Models for call acceptance in a cellular network based on handoff guarantees
Bibliographic record
Abstract
Call admission control (CAC) is important for cellular wireless networks in order to provide quality of service (QoS) requirements to users.There are different static and adaptive call admission control schemes.The adaptive schemes are usually better than static ones, partly because the static schemes make an unrealistic assumption about the distribution of the handoff call arrival process.On the other hand, the adaptive schemes make a different unrealistic, but less severe) assumption about the distribution of the number of users in a cell.In a static scheme where new calls arrive according to the Poisson process and both call holding and cell residence times are exponentially distributed it is usually assumed that handoff arrivals follow Poisson process.However, this assumption of Poisson distributed handoff arrival process is not justified for a wireless network consisting of two or more cells.Conversely, the probability distribution of the number of calls in a cell is approximated as Gaussian or Poisson distribution in distributed schemes.In general, we find that the handoff process is better captured by a two- dimensional lVlarkov chain for single class of calls.We use two-dimensional and four- dimensional Markov chains to capture the handoff arrival process exactly in two-cell wireless network for single and two classes of users, respectively in this thesis.This thesis develops adaptive call admission control schemes based on handoff guarantees without making any assumption about the distribution of number of users in a cell.Moreover, an efficient algorithm for analyzing the guard channel scheme, which is a static scheme, is developed in this thesis.We propose a novel adaptive cali admission control scheme for the two-cell system which accepts a new call if it can guarantee, with a certain probability, that a user's call will be maintained irrespective of its (his/her) movement in the system.Our scheme is II] 4.1 Hexagonal structure of celiular wireless network 89 4.2 Analytical and simulation results of class 1 new call blocking probability in the two-cell system for one call per unit time for class 2 new calls 93 4.3 Analytical and simulation results of class 2 new call blocking probability in the two-cell system for one call per unit time for class 2 new calls 93 4.4 Analytical and simulation results of class t handoff call failure probability in the two-cell system for one call per unit time for class 2 new calls .94 4.5 Analytical and simulation results of class 2 handoff call failure probability in the two-celi system for one call per unit time for class 2 new calls 94 4.6 Analybical and simulation results of class 1 new call blocking probability in the two-cell system for two calls per unit time for class 2 new calls 95 4.7 Analytical and simulation results of class 2 new call blocking probability in the two-cell system for two calls per unit time for class 2 new calls 95 4.8 Analytical and simulation results of class t handoff call failure probability in the two-cell system for two calls per unit time for class 2 new calls 96 4.9 Analytical and simulation results of class 2 handoff call failure probability in the two-cell system for two calls per unit time for class 2 new calls 96 4.10 Analytical and simulation results of channel utilization in the two-cell system for one call per unit time for class 2 new calls 97 4.11 Analytical and simulation results of class t handoffcall arrival rate in the two-cell system for one call per unit time for class 2 new calls 98 4.I2 Analytical and simulation results of class 2 handoff call arrival rate in the two-cell system for one call per unit time for class 2 new calls 98 i 4.13 Analytical and simulation results of channel utilization in the two-cell system for two calls per unit time for class 2 new calls .99 4.14 Analytical and simulation results of class I handoff call arrival rate in the two-cell system for two calls per unit time for class 2 new calls 100 4.15 Analytical and simulation results of class 2 handoff call arrival rate in the two-cell system for two calls per unit time for class 2 new calls 100 4.16 Simulation results of class 1 new call blocking probability in the nineteen- cell wrap around structure for one call per unit time for class 2 new calls 101 4.17 Simuiation results of class 2 new call blocking probability in the nineteen- cell wrap around structure for one call per unit time for class 2 new calls I02 4.18 Simulation results of class t handoff call failure probability in the nineteen- cell wrap around structure for one call per unit time for class 2 new calls 702 4.19 Simulation results of class 2 handoff call failure probability in the nineteen- cell wrap around structure for one call per unit time for class 2 new calls 103 4.20 Simulation results of class 1 new call blocking probability in the nineteencell wrap around structure for two calls per unit time for class 2 new calls 103 4.21 Simulation results of class 2 new call blocking probability in the nineteencell wrap around structure for two calls per unit time for class 2 new calls 104 4.22 Simulation results of class t handoff call failure probability in the nineteen- cell wrap around structure for two calls per unit time for class 2 new calls 104 4.23 Simulation results of class 2 handoff call failure probability in the nineteen- cell wrap around structure for two calls per unit time for class 2 new calls 105 4.24 Simtlation results of channel utilization in the nineteen-cell wrap around structure for one call per unit time for class 2 new calls 106 4.25 Simulation results of class t handoff call arrival rate in the wrap around structure for one call per unit time for class 2 4.26 Simulation results of class 2 handoff call arrival rate in the wrap around structure for one call per unit time for class 2 4.27 Simulation results of channel utilization in the nineteen-cell nineteen-cell new calls 106 nineteen-cell new calls I07 wrap around structure for two calls per unit time for class 2 new calls 107 4.28 Simulation results of class t handoff call arrival rate in the nineteen-cell Iurap around structure for two calls per unit time for class 2 new calls 108 4.29 Simulation results of class 2 handoff call arrival rate in the nineteen-cell wrap around structure for two calls per unit time for class 2 new calls 108 5.1 Monotonicity of the iterative values of handoff arrival rate L23 5.2 Analytical and simulation results of channel utiiization I32 5.3 Anal5,'tical and simulation results of new call blocking probability 133 5.4 Analytical and simulation results of handoff call failure probability 133 5.5 Analytical and simulation results of handoff arrival rate 734 XI Chapter 1 fntroduction Call admission control (CAC) is a provisioning strategy to provide high quality of service (QoS) to users by limiting the number of call connections into a communication network, to a profitable level, while reducing the network congestion, depending on the availability of resources.In cellular wireless networks, another factor comes into play, the possibility of dropping a connected call due to the mobility of users.It is more irritating for a user's call not to be completed due to handoff failure than the call to be blocked during the new call attempt.Therefore, handoff calls are prioritized over new calls.It has been found that prioritizing handoffcalls over new calls results in the decrease of the number of handoff failures and the call dropping probabilities.However, prioritizing handoff calls over new calls results in the increase of the new call blocking probability.A good CAC scheme for a wireless cellular network has to balance the call blocking and the call dropping probabilities in order to provide the desired QoS requirements.The design of CAC algorithms for mobile cellular wireless networks is especially challenging given the limited and highly variable resources, and the mobility of users encountered in such networks.Call admission control schemes for cellular wireless networks have been extensively studied in the literature.They can be broadly classified into two categoriesstatic schemes and distributed schemes.In the literature, static call admission control schemes, which are mainly cutoff priority schemes, are generally studied for a single cell [3, 45, 55, 90] to approximate the whole cellular wireless network.On the other hand, in distributed call admission control schemes [36, 56, 76., 79, 104], which are also called adaptive cail 'È
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".