Simulation and Modeling Methodology

Jeruchim, Michel C.; Balaban, P.; Shanmugan, K. Sam

doi:10.1007/978-1-4615-3298-9_6

Cited by 29 publications

(55 citation statements)

References 22 publications

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“…The desirable distribution of random numbers in this simulation can be obtained from the uniform distribution by inverse transform method, [11] or [12]. In the case of uniform users' density in the cell, equation (3), it is necessary to generate numbers with the inverse quadratic distribution.…”

Section: Simulationmentioning

confidence: 99%

“…So, the random numbers, which determine the distance between BTS and MS, are calculated as the square root of the uniformly distributed random numbers. It means that random distance BTS-MS (d) can be obtained in simulation from the random number RN2, which has the uniform distribution in the range (0,1), by implementing the operation d=R·RN3=R·SQRT(RN2), as presented in [11] or [12]. Finally, the output power of one connection in simulation process is obtained as the random variable w, which is the function of the random distance BTS-MS, according to (1).…”

Section: Simulationmentioning

confidence: 99%

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Use of Computer Simulation in Estimation of GSM Base Station Output Power

Lebl¹

2014

APH

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In this paper we present how computer simulation can be used to estimate the mean output power and the probability cumulative distribution function of one base station output power in GSM. The basis of the method is adding the simulation of random distance between the mobile station (MS) and base transceiver station (BTS) to the known method of telephone traffic simulation. The simulation is a suitable method for the estimation of base station output power, especially in the case when it is not easy to calculate the output power's dependence on these factors. A couple of numerical examples present the results of these simulations.

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Section: Simulationmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

Use of Computer Simulation in Estimation of GSM Base Station Output Power

Lebl¹

2014

APH

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“…The corresponding transfer functions are given, respectively, by (11) (12) which can be rewritten as (13) (14) in order to avoid the computation of the sums in (11) and (12).…”

Section: B Computation Of Mgfmentioning

confidence: 99%

“…In a semi-analytical simulation method of performance evaluation, the ASE noise influence on the receiver performance is dealt with analytically and the incoming signal at the optical receiver input is assumed as a deterministic signal that is obtained by computer simulation [11], [16]. After computing through numerical simulation, by following the reasoning described in Subsections II.A-II.C [using (1)-(23)], the performance of the DD-OFDM optical receiver when impaired by ASE noise is estimated.…”

Section: Computation Of Bit Error Probabilitymentioning

confidence: 99%

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Moment Generating Function for the Rigorous Performance Assessment of Direct-Detection Baseband OFDM Communication Systems

Rebola¹,

Cartaxo²

2012

J. Lightwave Technol.

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Abstract-A new moment generating function (MGF) isproposed to assess rigorously the performance of optically preamplified receivers with direct-detection employing baseband orthogonal frequency division multiplexing (OFDM). By comparison with Monte Carlo simulation, the proposed MGF-based method shows very good accuracy when predicting the bit error probability for arbitrary optical and electrical filters, different numbers of OFDM subcarriers, and double sideband and single sideband optical OFDM signals. Its good accuracy when estimating the bit error probability in each individual subcarrier is also demonstrated. The new MGF-based method exhibits a significant improvement of accuracy when compared to the MGF-based method previously reported in the literature, especially for filter bandwidths not exceeding the OFDM signal bandwidth, which is the range corresponding to the optimum filter bandwidth.

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Importance Sampling Over Monte Carlo Technique to Estimate Wireless Infrared On-Chip Communication

Balti

2023

Wireless Pers Commun

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Optical code division multiple access (OCDMA) represents the development of optical communication. The advantages of OCDMA such as, asynchronous multiuser communication transmission, flexibility, ample bandwidth and scalability; have made this technique a better candidate to be proposed for next generation network on chip. In this paper, performance of on-chip OCDMA communication system is analyzed. Analytical techniques for estimating the performance of optical communication systems are difficult, complicated and no complete analytical treatment of the problem has been obtained before. Simulation techniques such as Monte Carlo (MC) are used to obtain reasonable estimates of these systems performance. For optical communications, Monte Carlo require excessively large sample sizes which makes it cumbersome in terms of physical resources, and are not practical for estimating very low values of error probabilities. Modified Monte-Carlo (Importance sampling/IS) method was used to reduce the number of simulation tests for estimating error probabilities for optical communications systems by modifying the probability density function of the onchip noise process. Simulation results are presented and assessed in term of bit error rate (BER), showing the contribution of IS relatively to MC.

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Simulation and Modeling Methodology

Cited by 29 publications

References 22 publications

Use of Computer Simulation in Estimation of GSM Base Station Output Power

Use of Computer Simulation in Estimation of GSM Base Station Output Power

Moment Generating Function for the Rigorous Performance Assessment of Direct-Detection Baseband OFDM Communication Systems

Importance Sampling Over Monte Carlo Technique to Estimate Wireless Infrared On-Chip Communication

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