论文标题
基于机器聚类和算术分布的LORA-ESL的SNR和RSSI的实验比较
Experimental Comparison of SNR and RSSI for LoRa-ESL Based on Machine Clustering and Arithmetic Distribution
论文作者
论文摘要
洛拉缺乏通道状态的感应功能。接收的信号强度指示器（RSSI）由于碰撞，干扰和近乎FAR效应而降低，而对于信噪比（SNR），通过在较高扩散因子（SF）下降低传输功率（TP）来拒绝数据包。为了克服这些挑战，在电货架标签（ESL）的情况下，以最大程度地减少对重传和确认的依赖，最终设备（EDS）是基于机器集群的机器群集在Gateways（GWS）周围分配的，而动态SF则用于SNR，而RSSI的动态TP则分配。实验结果确定RSSI方法比SNR更为主导，因为确定了降低捕获效果的ED的确切位置。不同簇中各种GWS的EDS的算术分布有助于缩小近乎FAR的效果。在大多数连接的ED中，在每个集群处收到的功率（RP）高于阈值RP。
LoRa lacks the sensing capabilities of channel status. Received signal strength indicator (RSSI) decreases due to collision, interference, and near-far effect while for signal-to-noise ratio (SNR), the packets are rejected by decreasing the transmission power (TP) at a higher spreading factor (SF). To overcome these challenges in the case of electric shelf label (ESL) to minimize the dependency on retransmission and acknowledgment, the end devices (EDs) are allocated around gateways (GWs) based on machine clustering with dynamic SF for SNR while dynamic TP for RSSI. The experimental results determined that the RSSI approach is more dominant than SNR because of determining the exact locality of the ED that diminished the capture effect. Arithmetic distribution of EDs for various GWs in different clusters helps to minify the near-far effect. The resultant received power (RP) at each cluster is higher for most of the connected EDs than the threshold RP.
