A Robust Autoencoder HBC Transceiver With CGAN-Based Channel Modeling

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Abdelhay Ali;Amr N. Abdelrahman;Abdulkadir Celik;Mohammed E. Fouda;Ahmed M. Eltawil
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引用次数: 0

Abstract

Human body communication (HBC) offers a promising alternative for efficient and secure data transmission in wearable healthcare systems by leveraging the body’s conductive properties. Using the conductive properties of the human body, HBC offers significant advantages over conventional radio frequency wireless communication methods, including ultralow power consumption and minimal interference. However, HBC systems face key challenges in energy efficiency, data rate optimization, channel adaptability, and accurate body channel modeling. In this article, we present a novel dual-mode HBC transceiver architecture designed to overcome these challenges by integrating autoencoder-based signal processing with generative adversarial network (GAN)-driven channel modeling framework to enhance communication reliability. Operating in both broadband and narrowband modes, the transceiver dynamically adjusts its data rate and power efficiency based on application-specific demands. The design process involves first developing a conditional GAN (CGAN)-based channel model from real HBC measurements, and then using this model to train an autoencoder-based transceiver architecture. Our CGAN framework generates realistic synthetic channel responses for training, enabling the autoencoder to learn optimal encoding and decoding strategies that are robust to channel variations. Subsequently, we developed a low-power hardware architecture that supports flexible data rates of the proposed design while ensuring robust performance in diverse scenarios. This systematic approach provides key advantages: improved channel modeling accuracy achieving a 0.9 correlation coefficient between generated and real channels and mean squared error (mse) of 0.0071, reduced hardware complexity through elimination of digital-to-analog converter (DAC)/analog-to-digital converter (ADC), and flexible operation with dual-mode support. Operating at a clock speed of 42 MHz in the narrowband mode, the transceiver achieves an energy efficiency of 349 pJ/bit at a data rate of 262.5 kb/s with a sensitivity of −64 dBm, appealing for long-range and low-power applications. In broadband mode, the transceiver achieves an energy efficiency of 16 pJ/bit at a data rate of 5.25 Mb/s, suitable for applications demanding high data rates over shorter distances.
基于gan信道建模的鲁棒自编码器HBC收发器
人体通信(HBC)利用人体的导电特性,为可穿戴医疗保健系统中高效、安全的数据传输提供了一种有前途的替代方案。利用人体的导电特性,HBC比传统的射频无线通信方法具有显著的优势,包括超低功耗和最小干扰。然而,HBC系统在能源效率、数据速率优化、信道适应性和准确的身体信道建模方面面临着关键挑战。在本文中,我们提出了一种新的双模HBC收发器架构,旨在通过将基于自编码器的信号处理与生成对抗网络(GAN)驱动的信道建模框架集成在一起,以提高通信可靠性,从而克服这些挑战。该收发器可在宽带和窄带模式下工作,可根据具体应用需求动态调整数据速率和功率效率。设计过程包括首先根据实际HBC测量开发基于条件GAN (CGAN)的信道模型,然后使用该模型训练基于自编码器的收发器架构。我们的CGAN框架为训练生成真实的合成信道响应,使自编码器能够学习对信道变化具有鲁棒性的最佳编码和解码策略。随后,我们开发了一种低功耗硬件架构,支持所建议设计的灵活数据速率,同时确保在不同场景下的稳健性能。该系统方法的主要优点是:提高了通道建模精度,生成通道与真实通道之间的相关系数为0.9,均方误差(mse)为0.0071,通过消除数模转换器(DAC)/模数转换器(ADC)降低了硬件复杂性,以及支持双模式的灵活操作。该收发器在窄带模式下以42 MHz的时钟速度工作,数据速率为262.5 kb/s,能量效率为349 pJ/bit,灵敏度为−64 dBm,适合远距离和低功耗应用。在宽带模式下,收发器以5.25 Mb/s的数据速率实现16 pJ/bit的能量效率,适用于要求在较短距离内实现高数据速率的应用。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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