Propagation Models for Body-Area Networks: A Survey and New Outlook

Smith, David B.; Miniutti, Dino; Lamahewa, Tharaka A.; Hanlen, Leif

doi:10.1109/map.2013.6735479

Cited by 191 publications

(91 citation statements)

References 71 publications

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“…The model in short link lengths, as body area networks, should be a function of shadowing degree instead of the distance but the degree of the shadowing for a link is a difficult task to solve objectively. The possible unsuitability of the classical path loss model (1) for BANs is discussed also in [37]. The standard deviation σ S is calculated from the decibel values after normalizing the measured values around the x-axis.…”

Section: Path-loss Modelsmentioning

confidence: 99%

Categorized Uwb on-Body Radio Channel Modeling for Wbans

Kumpuniemi¹,

Hämäläinen²,

Yazdandoost³

et al. 2016

PIER B

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Abstract-A categorized radio channel modeling for wireless ultra-wideband on-body body area networks is discussed. Measurements in an anechoic chamber at fourteen antenna locations are conducted in a 2-8 GHz band. The dipole and double loop antenna types are used. Six link classes are formed based on the antenna spots on the torso, head or limb. The limb-limb and the head-limb links have the lowest and highest path losses, respectively. The head-limb links have the shortest channel impulse responses (CIRs) and limb-limb links the longest ones. The CIR amplitudes follow the inverse Gaussian distribution. The tap indexes and the total excess delays are modeled with the negative binomial distribution. In most cases, the CIRs decay faster for the dipole. Otherwise no major differences exist between the antennas.

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Section: Path-loss Modelsmentioning

confidence: 99%

Categorized Uwb on-Body Radio Channel Modeling for Wbans

Kumpuniemi¹,

Hämäläinen²,

Yazdandoost³

et al. 2016

PIER B

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“…This is seen by path losses for set of activities (standing, walking and running) given in Table 4 and 5 for different body positions at 2.36GHz [8]. The results showed that the sensor on the wrist provided the most reliable communications.…”

Section: Present Status Of Wban Modelsmentioning

confidence: 87%

Survey on Empirical Channel Models for WBAN

Kaur¹,

Malhotra²

2015

IJFGCN

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“…These models are required not only to optimize the quality of service metrics such as high data rate, low bit-error rate, and latency but also to ensure the safety of biological tissues by careful link budget evaluations. Although, on-body wireless communication channel characteristics have been thoroughly investigated [3], [11], the studies are limited on the in vivo wireless communication channels (implant-to-implant and implant-to-external device links) are limited. The in vivo channel exhibits different characteristics than those of the more familiar wireless cellular and Wi-Fi environments since the electromagnetic (EM) wave propagates through a very lossy environment inside the body and dominant scatterers are present in the near-field region of the antenna.…”

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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Propagation Models for Body-Area Networks: A Survey and New Outlook

Cited by 191 publications

References 71 publications

Categorized Uwb on-Body Radio Channel Modeling for Wbans

Categorized Uwb on-Body Radio Channel Modeling for Wbans

Survey on Empirical Channel Models for WBAN

Anatomical Region-Specific In Vivo Wireless Communication Channel Characterization

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