geographic based scheme --- p.66Chapter A.2 --- Time based scheme --- p.67Chapter A.3 --- Bloom filter based scheme --- p.68Chapter B --- On the optimality of the cost functions --- p.71Chapter B.1 --- Steepest Descent Algorithm for various protocols --- p.71Chapter B.2 --- Bloom filter based scheme --- p.72Chapter B.3 --- Time Based Scheme --- p.74Chapter B.4 --- One-Bit-Reply scheme --- p.75Chapter B.5 --- Geographic Based Scheme --- p.75Chapter C --- Simulation of OBR --- p.77Bibliography --- p.7

Intelligent Advancements in Location Management and C-RAN Power-Aware Resource Allocation

The evolving of cellular networks within the last decade continues to focus on delivering a robust and reliable means to cope with the increasing number of users and demanded capacity. Recent advancements of cellular networks such as Long-Term Evolution (LTE) and LTE-advanced offer a remarkable high bandwidth connectivity delivered to the users. Signalling overhead is one of the vital issues that impact the cellular behavior. Causing a significant load in the core network hence effecting the cellular network reliability. Moreover, the signaling overhead decreases the Quality of Experience (QoE) of users. The first topic of the thesis attempts to reduce the signaling overhead by developing intelligent location management techniques that minimize paging and Tracking Area Update (TAU) signals. Consequently, the corresponding optimization problems are formulated. Furthermore, several techniques and heuristic algorithms are implemented to solve the formulated problems. Additionally, network scalability has become a challenging aspect that has been hindered by the current network architecture. As a result, Cloud Radio Access Networks (C-RANs) have been introduced as a new trend in wireless technologies to address this challenge. C-RAN architecture consists of: Remote Radio Head (RRH), Baseband Unit (BBU), and the optical network connecting them. However, RRH-to-BBU resource allocation can cause a significant downgrade in efficiency, particularly the allocation of the computational resources in the BBU pool to densely deployed small cells. This causes a vast increase in the power consumption and wasteful resources. Therefore, the second topic of the thesis discusses C-RAN infrastructure, particularly where a pool of BBUs are gathered to process the computational resources. We argue that there is a need of optimizing the processing capacity in order to minimize the power consumption and increase the overall system efficiency. Consequently, the optimal allocation of computational resources between the RRHs and BBUs is modeled. Furthermore, in order to get an optimal RRH-to-BBU allocation, it is essential to have an optimal physical resource allocation for users to determine the required computational resources. For this purpose, an optimization problem that models the assignment of resources at these two levels (from physical resources to users and from RRHs to BBUs) is formulated