Bit-Loaded PAPR Reduction for High-Data-Rate Through-Metal Control Network Applications

Bielinski, Magdalena; Sosa, Guillermo; Wanuga, Kevin; Primerano, Richard; Kam, Moshe; Dandekar, Kapil R.

doi:10.1109/tie.2013.2272283

Cited by 12 publications

(3 citation statements)

References 27 publications

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“…In many sensing and control scenarios, signal transmission through metal barriers is necessary without physically penetrating them as much as possible. This includes monitoring the internal running status of a nuclear reactor [1,2], acquiring information from sensors deployed in high-pressure pipes [3,4], and passing data from one side of a watertight bulkhead to the other [5]. Traditional wireless communication based on electromagnetic waves is ineffective due to the Faraday shielding effect.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation

Zhang,

Jiang,

Zhang

et al. 2024

Sensors

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Ultrasound is extremely efficient for wireless signal transmission through metal barriers due to no limit of the Faraday shielding effect. Echoing in the ultrasonic channel is one of the most challenging obstacles to performing high-quality communication, which is generally coped with by using a channel equalizer or pre-distorting filter. In this study, a deep learning algorithm called a dual-path recurrent neural network (DPRNN) was investigated for echo cancellation in an ultrasonic through-metal communication system. The actual system was constructed based on the combination of software and hardware, consisting of a pair of ultrasonic transducers, an FPGA module, some lab-made circuits, etc. The approach of DPRNN echo cancellation was applied to signals with a different signal-to-noise ratio (SNR) at a 2 Mbps transmission rate, achieving higher than 20 dB SNR improvement for all situations. Furthermore, this approach was successfully used for image transmission through a 50 mm thick aluminum plate, exhibiting a 24.8 dB peak-signal-to-noise ratio (PSNR) and a about 95% structural similarity index measure (SSIM). Additionally, compared with three other echo cancellation methods—LMS, RLS and PNLMS—DPRNN has demonstrated higher efficiency. All those results firmly validate that the DPRNN algorithm is a powerful tool to conduct echo cancellation and enhance the performance of ultrasonic through-metal transmission.

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Section: Introductionmentioning

confidence: 99%

Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation

Zhang,

Jiang,

Zhang

et al. 2024

Sensors

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“…The high PAPR is however, one of the main drawbacks of a massive MIMO-OFDM system when all subcarriers of OFDM sum up in a coherent way. In recent years, numerous PAPR reduction schemes [1][2][3][4][5][6], have been developed. In [5] the authors proposed a Hybrid PAPR reduction technique which is a combination of Turbo coding and PTS scheme based on ESD algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Distributive Population-Based Differential Evolution Algorithm for SLM Scheme to Reduce PAPR in Massive MIMO-OFDM Systems

Rakshit

Bhattacharjee²,

Garai

et al. 2020

SN COMPUT. SCI.

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To achieve the ever rising demand for high data rates with high spectral efficiency, the energy efficient massive Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) will be essential and useful for future wireless communication systems as well as Industrial network applications to reduce the operational costs significantly. But the high Peak to Average Power Ratio (PAPR) in a massive MIMO-OFDM system is identified as a critical issue that causes the non-linear signal distortion and restricts the efficiency of the power amplifier. Among different PAPR reduction schemes, Selected Mapping (SLM) is a distortionless technique and highly attractive due to its simple implementation capability. However, the conventional SLM scheme is inefficient for the massive MIMO-OFDM antenna system for its enormous Side Information (SI) burden and large computational complexity to search the best set of phase factors. In this paper, a Distributive Population based Switching Differential Evolution strategy is incorporated in the SLM scheme which eventually enhances the searching ability. The algorithm is employed in different antenna grouping (homogeneous and heterogeneous) based massive MIMO systems. The antenna grouping concept in the massive MIMO system minimizes the SI burden. The proposed algorithm for SLM techniques outperforms the existing techniques in terms of PAPR, Bit Error Rate, SI burden, energy efficiency and computational complexity.

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“…This type of system is useful in low-rate telemetry applications. Dedicated data implementations of these systems [4][5][6] have been shown to be capable of high data-rate communications, with both implementations employing multicarrier transmission techniques to achieve over 15 Mbps data-rates, with predictions of much higher rates being possible. Conversely, for strictly power transmission, a system [7] composed of disk transducers across a thick stainless steel barrier was shown to be capable of delivering at least 140 W with an approximate efficiency of 67 percent at 1.25 MHz; whereas Bao et al [8] demonstrated delivery of over 1 kW at 24.5 kHz with an efficiency of 84 percent while using highly specialized piezoelectric transducer stacks operating on a thin titanium barrier.…”

Section: Introductionmentioning

confidence: 99%

One-dimensional pressure transfer models for acoustic–electric transmission channels

Wilt

Lawry

Scarton

et al. 2015

Journal of Sound and Vibration

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Bit-Loaded PAPR Reduction for High-Data-Rate Through-Metal Control Network Applications

Cited by 12 publications

References 27 publications

Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation

Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation

A Novel Distributive Population-Based Differential Evolution Algorithm for SLM Scheme to Reduce PAPR in Massive MIMO-OFDM Systems

One-dimensional pressure transfer models for acoustic–electric transmission channels

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