Key result
The proposed battery signal control model achieved a bit error rate of 1×10^-4 at a signal-to-noise ratio of 18dB, with a carrier-to-noise ratio remaining above 0.73 dB, outperforming comparison methods.
Why the study?
In large-scale IoT medical monitors, multipath interference hinders accurate battery signal control by causing delays and imbalance between transmit-receive signals.
The proposed battery signal control model effectively suppresses multipath interference in large-scale IoT medical monitors, maintaining low bit error rates and stable carrier-to-noise ratios.
Answers are grounded in this paper and cite their sources.
May improve IoT monitor reliability; leaves open clinical validation in cardiovascular practice.
INTRODUCTION: In large-scale IOT medical monitors, the accurate control of battery signals has been facing the problem of multipath interference. Multipath interference causes the receiver to receive multiple signals propagating through different paths and interfering with each other, which results in an imbalance in the battery signal control based on the time delay of the "transmit-receive" signals. OBJECTIVES: To solve the multipath interference problem of existing battery signal control, this paper designs a battery signal control model for a large-scale IoT medical monitor. METHODS: Firstly, this paper uses time synchronization to align the time between the receiver and the transmitter to synchronize the communication signals of the acquisition system; Next, a transverse time-domain filter is used for modulation filtering; Then, a judgment feedback equalization algorithm is introduced in combination with a full-feedback filter to suppress the inter-code interference and improve the signal quality; Finally, a fractional interval equalizer is designed to adjust the weight coefficients of the equalizer taps, and implement intelligent battery signal control in multi-hop communication under multipath interference based on fractional interval and bit error rate (BER) feedback modulation. RESULTS: Experimental results have shown that after using the method described in this paper to control the communication signal of the monitor battery, the output signal is relatively stable, and the BER reaches 1×10-4 when the signal-to-noise ratio is equal to 18dB. The BER is low, and the carrier-to-noise ratio of the output signal is 0.73~0.85. The carrier-to-noise ratio always remains above 0.73. CONCLUSION: The technologies effectively deal with complex network environment and channel condition variation, ensuring balanced system control, and the signal control effect is outstanding.
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Yang et al. (2025) studied this question. Battery signal control model based on synchronized anti-interference and finite impact response vs. Narrowband interference suppression method and OFDM/OQAM system method was evaluated on Bit error rate (BER) at 18dB signal-to-noise ratio. The proposed battery signal control model achieved a bit error rate of 1×10^-4 at a signal-to-noise ratio of 18dB, with a carrier-to-noise ratio remaining above 0.73 dB, outperforming comparison methods.
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