Multiple Access Protocols For Mobile Communications

Brand, Alex; Aghvami, Hamid

doi:10.1002/0470846224

Cited by 45 publications

(31 citation statements)

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“…Graphs (5)(6)(7)(8)(9)(10) show the effect of the variability of λ on the S 8×8 (GGeo/GGeo/1)/512 system for C 2 a = 1, C 2 s = 1, C 2 a = 500, C 2 s = 1 or C 2 a = 500, C 2 s = 5, for both the blocking probability and the utilization.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Graphs (8)(9)(10) show that π a increases as λ increases on the case of 'small' traffic load and it is possible that π a will decrease as λ increases by investigating the tendency of π a . By investigating the above example of a simplified queueing model of one UMTS cell and its graphs, we can say that the utilization and the blocking probability of UMTS cell is mainly affected by C 2 a , C 2 s and λ.…”

Section: Numerical Resultsmentioning

confidence: 99%

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Performance of Queueing Model of one UMTS Cell with R Virtual Zones by the Maximum Entropy Solution

Zreikat

2006

Wireless Pers Commun

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UMTS stands for Universal Mobile Telecommunications System, which is one of the third generation (i.e., 3G) cellular systems, will come into full commercial phase by year 2005 and the first UMTS services are launched commercially in 2001. Therefore, the research of UMTS is an important and urgent task. In this paper, the performance of UMTS systems is studied through a queueing model which consists of one UMTS with many virtual zones to evaluate the system uplink traffic performance. The two performance measures: blocking probability and system utilization are obtained by the Maximum Entropy Principle, i.e., ME solutions for S R×R (GGeo/GGeo/1)/N queue. S R×R (GGeo/GGeo/1)/N system means that arrival time and service time are both GGeo (Generalized Geometric) distributed, each output port has a single server and the maximum capacity is N . After the theoretical analysis, the numerical results are found for the idiographic example, S 8×8 (GGeo/GGeo/1)/512 queueing model. Additionally, the performance of this queueing model is discussed with the effect of the parameters Ca 2 (squared coefficient of variation of the interarrival time), Cs 2 (squared coefficient of variation of the service time) and the arrival rate, λ.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Numerical Resultsmentioning

confidence: 99%

Performance of Queueing Model of one UMTS Cell with R Virtual Zones by the Maximum Entropy Solution

Zreikat

2006

Wireless Pers Commun

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“…This is due to the fact that there exists two types of networks: Switched (packet based approach) because of the dedicated connection however this is a consequence of higher costs [7]. Other packet based approach can also be considered and different combinations can be set up.…”

Section: Mac: Medium Access Controlmentioning

confidence: 99%

An efficient QoS MAC for IEEE 802.11p over cognitive multichannel vehicular networks

Ajaltouni

Pazzi

Boukerche

2012

2012 IEEE International Conference on Communications (ICC)

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One of the most challenging issues facing vehicular networks lies in the design of an efficient MAC protocol due to mobile nature of nodes, delay constraints for safety applications and interference.In this thesis, I propose an efficient Multichannel QoS Cognitive MAC (MQOG).MQOG assesses the quality of channel prior to transmission employing dynamic channel allocation and negotiation algorithms to achieve significant increase in channel reliability, throughput and delay constraints while simultaneously addressing Quality of Service.

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“…New generations of GSM were introduced over the past decade that includes GPRS, UMTS... etc in order to improve the transmission rates, and offer new types of services (Brand and Aghvami, 2002). The GSM which is also known as the cellular network is based on frequency reuse.…”

Section: Gsm/gprsmentioning

confidence: 99%