Uplink conflict signal decoding method and system for low-power wide area network

Abstract

The embodiment of the invention provides an uplink conflict signal decoding method and system for a low-power wide area network, and the method comprises the steps: segmenting an uplink conflict signal through a plurality of preset decoding windows, and obtaining an uplink conflict segment signal in each preset decoding window; de-spreading the uplink conflict segmented signal in the preset decoding windows, and performing Fourier transform on a de-spreading result to obtain a corresponding frequency domain wave crest feature; measuring each energy wave crest in the frequency domain of the frequency domain wave crest feature to obtain a frequency wave crest energy ratio and a frequency wave crest energy sum corresponding to each coding symbol in the uplink conflict signal, clustering eachcoding symbol based on a Bayesian clustering algorithm to obtain a corresponding uplink conflict signal data packet, and decoding according to the uplink conflict signal data packet to obtain a decoding result of the uplink conflict signal. According to the embodiment of the invention, the uplink transmission efficiency of a low-power wide area network is improved, conflict data packet retransmission is avoided, and the node service life is prolonged.

Description

technical field [0001] The present invention relates to the technical field of wireless communication, in particular to an uplink conflict signal decoding method and system for a low power consumption wide area network. Background technique [0002] Low-power wide-area network technology is widely used in IoT connections. Its low power consumption and long-distance transmission characteristics enable IoT nodes to use only a few milliwatts of communication energy to achieve a transmission distance of tens of kilometers. The Internet of Things system based on low power wide area network technology has a wide range of applications in environmental monitoring, disaster early warning, and target tracking. However, limited by the network architecture and actual network scale, the low-power wide-area network system deployed in reality generally has a serious problem of uplink signal conflict: when multiple nodes send data packets to the gateway at the same time, the uplink signals ...

AI Technical Summary

Problems solved by technology

However, limited by the network architecture and actual network scale, the low-power wide-area network system deployed in reality generally has a serious problem of uplink signal conflict: when multiple nodes send data packets to the gateway at the same time, the uplink signals from different nodes will be sent to the gateway. Collisions occur here, resulting in packet corruption or even packet loss

The former assigns different spreading factors to different nodes, so that the signals of different nodes are approximately orthogonal, so that multiple conflicting signals can be decoded at the receiving end at the same time, but this method is limited by the number of optional spreading parameters. With the expansion of network scale, different nodes will inevitably be given the same spread spectrum parameters, so the problem of signal conflict cannot be solved fundamentally; the latter uses crystal oscillator hardware frequency offset to distinguish conflicting signals, and its core idea is to After transforming to the frequency domain, the frequency offset of each group of peaks is extracted, and then the signals from different nodes are clustered and decoded according to the frequency offset of the peaks, but the performance of this method is limited by the measurement accuracy of the hardware frequency offset , the hardware frequency offset is difficult to measure accurately when the signal-to-noise ratio is low

In addition, the ambient temperature and humidity will also affect the signal frequency offset. When the environment changes dynamically, the performance of the above collision decoding method will be significantly reduced.

Images