Experimental Evaluation of Implant UWB-IR Transmission With Living Animal for Body Area Networks

Anzai, Daisuke; Katsu, Kenta; Chávez-Santiago, Raúl; Wang, Qiong; Plettemeier, Dirk; Wang, Jianqing; Balasingham, Ilangko

doi:10.1109/tmtt.2013.2291542

Cited by 80 publications

(47 citation statements)

References 22 publications

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“…10 and 11, and a sample of the test data is given in Table 4. Similar to (10) and (11), the RMSE is calculated in this case as…”

Section: Resultsmentioning

confidence: 99%

“…However, for the implant communications scenario, it is simply not feasible to acquire measurements from living human bodies in the same manner. Therefore, Several prior works regarding propagation modeling for implant communications and the UWB case in particular can be found in the literature [10][11][12][13]. To the best of the authors' knowledge, the most recent contribution in this area was again the authors' own, wherein it was attempted to model the frequency-dependent path loss characteristics based on results of finite difference time domain (FDTD) simulations [14].…”

Section: Introductionmentioning

confidence: 99%

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A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Kanaan

Suveren

2016

Neural Comput & Applic

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We report on the results of our investigations into more computationally efficient methods for propagation modeling in the implant environment through the use of neural networks. Our results are applicable to the important case of ultra-wideband systems operating in the implant environment. A propagation modeling engine based on neural networks can help the system designer avoid time-consuming numerical simulations normally utilized for propagation modeling. The contributions of this paper are as follows. We perform a systematic investigation of two neural network architectures for propagation modeling, namely the multilayer perceptron (MLP) and the recurrent Elman network. We perform a quantitative comparison of the results produced by these networks to the results produced by time-consuming numerical methods, such as the finite difference time domain (FDTD) method. Our results indicate that it is indeed possible to obtain path loss results using neural network methods that approach FDTD results within a 3-to 4-dB margin of error. We further illustrate that it is possible to attain this level of accuracy through the simpler MLP structure, and that the increased complexity and training time associated with the Elman network is unwarranted. We also observe that the MLP network can attain this performance through reasonable network sizes of 20-30 neurons.

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“…10 and 11, and a sample of the test data is given in Table 4. Similar to (10) and (11), the RMSE is calculated in this case as…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Kanaan

Suveren

2016

Neural Comput & Applic

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“…Anzai et al [13] conducted experiments on animals using UWB impulse radio, which resulted in a path of 80dB and a bit error rate (BER) of 10 −2 within the distance of 120mm, with a high data rate of 1Mb/s. In [15], Propagation model was proposed for UWB body-centric wireless communication.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Ultra Wideband <italic>in Vivo</italic> Radio Channel

et al. 2018

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This paper proposes a novel mathematical model for an in vivo radio channel at ultra-wideband frequencies (3.1-10.6 GHz), which can be used as a reference model for in vivo channel response without performing intensive experiments or simulations. The statistics of error prediction between experimental and proposed model is RMSE = 5.29, which show the high accuracy of the proposed model. Also, the proposed model was applied to the blind data, and the statistics of error prediction is RMSE = 7.76, which also shows a reasonable accuracy of the model. This model will save the time and cost on simulations and experiments, and will help in designing an accurate link budget calculation for a future enhanced system for ultra-wideband body-centric wireless systems.INDEX TERMS Channel characterization, implants/wearable devices, in vivo channel, mathematical modeling, wireless body area networks.

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“…The validation of numerical studies with real experimental measurements is required, however performing experiments on a living human is strictly regulated. Therefore, physical phantoms [4], [8], [16] or anesthetized animals [5], [6] are often used for experimental investigations.…”

Section: Introductionmentioning

confidence: 99%

Anatomical Region-Specific In Vivo Wireless Communication Channel Characterization

Demir

Abbasi

Ankarali

et al. 2017

IEEE J. Biomed. Health Inform.

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Abstract-In vivo wireless body area networks (WBANs) and their associated technologies are shaping the future of healthcare by providing continuous health monitoring and noninvasive surgical capabilities, in addition to remote diagnostic and treatment of diseases. To fully exploit the potential of such devices, it is necessary to characterize the communication channel which will help to build reliable and high-performance communication systems. This paper presents an in vivo wireless communication channel characterization for male torso both numerically and experimentally (on a human cadaver) considering various organs at 915 MHz and 2.4 GHz. A statistical path loss (PL) model is introduced, and the anatomical region-specific parameters are provided. It is found that the mean PL in dB scale exhibits a linear decaying characteristic rather than an exponential decaying profile inside the body, and the power decay rate is approximately twice at 2.4 GHz compared to 915 MHz. Moreover, the variance of shadowing increases significantly as the in vivo antenna is placed deeper inside the body since the main scatterers are present in the vicinity of the antenna. Multipath propagation characteristics are also investigated to facilitate proper waveform designs in the future wireless healthcare systems, and a rootmean-square (RMS) delay spread of 2.76 ns is observed. Results show that the in vivo channel exhibit different characteristics than the classical communication channels, and location dependency is very critical for accurate, reliable, and energy-efficient link budget calculations.Index Terms-Channel characterization, implants, in/on-body communication, in vivo, wireless body area networks (WBANs).

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Experimental Evaluation of Implant UWB-IR Transmission With Living Animal for Body Area Networks

Cited by 80 publications

References 22 publications

A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

A new propagation modeling technique for ultra-wideband implant body area networks based on a neural network architecture

Mathematical Modeling of Ultra Wideband <italic>in Vivo</italic> Radio Channel

Anatomical Region-Specific In Vivo Wireless Communication Channel Characterization

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