Performance analysis of the interference adaptation dynamic channel allocation technique in wireless communication networks

Dynamic channel allocation (DCA) problem is one of the major research topics in the wireless networking area. The purpose of this technique is to relieve the contradiction between the increasing traffic load in wireless networks and the limited bandwidth resource across the air interface. The challenge of this problem comes from the following facts: a) even the basic DCA problem is shown to be NP-complete (none polynomial complete); b) the size of the state space of the problem is very large; and c) any practical DCA algorithm should run in real-time. Many heuristic DCA schemes have been proposed in the literature. It has been shown through simulation results that the interference adaptive dynamic channel allocation (IA-DCA) scheme is a promising strategy in Time Devision [sic] Multiple Accesss/Frequency Devision [sic] Multiple Accesss [sic] (TDMA/FDMA) based wireless communication systems. However, the analytical work on the IA-DCA strategy in the literature is nearly blank. The performance of a, DCA algorithm in TDMA/FDMA wireless systems is influenced by three factors: representation of the interference, traffic fluctuation, and the processing power of the algorithm. The major obstacle in analyzing IA-DCA is the computation of co-channel interference without the constraint of conventional channel reuse factors. To overcome this difficulty, one needs a representation pattern which can approximate the real interference distribution as accurately as desired, and is also computationally viable. For this purpose, a concept called channel reuse zone (CRZ) is introduced and the methodology of computing the area of a CRZ with an arbitrary, non-trivial channel reuse factor is defined. Based on this new concept, the computation of both downlink and uplink CO-channel interference is investigated with two different propagation models, namely a simplified deterministic model and a shadowing model. For the factor of the processing power, we proposed an idealized Interference Adaptation Maximum Packing (IAMP) scheme, which gives the upper bound of all IA-DCA schemes in terms of the system capacity. The effect of traffic dynamics is delt [sic] with in two steps. First, an asymptotic performance bound for the IA-DCA strategy is derived with the assumption of an arbitrarily large number of channels in the system. Then the performance bound for real wireless systems with the IA-DCA strategy is derived by alleviating this assumption. Our analytical result is compared with the performance bound drawn by Zander and Eriksson for reuse-partitioning DCA1 and some simulation results for IA-DCA in the literature. It turns out that the performance bound obtained in this work is much tighter than Zander and Eriksson\u27s bound and is in agreement with simulation results. 1only available for deterministic propagation model and downlink connection

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 dynamic channel allocation in mobile computing system

Channel allocation problem is one of the most important issues in mobile computing networks. The purpose of this thesis is to develop a new distributed dynamic channel allocation algorithm. The proposed algorithm attempts to reuse channels in different cells to optimize the channel usage. It also assigns a large number of channels to the heavily loaded cells, and a few channels to the lightly loaded cells according to the traffic patterns of mobile computing network in real time. Co-channel interference is prevented in this algorithm. Moreover, the proposed algorithm is deadlock-free, interference-free, and achieves that maximum channel utilization

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

Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters

Connection admission control and packet scheduling for IEEE 802.16 networks

Includes bibliographical references.The IEEE 802.16 standard introduced as one of the Wireless Metropolitan Area Networks (WMAN) for Broadband Wireless Access (BWA) which is known as Worldwide Interoperability for Microwave Access (WiMAX), provides a solution of broadband connectivity to areas where wired infrastructure is economically and technically infeasible. Apart from the advantage of having high speeds and low costs, IEEE 802.16 has the capability to simultaneously support various service types with required QoS characteristics. ... While IEEE 802.16 standard defines medium access control (MAC) and physical (PHY) layers specification, admission control and packet scheduling mechanisms which are important elements of QoS provisioning are left to vendors to design and implement for service differentiation and QoS support