Abstract
In order to facilitate massive connectivity in fifth-generation (5G) systems and make full use of advanced coding schemes, designing effective multiuser detection algorithms is necessary for mitigating the interference in multiple access systems. To evaluate the performance of different detection algorithms, the conventional method is implementing the Monte Carlo (MC) simulation to estimate the detection error rate. In this paper, we present a novel simulation scheme based on adaptive importance-sampling (AIS) theory, which accelerates the simulation speed for estimating the extremely low detection error rate in multiple access systems. Specifically, by restricting the generation of random codewords to the joint Gaussian distribution biased with scaling parameters, two algorithms are proposed to determine the optimal biased parameters such that the estimated variance or the cross-entropy resulted in the AIS simulation is minimized respectively. Our proposed simulation scheme is compared with the standard MC simulation in the performance evaluation of message passing algorithm (MPA) for uplink sparse code multiple access (SCMA) system. Numerical results show that our proposed scheme provides a feasible estimation of extremely low detection error rate and achieves significant performance gain with reduced simulation overhead.
Highlights
The Internet of Things (IoT) has emerged as an intelligent network for the fifth-generation (5G) wireless communications, which supports connections among a large number of users and/or smart devices [1]–[3]
NUMERICAL RESULTS simulations were carried out to investigate the performance of our proposed fast simulation schemes for the uplink sparse code multiple access (SCMA) systems
It is evident that the number of trials that need to be performed using our proposed scheme can be significantly reduced compared with the Monte Carlo (MC) scheme especially at high SNRs
Summary
The Internet of Things (IoT) has emerged as an intelligent network for the fifth-generation (5G) wireless communications, which supports connections among a large number of users and/or smart devices [1]–[3]. The conventional MC scheme often requires generating a large number of random simulation samples to reach specific accuracy, and will lead to prohibitive computational complexity when the estimated detection error rate is extremely low [15]. Toward this end, a more efficient simulation scheme (with low complexity and high accuracy) to evaluate the performance of different multiuser detection algorithms is expected. Simulation results show that our proposed scheme will significantly reduce the simulation overhead compared with the conventional MC scheme, especially for estimating the extremely low detection error rate
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