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Fast Adaptive S-ALOHA Scheme for Event-Driven Machine-to-Machine Communications

机译:用于事件驱动的机器对机器通信的快速自适应S-ALOHA方案

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Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S- ALOHA (FASA) scheme for M2M communication systems with bursty traffic. The statistics of consecutive idle and collision slots, rather than the observation in a single slot, are used in FASA to accelerate the tracking process of network status. Furthermore, the fast convergence property of FASA is guaranteed by using drift analysis. Simulation results demonstrate that the proposed FASA scheme achieves near-optimal performance in reducing access delay, which outperforms that of traditional additive schemes such as PB-ALOHA. Moreover, compared to multiplicative schemes, FASA shows its robustness even under heavy traffic load in addition to better delay performance.
机译:机器对机器(M2M)通讯现在在广泛的商业世界中扮演着改变市场的角色。但是,在事件驱动的M2M通信中,大量设备在短时间内激活,从而导致无线电拥塞严重和访问延迟严重。为了解决此问题,我们提出了一种用于突发流量的M2M通信系统的快速自适应S-ALOHA(FASA)方案。 FASA使用连续空闲和冲突时隙的统计信息,而不是在单个时隙中进行观察,来加快对网络状态的跟踪过程。此外,通过使用漂移分析可以保证FASA的快速收敛性。仿真结果表明,所提出的FASA方案在减少访问延迟方面达到了近乎最佳的性能,优于传统的加性方案(如PB-ALOHA)。此外,与乘法方案相比,FASA表现出了鲁棒性,即使在繁重的流量负载下,它也具有更好的延迟性能。

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