Reduce the probability of blocking for handoff and calls in cellular systems based on fixed and dynamic channel assignment

In cellular systems the high probability of blocking represents a big problem for users, The proposed solution by reducing the blocking probability and investigation cellular systems by method channels assignment. The aim from apaper is studying the effect the channel assignment on the value of blocking probability. The results showed that the fixe channeld assignment gives a large probability of blocking for high loads, While  (FCA) reduce probability of blocking for handoff and calls according to cluster size. The cellular system representation in the case of (DCA), in (3-cell reuse) and (7-cell reuse), the results showed the first best way to reduce blocking probability and lead to reduce to approximately zero when loads that are less than 200%. Increasing  the cluster size causes to reduce blocking  probability. the results showed that the probability blocking for handoff  less than from probability of  blocking for new calls

A quasi-static cluster-computing approach for dynamic channelassignment in cellular mobile communication systems

Efficient management of the radio spectrum can be accomplished by making use of channel assignment techniques, which work by allocating different channels of the spectrum to the cells of the network in a conflict-free manner (i.e., the co-channel interference is minimized). The problem of dynamically reallocating the channels in response to change in user location patterns, which occurs frequently for a microcell network architecture, is even more difficult to tackle in a timely manner. Most existing approaches use various sequential search-based heuristics which cannot produce high-quality allocation fast enough to cope with the frequent traffic requirement variations. In this paper, we propose a quasi-static approach which combines the merits of both static and dynamic schemes. The static component of our approach uses a parallel genetic algorithm to generate a suite of representative assignments based on a set of different estimated traffic scenarios. At on-line time, the dynamic component observes the actual traffic requirement and retrieves the representative assignment of the closest scenario from the off-line table. The retrieved assignment is then quickly refined by using a fast parallel local search algorithm. Our extensive simulation experiments have indicated that the proposed quasi-static system outperforms other dynamic channel assignment techniques significantly in terms of both blocking probabilities and computational overhead.published_or_final_versio

A Novel Load Balancing Scheme for Hot-spot Cells

The radio spectrum that is available to us is very limited. The cellular network works fine when the traffic conditions are normal or below normal. But when the cellular traffic increases the network cannot perform efficiently under this increasing traffic load as the radio spectrum to serve this increasing traffic is very limited. To avoid degradation and to increase performance of wireless cellular network frequency reuse and channel allocation techniques are used. The sole purpose of the channel allocation techniques is to allocate the available channel in such a way that the call blocking probability is reduced. In this paper we propose a HCA technique which will reduce the call blocking probability when the Cell becomes a hot spot i.e. the cellular traffic is beyond normal. These papers propose a novel load balancing scheme that will allocate channel to the overburden cell using hot spot notification. The HCA scheme is a combination of FCA and DCA scheme which effectively utilize the central pool for allocation of channels to the cells under heavy traffic. This HCA Scheme work like FCA in initial stages i.e. under low traffic levels and more like DCA at later stages i.e. high traffic levels and also reduces the Call blocking probability to great extent

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

A Comparative Study of Prioritized Handoff Schemes with Guard Channels in Wireless Cellular Networks

Mobility management has always been the main challenge in most mobile systems. It involves the management of network radio channel resource capacity for the purpose of achieving optimum quality of service (QoS) standard. In this era of wireless Personal Communication Networks such as Global System for Mobile Communication (GSM), Wireless Asynchronous Transfer Mode (WATM), Universal Mobile Telecommunication System (UMTS), there is a continuous increase in demand for network capacity. In order to accommodate the increased demand for network capacity (radio resource) over the wireless medium, cell sizes are reduced. As a result of such reduction in cell sizes, handoffs occur more frequently, and thereby result in increased volume of handoff related signaling. Therefore, a handoff scheme that can handle the increased signaling load while sustaining the standard QoS parameters is required.This work presents a comparative analysis of four popular developed handoff schemes. New call blocking probability, forced termination probability and throughput are the QoS parameters employed in comparing the four schemes. The four schemes are:RCS-GC,MRCS-GC, NCBS-GC, and APS-GC. NCBS-GChas the leased new call blocking probability while APS-GC has the worst. In terms of forced termination probability, MRCS-GC has the best result, whileRCS-GChas the worst scheme.MRCS-GC delivers the highest number of packets per second while APS-GC delivers the least. These performance metrics are computed by using the analytical expressions developed for these metrics in the considered models in a Microsoft Excel spreadsheet environment.http://dx.doi.org/10.4314/njt.v34i3.2

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

Development of resource allocation strategies based on cognitive radio

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