Battery signal control model for large-scale IoT medical monitors under multipath interference

Shuhua Yang; Shengnan Zhang; Ding Chen; Syed Atif Moqurrab

doi:10.4108/eetpht.11.5832

Authors

Shuhua Yang Nanchang Jiaotong Institute
Shengnan Zhang Nanchang Jiaotong Institute
Ding Chen Nanchang Jiaotong Institute
Syed Atif Moqurrab University of Southampton

DOI:

https://doi.org/10.4108/eetpht.11.5832

Keywords:

Multipath interference, Internet of Things, Signal control, Impulse response, Intercoder interference

Abstract

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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References

[1] Raj S. An Efficient IoT-Based Platform for Remote Real-Time Cardiac Activity Monitoring[J]. IEEE Transactions on Consumer Electronics, 2020, 66(02):106-114.

[2] Yan S, Bu X, Zhu P, et al. A Queue Length Balance Control Method for Intersection in the Case of Data Packet Dropout[J]. Computer Engineering, 2021, 47(1): 21-29.

[3] Cao Z, Liu Q, Liu Di, et al. Traffic Scheduling Method for Time-Sensitive Network[J]. Computer Engineering, 2021, 47(7): 168-175, 182.

[4] Zhan Y, Zeng G, Duan C. A high order statistics based multipath interference detection method [J]. IET Communications, 2021, 15 (12):1586-1596.

[5] Nie R, Li B. Detection and simulation of quasi random frequency hopping signal based on interference analysis algorithm[J]. Neural Computing & Applications, 2023, 35(12): 8847-8858.

[6] Kumar JT, Sridevi CH, Kumar VS. Blind signal-to-interference-plus-noise ratio estimation of OFDM/OQAM system in radio frequency interference environment[J]. Soft computing, 2023, 27(1):529-536.

[7] Niu Y, Yao X, Zhang K. Interference-free power control algorithm for wireless communication systems based on interference observation[J]. Journal of Electronics & Information Technology, 2023, 45(11):4033-4040.

[8] Bai G, Cheng Y, Tang W. Research on Single Carrier Frequency Domain Equalization Algorithm Based on Deep Learning[J]. Journal of Signal Processing, 2021, 37(06): 922-931.

[9] Koseoglou M, Tsioumas E, Jabbour N, et al. Highly Effective Cell Equalization in a Lithium-Ion Battery Management System[J]. IEEE Transactions on Power Electronics, 2020, 35(02):2088-2099.

[10] Xiao Y, Xia K, Yin H, et al. AFSTGCN: prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network[J], Digital Communications and Networks, 2024, 10(2): 292-303.

[11] Jiang C, Chen S, Chen Y, et al. An UWB Channel Impulse Response De-Noising Method for NLOS/LOS Classification Boosting [J]. IEEE Communications Letters, 2020, 24(11):2513-2517.

[12] Liu S, Chen X, Li Y, et al. Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis, Symmetry[J], 2019, 11, 1471.

[13] Jian, WU, Xiao mei, et al. Unbiased Interference Suppression Method Based on Spectrum Compensation [J]. IEICE Transactions on Communications, 2020, 103(01):52-59.

[14] Mu J, Wei Y, Ma H, et al. Spectrum Allocation Scheme for Intelligent Partition Based on Machine Learning for Inter-WBAN Interference [J]. IEEE Wireless Communications, 2020, 27(5):32-37.

[15] Ge Y, Deng Q, Ching P C, et al. Receiver Design for OTFS with a Fractionally Spaced Sampling Approach [J]. IEEE transactions on wireless communications, 2021, 20(7):4072-4086.

[16] Liu S, Bai W, Srivastava G, et al. Property of Self-Similarity Between Baseband and Modulated Signals[J]. Mobile Networks & Applications, 2020, 25(4): 1537-1547

[17] Zhang Y, Satapathy S C, Guttery D S, et al. Improved breast cancer classification through combining graph convolutional network and convolutional neural network[J], Information Processing and Management, 2021, 58(2), 102439

[18] Song L, Liu X, Chen S, et al. A deep fuzzy model for diagnosis of COVID-19 from CT images[J]. Applied Soft Computing, 2022, 122: 108883