Hybrid model for throughput evaluation of orthogonal frequency division multiple access networks

Abstract

Data throughput is an important metric used in the performance evaluation of the next generation cellular networks such as long-term evolution (LTE) and LTE-advanced. To evaluate the performance of these networks, Monte Carlo simulation schemes are usually used. Such simulations do not provide the throughput of intermediate call state; instead it gives the overall performance of this network. The authors propose a hybrid model consisting of both analysis and simulation. The benefit of this model is that the throughput of any possible call state in the system can be evaluated. Here, the probability of possible call distribution is first obtained by analysis, which is used as input to the event-driven-based simulator to calculate the throughput of a call state. Comparison is made between throughput obtained from the author's hybrid model with that obtained from event-driven-based simulation. Numerical results are presented and show good agreement between both the proposed hybrid model and the simulation. The maximum difference of relative throughput between their hybrid model and the simulation is found in the interval of (0.04 and 1.06%) over a range of call arrival rates, mean holding times and number of resource blocks in the system.

Keywords

• frequency division multiple access
• OFDM modulation
• Monte Carlo methods
• Long Term Evolution
• discrete event simulation
• cellular radio
• orthogonal frequency division multiple access networks
• data throughput
• performance evaluation
• next generation cellular networks
• long-term evolution
• LTE-advanced
• Monte Carlo simulation
• call state
• event-driven-based simulation
• call arrival rates
• mean holding times