Pulse waveforms for 60 GHz<i>M</i>‐ary pulse position modulation communication systems

Zhang, Hao; Lu, Tingting; Gulliver, T. Aaron

doi:10.1049/iet-com.2012.0257

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…Both Line of sight (LOS) and NLOS channel models are given for indoor residential, indoor office, industrial, outdoor, and open outdoor environments [20,21] . Several M -ary PPM signal waveforms have been proposed for 60GHz MMW systems [22] . The PPM 60GHz signal can be expressed as:…”

Section: System Modelmentioning

confidence: 99%

NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System

et al. 2018

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Section: System Modelmentioning

confidence: 99%

NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System

et al. 2018

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“…Indoor residential, indoor office, and library channel models are provided for both line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) environments [18, 19]. In this paper, a pulse position modulation time hopping 60 GHz wideband signal [20] is employed which can be expressed as

s ()(, t) = \sum_{j = - normal∞}^{\infty} p (t - j T_{normals} - C_{j} T_{normalc} - a_{j} ε)

where the frame index and frame duration are j and

T_{s}

, respectively. Time‐hopping codes

C_{j} \in (}{, 0, 1, \dots, N_{h} - 1)

are employed where

N_{h} = T_{s} / T_{c}

is the number of chip positions per frame and T c is the chip duration.…”

Section: 60 Ghz System Modelmentioning

confidence: 99%

Extreme learning machine for 60 GHz millimetre wave positioning

Liang

Zhang

et al. 2017

IET Communications

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“…Several M-ary PPM signal waveforms have been proposed for 60 GHz MMW systems [24]. In this paper, the 60 GHz PPM signal is given by…”

Section: System Modelmentioning

confidence: 99%

Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals

2016

KSII TIIS

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Neural networks (NNs) are extensively used in applications requiring signal classification and regression analysis. In this paper, a NN based threshold selection algorithm for 60 GHz millimeter wave (MMW) time of arrival (TOA) estimation using an energy detector (ED) is proposed which is based on the skewness, kurtosis, and curl of the received energy block values. The best normalized threshold for a given signal-to-noise ratio (SNR) is determined, and the influence of the integration period and channel on the performance is investigated. Results are presented which show that the proposed NN based algorithm provides superior precision and better robustness than other ED based algorithms over a wide range of SNR values. Further, it is independent of the integration period and channel model.

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Pulse waveforms for 60 GHzM‐ary pulse position modulation communication systems

Cited by 12 publications

References 18 publications

NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System

NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System

Extreme learning machine for 60 GHz millimetre wave positioning

Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals

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