To enhance the sensitivity of LoRaWAN water quality sensors, improvements can be made in hardware design, parameter configuration, and signal processing. The specific methods are as follows:

1. Optimize the hardware design

The use of low-noise amplifiers (LNA): LNA amplifies signals before they enter the receiver circuit, reducing noise interference and improving the signal-to-noise ratio. For example, an industrial IoT device optimizes LNA gain allocation to maintain stable output across a dynamic range of-110dBm to-90dBm, effectively reducing the bit error rate to below 10^{-6}.

Selecting high-gain antennas: These antennas enhance signal transmission and reception efficiency, thereby expanding coverage. For example, terminal devices equipped with ring patch antennas achieve a 22% greater signal coverage radius compared to traditional patch antennas in complex building environments.

Optimize antenna layout: A well-designed antenna layout reduces feeder loss and boosts effective radiated power. For example, 3D printed antenna technology can increase effective radiated power by 8%-10%.

Use multi-antenna diversity reception technology: This technology receives signals through multiple antennas and then combines these signals to improve the receiving sensitivity. For example, in the IEEE 802.15.4-2020 standard, multi-antenna diversity reception technology can improve the receiving sensitivity to-125dBm.

2. Properly configure communication parameters

Adjust spread factor: The higher the spread factor, the higher the receiving sensitivity. However, increasing the spread factor may also reduce the data transmission rate, so a trade-off should be made based on the actual application scenario.

Optimize bandwidth: Reducing bandwidth improves reception sensitivity but also decreases data transmission rate. In practice, adjust bandwidth through field testing to achieve optimal communication performance while maintaining the minimum signal-to-noise ratio.

Dynamic channel selection: Regular channel quality assessments are performed to dynamically select channels with a signal-to-noise ratio (SNR) ≥8dB, avoiding those with high interference to enhance reception sensitivity.

3. Optimization of signal processing algorithm

Forward Error Correction (FEC) algorithm: The FEC algorithm encodes data at the transmitter and decodes it at the receiver to correct transmission errors, thereby improving signal anti-interference capability and indirectly enhancing reception sensitivity. For instance, upgrading channel coding from CRC-16 to CRC-24 increases the bit error correction rate from 10^{-4} to 10^{-6}.

Adaptive Modulation and Coding (AMC) is implemented: AMC can dynamically adjust the modulation order and coding scheme according to the channel state information. When the channel condition is good, high rate modulation and coding scheme is adopted; when the channel condition is poor, low rate and high reliability modulation and coding scheme is adopted to improve the receiving sensitivity and data transmission efficiency.