论文标题
用于估计CSMA网络性能的随机几何建模的局限性
Limitations of Stochastic Geometry Modelling for Estimating the Performance of CSMA Networks
论文作者
论文摘要
该字母考虑了随机几何建模（SGM），用于估计CSMA网络的信号与差异和噪声比（SINR）和吞吐量。我们表明，尽管具有紧凑的数学表述，但SGM在准确性和计算效率方面都有严重的局限性。 SGM通常严重低估了SINR与NS-3模拟，但是当它忽略映射SINR到吞吐量时的传感开销时，SGM通常高估了吞吐量。我们提出了CSMA的混合模型，我们认为这是一种出色的建模方法，因为比SGM明显更准确，并且至少一个数量级的计算顺序。
This letter considers stochastic geometry modelling (SGM) for estimating the signal-to-interference-and-noise ratio (SINR) and throughput of CSMA networks. We show that, despite its compact mathematical formulation, SGM has serious limitations in terms of both accuracy and computational efficiency. SGM often severely underestimates the SINR versus ns-3 simulations, yet as it neglects the sensing overhead when mapping SINR to throughput, SGM usually overestimates the throughput substantially. We propose our hybrid model for CSMA, which we argue is a superior modelling approach due to being significantly more accurate and at least one order of magnitude faster to compute than SGM.
