arXivDaily arXiv每日学术速递 周一至周五更新
北美退伍军人节(Veterans Day),12 日无更新
arXiv 2009.05072cs.ITeess.SPmath.IT
收藏

低功率广域网突发干扰下基于报文分割传输的高效检测器

Efficient Detectors for Telegram Splitting based Transmission in Low Power Wide Area Networks with Bursty Interference

Steven Kisseleff, Jakob Kneissl, Gerd Kilian, Wolfgang H. Gerstacker

摘要:

已知低功率广域(LPWA)网络在分组冲突方面非常容易受到外部带内干扰,这可能大大降低系统性能。为了提高在这种情况下的性能,电报分裂(TS)的方法最近已经提出。这种方法利用了典型的突发性的干扰通过前向纠错(FEC),并提供了一个实质性的性能改善相比,其他方法在LPWA网络中的数据包传输。虽然已经证明TS方法受益于关于接收机侧的当前干扰状态的知识,但是仍然缺少高性能的对应的实用接收机算法。通过马尔可夫链的突发干扰的建模导致最佳检测器的后验符号错误概率。然而,该解决方案需要高的计算复杂性,假设关于干扰特性的先验知识,并且缺乏灵活性。我们提出了一个进一步发展的计划,增加了灵活性,并引入了一种方法,以降低其复杂性,同时保持接近最佳的性能。特别地,所提出的低复杂度解决方案在分组出错率方面显著优于现有的实际方法,并且因此对于实际的LPWA网络场景是非常有益的。

英文摘要:

Low Power Wide Area (LPWA) networks are known to be highly vulnerable to external in-band interference in terms of packet collisions which may substantially degrade the system performance. In order to enhance the performance in such cases, the telegram splitting (TS) method has been proposed recently. This approach exploits the typical burstiness of the interference via forward error correction (FEC) and offers a substantial performance improvement compared to other methods for packet transmissions in LPWA networks. While it has been already demonstrated that the TS method benefits from knowledge on the current interference state at the receiver side, corresponding practical receiver algorithms of high performance are still missing. The modeling of the bursty interference via Markov chains leads to the optimal detector in terms of a-posteriori symbol error probability. However, this solution requires a high computational complexity, assumes an a-priori knowledge on the interference characteristics and lacks flexibility. We propose a further developed scheme with increased flexibility and introduce an approach to reduce its complexity while maintaining a close-to-optimum performance. In particular, the proposed low complexity solution substantially outperforms existing practical methods in terms of packet error rate and therefore is highly beneficial for practical LPWA network scenarios.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑