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On SINR-based Random Multiple Access using Codes on Graph

机译:在基于SINR的随机多次访问,使用图表上的代码

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We revisit random multiple access (RMAC) for wireless systems with successive interference cancellation (SIC) employed at the access point (AP). We consider an asymptotically large number of users that transmit over a large number of orthogonal sub-channels. In each transmission block, each user chooses a degree d, where d is a random variable that follows a predefined degree distribution Ω(x). Then, users transmit in d sub-channels chosen uniformly at random. Specifically, we consider signal to interference and noise ratio (SINR) based RMAC where it is assumed that a user's information can be recovered successfully at a given iteration of the SIC process when its updated SINR is above a predetermined threshold. In this paper, we develop a generalized analytical framework based on the codes-on-graph representation to track the evolution of error probabilities in each iteration of the SIC process. We compare our approach to the conventional RMAC employing SIC which assumes that only clean, interference-free transmissions can be recovered successfully. This clean packet model relies on having time slots with a single user's transmission at each iteration of the SIC process. It was shown to be analogous to the iterative recovery process of codes-on-graph for the binary erasure channel (BEC), thus, allowing the direct application of the AND-OR tree analysis. We show that the clean packet model is a special case of our more generalized tree-based analytical framework. Our numerical results show that our model can support more users under the same power requirements.
机译:我们为访问点(AP)采用的连续干扰消除(SIC)重新审视随机多址(RMAC)。我们考虑在大量正交子信道上传输的渐近大量用户。在每个传输块中,每个用户选择程度d,其中d是遵循预定度分布ω(x)的随机变量。然后,用户在随机选择的D子通道中发送。具体地,我们考虑基于RMAC的干扰和噪声比(SINR)的信号,其中假设当其更新的SINR高于预定阈值时,假设用户的信息可以成功地恢复。在本文中,我们基于图表代码表示,开发了一种广义分析框架,以跟踪SIC过程每次迭代中的误差概率的演变。我们将我们的方法与传统的RMAC采用SiC进行比较,该方法假设只能成功恢复清洁,无干扰传输。此清洁数据包模型依赖于在SIC过程的每次迭代时具有单个用户传输的时隙。因此,它被示出类似于用于二进制擦除通道(BEC)的图表的迭代恢复过程,从而允许直接应用树或树分析。我们表明清洁数据包模型是我们更广泛的基于树的分析框架的特殊情况。我们的数值结果表明,我们的模型可以在相同的电源要求下支持更多用户。

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