Comparitive Evaluation of Geometric Dynamic Channel Allocation Over Other Channel Allocation Schemes

Wireless services are one of the strongest growth areas in telecommunications today. Cellular voice is well established as a high-end service in most areas, but demand is increasing rapidly. In cellular systems, the geographical region is split using a regular topology, into cells each containing one base station. The base station should assign a channel that is not currently used within some specified distance. There are many kinds of channel assignment methods used in mobile communication starting with Fixed Channel Assignment (FCA), through Dynamic Channel Assignment (DCA) and Hybrid Channel Assignment (HCA). Personal Communication Services (PCS's) have been introduced as a mass-market phone service. The capacity, however, is now a critical issue for all of these services. The solution to the increasing spectrum efficiency demand in Personal Communication Services (PCS's) is the implementation of Dynamic Channel Allocation (DCA) strategy with distributed control. This thesis concentrates on one ,specific class of dynamic channel allocation called the Geometric Dynamic Channel Allocation (GDCA). The main feature of the GDCA lies in its ability to organise the dynamic resource assignment so that the resulting carrier usage pattern resembles what corresponds to other strategies , as long as that is compatible with the offered traffic pattern. Besides that, the overall performance advantage of GDCA over other strategies increases , as the offered traffic becomes larger

A distributed channel allocation scheme for cellular network using intelligent software agents

PhDAbstract not availabl

Efficient admission control schemes in cellular IP networks

The rapid growth of real-time multimedia applications over IP (Internet Protocol) networks has made the Quality of Service (QoS) a critical issue. One important factor affecting the QoS in the overall IP networks is the admission control in the fast expanding wireless IP networks. Due to the limitations of wireless bandwidth, wireless IP networks (cellular IP networks in particular) are generally considered to be the bottlenecks of the global IP networks. Admission control is to maintain the QoS level for the services admitted. It determines whether to admit or reject a new call request in the mobile cell based on the availability of the bandwidth. In this thesis, the term “call” is for general IP services including voice calls (VoIP) and the term “wireless IP” is used interchangeably with “cellular IP”, which means “cellular or mobile networks supporting IP applications”. In the wireless IP networks, apart from new calls, there are handoff (handover) calls which are calls moving from one cell to another. The general admission control includes the new call admission control and handoff call admission control. The desired admission control schemes should have the QoS maintained in specified levels and network resources (i.e. bandwidth in this case) are utilised efficiently. The study conducted in this thesis is on reviewing current admission control schemes and developing new schemes. Threshold Access Sharing (TAS) scheme is one of the existing schemes with good performance on general call admission. Our work started with enhancing TAS. We have proposed an improved Threshold Access Sharing (iTAS) scheme with the simplified ratebased borrowing which is an adaptive mechanism. The iTAS aims to lower handoff call dropping probability and to maximise the resource utilisation. The scheme works at the cell level (i.e. it is applied at the base station), on the basis of reserving a fixed amount of bandwidth for handoff calls. Prioritised calls can be admitted by “borrowing” bandwidth from other ongoing calls. Our simulation has shown that the new scheme has outperformed the original TAS in terms of handoff prioritisation and handling, especially for bandwidth adaptive calls. However, in iTAS, the admission decision is made solely based on bandwidth related criteria. All calls of same class are assumed having similar behaviour. In the real situation, many factors can be referred in decision making of the admission control, especially the handoff call handling. We have proposed a novice scheme, which considered multiple criteria with different weights. The total weights are used to make a decision for a handoff. These criteria are hard to be modelled in the traditional admission models. Our simulated result has demonstrated that this scheme yields better performance in terms of handoff call xiv dropping compared with iTAS. We further expand the coverage of the admission control from a cell level to a system level in the hierarchical networks. A new admission control model was built, aiming to optimise bandwidth utilisation by separating the signalling channels and traffic channels in different tiers. In the new model, handoff calls are also prioritised using call classification and admission levels. Calls belonging to a certain class follow a pre-defined admission rule. The admission levels can be adjusted to suit the traffic situation in the system. Our simulated results show that this model works better than the normal 2-tier hierarchical networks in terms of handoff calls. The model settings are adjustable to reflect real situation. Finally we conclude our research and suggest some possible future work

Cellular radio networks systems engineering.

by Kwan Lawrence Yeung.Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 115-[118]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Cellular Concept --- p.1Chapter 1.2 --- Fixed Channel Assignment --- p.2Chapter 1.3 --- Dynamic Channel Assignment --- p.2Chapter 1.4 --- Performance Evaluation of DC A --- p.3Chapter 1.5 --- Han doff Analysis --- p.3Chapter 1.6 --- Mobile Location Tracking Strategies --- p.3Chapter 1.7 --- QOS Measure --- p.4Chapter 1.8 --- Organization of Thesis --- p.4Chapter 2 --- Optimization of Channel Assignment I --- p.6Chapter 2.1 --- Introduction --- p.6Chapter 2.2 --- Generating Compact Patterns --- p.7Chapter 2.2.1 --- Regular size cells --- p.7Chapter 2.2.2 --- Irregular size cells --- p.7Chapter 2.3 --- Nominal Channel Allocation Methods --- p.10Chapter 2.3.1 --- Compact pattern allocation --- p.10Chapter 2.3.2 --- Greedy allocation --- p.11Chapter 2.3.3 --- Hybrid allocation --- p.11Chapter 2.3.4 --- The K-Optimal variations --- p.11Chapter 2.3.5 --- Backtracking strategies --- p.12Chapter 2.4 --- Performance Comparison --- p.12Chapter 2.5 --- Conclusions --- p.16Chapter 3 --- Optimization of Channel Assignment II --- p.18Chapter 3.1 --- Introduction --- p.18Chapter 3.2 --- Basic Heuristics --- p.20Chapter 3.2.1 --- Two methods for cell ordering --- p.20Chapter 3.2.2 --- Two channel assignment strategies --- p.20Chapter 3.3 --- Channel Assignments with Cell Re-ordering --- p.21Chapter 3.3.1 --- Four channel assignment algorithms --- p.21Chapter 3.3.2 --- Complexity --- p.22Chapter 3.3.3 --- An example --- p.22Chapter 3.4 --- Channel Assignment at Hotspots --- p.23Chapter 3.4.1 --- Strategy F vs strategy R --- p.23Chapter 3.4.2 --- Strategy FR --- p.24Chapter 3.5 --- Numerical Examples --- p.25Chapter 3.5.1 --- "Performance of algorithms F/CR,F/DR,R/CR and R/DR" --- p.26Chapter 3.5.2 --- Effect of X & Y on performance of algorithms FR/CR & FR/DR --- p.26Chapter 3.5.3 --- Performance of algorithms FR/CR & FR/DR --- p.27Chapter 3.6 --- Conclusions --- p.27Chapter 4 --- Compact Pattern Based DCA --- p.29Chapter 4.1 --- Introduction --- p.29Chapter 4.2 --- Compact Pattern Channel Assignment --- p.30Chapter 4.2.1 --- Data structures --- p.30Chapter 4.2.2 --- Two functions --- p.31Chapter 4.2.3 --- Two phases --- p.32Chapter 4.3 --- Performance Evaluation --- p.33Chapter 4.4 --- Conclusions --- p.36Chapter 5 --- Cell Group Decoupling Analysis --- p.37Chapter 5.1 --- Introduction --- p.37Chapter 5.2 --- One-Dimensional Cell Layout --- p.38Chapter 5.2.1 --- Problem formulation --- p.38Chapter 5.2.2 --- Calculation of blocking probability --- p.39Chapter 5.3 --- Two-Dimensional Cell Layout --- p.41Chapter 5.3.1 --- Problem formulation --- p.41Chapter 5.3.2 --- Calculation of blocking probability --- p.42Chapter 5.4 --- Illustrative Examples --- p.42Chapter 5.4.1 --- One-dimensional case --- p.42Chapter 5.4.2 --- Two-dimensional case --- p.45Chapter 5.5 --- Conclusions --- p.45Chapter 6 --- Phantom Cell Analysis --- p.49Chapter 6.1 --- Introduction --- p.49Chapter 6.2 --- Problem Formulation --- p.49Chapter 6.3 --- Arrival Rates in Phantom Cells --- p.50Chapter 6.4 --- Blocking Probability and Channel Occupancy Distribution --- p.51Chapter 6.4.1 --- Derivation of α --- p.51Chapter 6.4.2 --- Derivation of Bside --- p.52Chapter 6.4.3 --- Derivation of Bopp --- p.53Chapter 6.4.4 --- Channel occupancy distribution --- p.54Chapter 6.5 --- Numerical Results --- p.55Chapter 6.6 --- Conclusions --- p.55Chapter 7 --- Performance Analysis of BDCL Strategy --- p.58Chapter 7.1 --- Introduction --- p.58Chapter 7.2 --- Borrowing with Directional Carrier Locking --- p.58Chapter 7.3 --- Cell Group Decoupling Analysis --- p.59Chapter 7.3.1 --- Linear cellular systems --- p.59Chapter 7.3.2 --- Planar cellular systems --- p.61Chapter 7.4 --- Phantom Cell Analysis --- p.61Chapter 7.4.1 --- Call arrival rates in phantom cells --- p.62Chapter 7.4.2 --- Analytical model --- p.62Chapter 7.5 --- Numerical Examples --- p.63Chapter 7.5.1 --- Linear cellular system with CGD analysis --- p.63Chapter 7.5.2 --- Planar cellular system with CGD analysis --- p.65Chapter 7.5.3 --- Planar cellular system with phantom cell analysis --- p.65Chapter 7.6 --- Conclusions --- p.68Chapter 8 --- Performance Analysis of Directed Retry --- p.69Chapter 8.1 --- Introduction --- p.69Chapter 8.2 --- Directed Retry Strategy --- p.69Chapter 8.3 --- Blocking Performance of Directed Retry --- p.70Chapter 8.3.1 --- Analytical model --- p.70Chapter 8.3.2 --- Numerical examples --- p.71Chapter 8.4 --- HandofF Analysis for Directed Retry --- p.73Chapter 8.4.1 --- Analytical model --- p.73Chapter 8.4.2 --- Numerical examples --- p.75Chapter 8.5 --- Conclusions --- p.77Chapter 9 --- Handoff Analysis in a Linear System --- p.79Chapter 9.1 --- Introduction --- p.79Chapter 9.2 --- Traffic Model --- p.80Chapter 9.2.1 --- Call arrival rates --- p.80Chapter 9.2.2 --- Channel holding time distribution --- p.81Chapter 9.3 --- Analytical Model --- p.81Chapter 9.3.1 --- Handoff probability --- p.81Chapter 9.3.2 --- Handoff call arrival rate --- p.81Chapter 9.3.3 --- Derivation of blocking probability --- p.81Chapter 9.3.4 --- Handoff failure probability --- p.82Chapter 9.3.5 --- Finding the optimal number of guard channels --- p.83Chapter 9.4 --- Numerical Results --- p.83Chapter 9.4.1 --- System parameters --- p.83Chapter 9.4.2 --- Justifying the analysis --- p.84Chapter 9.4.3 --- The effect of the number of guard channels --- p.84Chapter 9.5 --- Conclusions --- p.85Chapter 10 --- Mobile Location Tracking Strategy --- p.88Chapter 10.1 --- Introduction --- p.88Chapter 10.2 --- Review of Location Tracking Strategies --- p.89Chapter 10.2.1 --- Fixed location area strategy --- p.89Chapter 10.2.2 --- Fixed reporting center strategy --- p.89Chapter 10.2.3 --- Intelligent paging strategy --- p.89Chapter 10.2.4 --- Time-based location area strategy --- p.89Chapter 10.2.5 --- Movement-based location area strategy --- p.90Chapter 10.2.6 --- Distance-based location area strategy --- p.90Chapter 10.3 --- Optimization of Location Area Size --- p.90Chapter 10.3.1 --- Location updating rates ´ؤ linear systems --- p.90Chapter 10.3.2 --- Location updating rates ´ؤ planar systems --- p.91Chapter 10.3.3 --- Optimal location area size ´ؤ linear systems --- p.92Chapter 10.3.4 --- Optimal location area size ´ؤ planar systems --- p.92Chapter 10.4 --- Comparison of FLA & DBLA Strategies --- p.93Chapter 10.5 --- Adaptive Location Tracking Strategy --- p.94Chapter 10.5.1 --- Mobility tracking --- p.94Chapter 10.5.2 --- Protocols for ALT strategy --- p.94Chapter 10.6 --- Numerical Examples --- p.95Chapter 10.7 --- Conclusions --- p.97Chapter 11 --- A New Quality of Service Measure --- p.99Chapter 11.1 --- Introduction --- p.99Chapter 11.2 --- QOS Measures --- p.99Chapter 11.3 --- An Example --- p.101Chapter 11.4 --- Case Studies --- p.101Chapter 11.5 --- Conclusions --- p.106Chapter 12 --- Discussions & Conclusions --- p.107Chapter 12.1 --- Summary of Results --- p.107Chapter 12.2 --- Topics for Future Research --- p.108Chapter A --- Borrowing with Directional Channel Locking Strategy --- p.110Chapter B --- Derivation of p2 --- p.112Chapter C --- Publications Derived From This Thesis --- p.114Bibliography --- p.11

Distributed Simplicial Homology Based Load Balancing Algorithm for Cellular Networks

International audience—In this paper, we introduce a distributed load balancing algorithm for cellular networks. Traffic load in cellular networks is sometimes unbalanced. Some cells are overloaded, while others remain free. Simplicial homology is a tool from algebraic topology that allows to compute the coverage of a network by using only simple matrix computations. Our algorithm, which is based on simplicial homology, controls the transmission power of each cell in the network, not only to satisfy the coverage constraint, but also to redirect users from the overloaded cells to the underloaded ones. As a result, the traffic load of the cellular network is more balanced. The simulation results show that this algorithm improves the capacity of the whole network by 2.3% when the user demand is fast varying

FAULT-TOLERANT DISTRIBUTED CHANNEL ALLOCATION ALGORITHMS FOR CELLULAR NETWORKS

In cellular networks, channels should be allocated efficiently to support communication betweenmobile hosts. In addition, in cellular networks, base stations may fail. Therefore, designing a faulttolerantchannel allocation algorithm is important. That is, the algorithm should tolerate failuresof base stations. Many existing algorithms are neither fault-tolerant nor efficient in allocatingchannels.We propose channel allocation algorithms which are both fault-tolerant and efficient. In theproposed algorithms, to borrow a channel, a base station (or a cell) does not need to get channelusage information from all its interference neighbors. This makes the algorithms fault-tolerant,i.e., the algorithms can tolerate base station failures, and perform well in the presence of thesefailures.Channel pre-allocation has effect on the performance of a channel allocation algorithm. Thiseffect has not been studied quantitatively. We propose an adaptive channel allocation algorithmto study this effect. The algorithm allows a subset of channels to be pre-allocated to cells. Performanceevaluation indicates that a channel allocation algorithm benefits from pre-allocating allchannels to cells.Channel selection strategy also inuences the performance of a channel allocation algorithm.Given a set of channels to borrow, how a cell chooses a channel to borrow is called the channelselection problem. When choosing a channel to borrow, many algorithms proposed in the literaturedo not take into account the interference caused by borrowing the channel to the cells which havethe channel allocated to them. However, such interference should be considered; reducing suchinterference helps increase the reuse of the same channel, and hence improving channel utilization.We propose a channel selection algorithm taking such interference into account.Most channel allocation algorithms proposed in the literature are for traditional cellular networkswith static base stations and the neighborhood relationship among the base stations is fixed.Such algorithms are not applicable for cellular networks with mobile base stations. We proposea channel allocation algorithm for cellular networks with mobile base stations. The proposedalgorithm is both fault-tolerant and reuses channels efficiently.KEYWORDS: distributed channel allocation, resource planning, fault-tolerance, cellular networks,3-cell cluster model

A Socio-inspired CALM Approach to Channel Assignment Performance Prediction and WMN Capacity Estimation

A significant amount of research literature is dedicated to interference mitigation in Wireless Mesh Networks (WMNs), with a special emphasis on designing channel allocation (CA) schemes which alleviate the impact of interference on WMN performance. But having countless CA schemes at one's disposal makes the task of choosing a suitable CA for a given WMN extremely tedious and time consuming. In this work, we propose a new interference estimation and CA performance prediction algorithm called CALM, which is inspired by social theory. We borrow the sociological idea of a "sui generis" social reality, and apply it to WMNs with significant success. To achieve this, we devise a novel Sociological Idea Borrowing Mechanism that facilitates easy operationalization of sociological concepts in other domains. Further, we formulate a heuristic Mixed Integer Programming (MIP) model called NETCAP which makes use of link quality estimates generated by CALM to offer a reliable framework for network capacity prediction. We demonstrate the efficacy of CALM by evaluating its theoretical estimates against experimental data obtained through exhaustive simulations on ns-3 802.11g environment, for a comprehensive CA test-set of forty CA schemes. We compare CALM with three existing interference estimation metrics, and demonstrate that it is consistently more reliable. CALM boasts of accuracy of over 90% in performance testing, and in stress testing too it achieves an accuracy of 88%, while the accuracy of other metrics drops to under 75%. It reduces errors in CA performance prediction by as much as 75% when compared to other metrics. Finally, we validate the expected network capacity estimates generated by NETCAP, and show that they are quite accurate, deviating by as low as 6.4% on an average when compared to experimentally recorded results in performance testing

Efficient Algorithms and Framework for Bandwidth Allocation, Quality-of-Service Provisioning and Location Management in Mobile Wireless Computing

The fusion of computers and communications has promised to herald the age of information super-highway over high speed communication networks where the ultimate goal is to enable a multitude of users at any place, access information from anywhere and at any time. This, in a nutshell, is the goal envisioned by the Personal Communication Services (PCS) and Xerox's ubiquitous computing. In view of the remarkable growth of the mobile communication users in the last few years, the radio frequency spectrum allocated by the FCC (Federal Communications Commission) to this service is still very limited and the usable bandwidth is by far much less than the expected demand, particularly in view of the emergence of the next generation wireless multimedia applications like video-on-demand, WWW browsing, traveler information systems etc. Proper management of available spectrum is necessary not only to accommodate these high bandwidth applications, but also to alleviate problems due to sudden explosion of traffic in so called hot cells. In this dissertation, we have developed simple load balancing techniques to cope with the problem of tele-traffic overloads in one or more hot cells in the system. The objective is to ease out the high channel demand in hot cells by borrowing channels from suitable cold cells and by proper assignment (or, re-assignment) of the channels among the users. We also investigate possible ways of improving system capacity by rescheduling bandwidth in case of wireless multimedia traffic. In our proposed scheme, traffic using multiple channels releases one or more channels to increase the carried traffic or throughput in the system. Two orthogonal QoS parameters, called carried traffic and bandwidth degradation, are identified and a cost function describing the total revenue earned by the system from a bandwidth degradation and call admission policy, is formulated. A channel sharing scheme is proposed for co-existing real-time and non-real-time traffic and analyzed using a Markov modulated Poisson process (MMPP) based queueing model. The location management problem in mobile computing deals with the problem of a combined management of location updates and paging in the network, both of which consume scarce network resources like bandwidth, CPU cycles etc. An easily implementable location update scheme is developed which considers per-user mobility pattern on top of the conventional location area based approach and computes an update strategy for each user by minimizing the average location management cost. The cost optimization problem is elegantly solved using a genetic algorithm

Load balancing using cell range expansion in LTE advanced heterogeneous networks

The use of heterogeneous networks is on the increase, fueled by consumer demand for more data. The main objective of heterogeneous networks is to increase capacity. They offer solutions for efficient use of spectrum, load balancing and improvement of cell edge coverage amongst others. However, these solutions have inherent challenges such as inter-cell interference and poor mobility management. In heterogeneous networks there is transmit power disparity between macro cell and pico cell tiers, which causes load imbalance between the tiers. Due to the conventional user-cell association strategy, whereby users associate to a base station with the strongest received signal strength, few users associate to small cells compared to macro cells. To counter the effects of transmit power disparity, cell range expansion is used instead of the conventional strategy. The focus of our work is on load balancing using cell range expansion (CRE) and network utility optimization techniques to ensure fair sharing of load in a macro and pico cell LTE Advanced heterogeneous network. The aim is to investigate how to use an adaptive cell range expansion bias to optimize Pico cell coverage for load balancing. Reviewed literature points out several approaches to solve the load balancing problem in heterogeneous networks, which include, cell range expansion and utility function optimization. Then, we use cell range expansion, and logarithmic utility functions to design a load balancing algorithm. In the algorithm, user and base station associations are optimized by adapting CRE bias to pico base station load status. A price update mechanism based on a suboptimal solution of a network utility optimization problem is used to adapt the CRE bias. The price is derived from the load status of each pico base station. The performance of the algorithm was evaluated by means of an LTE MATLAB toolbox. Simulations were conducted according to 3GPP and ITU guidelines for modelling heterogeneous networks and propagation environment respectively. Compared to a static CRE configuration, the algorithm achieved more fairness in load distribution. Further, it achieved a better trade-off between cell edge and cell centre user throughputs. [Please note: this thesis file has been deferred until December 2016