Capacity estimation algorithm for simultaneous support of multi-class traffic services in Mobile WiMAX

Ahmadzadeh, Amir; Sánchez-García, Juan; Saavedra-Moreno, B.; Portilla-Figueras, Antonio; Salcedo‐Sanz, Sancho

doi:10.1016/j.comcom.2011.08.002

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…VoLTE cell capacity estimation for developing effective and efficient CAC policy has been an active area of research in mobile communication. According to the work reported in [9–13], cell capacity relates to the maximum number of active concurrent voice users that can be accommodated within the cell while maintaining the desired voice quality. Higher capacity enables the network operators to satisfy more mobile users especially in dense urban area and there by reduces the number of eNBs required within the geographical area.…”

Section: Related Workmentioning

confidence: 99%

Modelling the region‐based VoLTE cell capacity estimation using resource optimisation

Senapati

Pati

2019

IET Communications

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Section: Related Workmentioning

confidence: 99%

Modelling the region‐based VoLTE cell capacity estimation using resource optimisation

Senapati

Pati

2019

IET Communications

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“…The control overhead messages are preamble, FCH (Frame Control Header), DL-MAP, UL-MAP, DCD (Downlink Channel Descriptor), and UCD (Uplink Channel Descriptor) [6,7,9,14,15].…”

Section: Mac Layer Overhead Calculations In DL Fusc and Pusc Permutatmentioning

confidence: 99%

“…Such that, the calculated data rate represents the whole TDD frame. The physical layer peak data rate calculated according to equation (15) [16,17,18] as follows: R Physical = N data * N b * C r / T s (15) where, N data : number of data subcarriers, see table (1)…”

Section: Physical Layer Throughput Calculationsmentioning

confidence: 99%

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Capacity Analysis and Simulation for Mobile WiMAX System in Distributed Subcarrier Permutation Modes in a Proposed Channel

Fatah¹,

Mawjoud²

2013

(AREJ)

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In this paper, capacity analysis for mobile WiMAX working in distributed subcarrier permutation modes is investigated mathematically depending on the physical layer simulation results. These simulation results of physical layer dispose exact values for the number of best burst profiles that candidate to carry Medium Access Control (MAC) payload in a proposed ITU-R mixed user channel. The complete simulation model for physical and MAC layers has been made using OPNET 14.5 to validate the analytical results of the obtained capacity. This approach for capacity analysis based on simulation results is proposed to overcome the lack of interests by other researches in the significant influence of the physical layer constraints on the MAC layer capacity calculations. As well as in this paper, a proposed ITU-R mixed user channel is performed to suit the high mobility users involved under distributed subcarrier permutation modes. Moreover, this paper focused on the exact overhead calculations by taking into account all the significant overhead parameters in the MAC layer frame structure that affects the overall system capacity. KeywordsIEEE802.16e-2005, OFDMA, PUSC, FUSC, QoS, proposed ITU-R mixed user channel model, PER.

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“…Optimal distribution of services over the radio access network in current mobile broadband networks has been extensively studied in the literature. However previous works refer to different scheduling methods that are mainly applied in the operation of the network or consider a single technology .…”

Section: Introductionmentioning

confidence: 99%

A coral‐reef optimization algorithm for the optimal service distribution problem in mobile radio access networks

Salcedo‐Sanz

Sánchez-García

Portilla-Figueras

et al. 2013

Trans. Emerging Tel. Tech.

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Mobile technology is currently one of the main pillars of worldwide economy. The constant evolution that mobile communications have undergone in the last decades, due to the appearance of new services and new technologies such as Universal Mobile Telecommunication Systems/High Speed Data Access and Long Term Evolution, has contributed to achieve this position in global economy. However, because of the crisis of the sector in the last 5 years, mobile operator's revenues and investments have been reduced. Thus, mobile network operators tend to exploit the existing infrastructure at maximum possible, trying to use the existing network in the most efficient way. In this paper, a novel bio-inspired algorithm, the coral reef optimization algorithm (CRO) is introduced to minimise a network deployment investment cost problem. This is carried out by means of optimising the user demand of different services offered by mobile operators over the available technologies in the market, namely the optimal service distribution problem. The CRO is a recently proposed meta-heuristic based on the computer simulation of corals reproduction and reefs' formation. In this paper, this algorithm has been tested on several optimal service distribution problem scenarios in Spain, observing a significant reduction (up to 400 Me) on the total investment costs associated to the radio access network deployment. We compare the performance of the CRO approach with that of a classical (experience-based) services distribution, and with alternative meta-heuristics techniques, obtaining good results in all cases.

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Capacity estimation algorithm for simultaneous support of multi-class traffic services in Mobile WiMAX

Cited by 7 publications

References 10 publications

Modelling the region‐based VoLTE cell capacity estimation using resource optimisation

Modelling the region‐based VoLTE cell capacity estimation using resource optimisation

Capacity Analysis and Simulation for Mobile WiMAX System in Distributed Subcarrier Permutation Modes in a Proposed Channel

A coral‐reef optimization algorithm for the optimal service distribution problem in mobile radio access networks

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