GoS Evaluation for OFDMA Cellular Networks
Analytical methods on grade of service (GoS, quantified by the blocking probability which is denoted as \(P_b\)) evaluation in orthogonal frequency division multiple access cellular networks for real-time traffic are important for cell-dimensioning. Existing works use simplified spectral efficiency (SE) models that lead to conservative outcomes. For computing cell-wide and cell-average \(P_b\) the exact but mathematically intractable Kaufman Roberts algorithm (KRA) is indispensable. The Erlang approximation (EA) tractably computes only cell-average \(P_b\) but ignores bandwidth allocation granularity leading to an error of around 25% in typical scenarios. In this work, by using an accurate SE model we show a significant reduction in projected base station (BS) density requirement for maintaining satisfactory cell-average \(P_b\). We show that cell-average \(P_b\) does not completely depict the GoS situation within a cell since there is significant variation of \(P_b\) inside it. Further, we enhance the EA in two ways. Firstly, we improve its accuracy to within 2% of KRA by accounting for bandwidth allocation granularity. Secondly, we extend it to compute cell-wide \(P_b\) like the KRA. The resulting method helps to explain properties such as the linearly increasing trend of region-wise \(P_b\) versus region-index in case of low bit-rate streaming calls. We also use the aforementioned linearly increasing trend to arrive at another alternative to KRA. The development of these two alternatives to KRA highlights certain interesting properties of region-wise and cell-average \(P_b\) as well as the EA. The error in estimated BS density requirement with these alternatives in case of 12.2kbps voice in the interference limited situation is negligibly small. We employ discrete event simulations to validate our models.
ofdma cellular networks grade of service blocking probability kaufman roberts algorithm erlang approximation quality of service