论文标题
Nephalai：朝着带有物理层压缩的LPWAN C-RAN
Nephalai: Towards LPWAN C-RAN with Physical Layer Compression
论文作者
论文摘要
我们提出了Nephalai，这是一种基于压缩感应的云无线电访问网络（C-RAN），以降低网关和云服务器之间的物理层（PHY）的上行链路比特速率，用于多通道LPWANS。最近的研究表明，单渠道LPWANS遭受可伸缩性问题的困扰。虽然多个渠道可以改善这些问题，但数据传输却很昂贵。此外，最近的研究表明，云中LPWAN网关卸载的共同解码原始物理层可以提高周期无线电信号的信噪比（SNR）。但是，当涉及到多个渠道时，这种方法需要高的网络基础架构带宽，以将大量的PHY样本从网关传输到云服务器，这导致网络拥堵和由于Internet数据的使用而导致的高成本。为了减少操作的带宽，我们提出了一种新型的LPWAN数据包采集机制，其基于压缩感测的定制词典利用了LPWAN数据包的结构，可降低每个网关上的样品比特速率，并在云中使用（关节）稀疏近似云中的PHY在云中删除PHY。此外，我们提出了一种自适应压缩方法，该方法将扩散因子（SF）和SNR考虑在内。我们的经验评估表明，当SF = 9且SNR较高时，Nephalai可以减少多达93.7％的PHY样品，而数据包接收率（PRR）却没有降解。使用四个网关，可以通过压缩87.5％的PHY样品来实现1.7倍的PRR，这可以将嵌入式IoT设备的电池寿命扩展到1.7。
We propose Nephalai, a Compressive Sensing-based Cloud Radio Access Network (C-RAN), to reduce the uplink bit rate of the physical layer (PHY) between the gateways and the cloud server for multi-channel LPWANs. Recent research shows that single-channel LPWANs suffer from scalability issues. While multiple channels improve these issues, data transmission is expensive. Furthermore, recent research has shown that jointly decoding raw physical layers that are offloaded by LPWAN gateways in the cloud can improve the signal-to-noise ratio (SNR) of week radio signals. However, when it comes to multiple channels, this approach requires high bandwidth of network infrastructure to transport a large amount of PHY samples from gateways to the cloud server, which results in network congestion and high cost due to Internet data usage. In order to reduce the operation's bandwidth, we propose a novel LPWAN packet acquisition mechanism based on Compressive Sensing with a custom design dictionary that exploits the structure of LPWAN packets, reduces the bit rate of samples on each gateway, and demodulates PHY in the cloud with (joint) sparse approximation. Moreover, we propose an adaptive compression method that takes the Spreading Factor (SF) and SNR into account. Our empirical evaluation shows that up to 93.7% PHY samples can be reduced by Nephalai when SF = 9 and SNR is high without degradation in the packet reception rate (PRR). With four gateways, 1.7x PRR can be achieved with 87.5% PHY samples compressed, which can extend the battery lifetime of embedded IoT devices to 1.7.
