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Proceedings ArticleDOI

HDM: Hyper-Dimensional Modulation for Robust Low-Power Communications

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TLDR
Analysis reveals HDM demodulation complexity is lower than that of LDPC and Polar decoders when the block length is relatively small, and provides graceful tradeoffs between data rate and signal-to-noise ratio for robust short message communications among power- and complexity- constrained devices.
Abstract
This paper introduces hyper-dimensional modulation (HDM), a new class of practical modulation scheme for robust communication among low-power and low- complexity devices. Unlike conventional orthogonal modulations, HDM conveys numerous information bits per symbol by combining hyper-dimensional vectors that are not strictly orthogonal to each other. Information bits are spread across many elements in the hyper-dimensional vector, thus HDM is tolerant of element-wise failures in high noise channels. Bit error rate (BER) evaluation results confirm that uncoded HDM with 256-dimension outperforms low density parity check (LDPC) and Polar codes with the same block length of 256. Analysis reveals HDM demodulation complexity is lower than that of LDPC and Polar decoders when the block length is relatively small. Moreover, HDM provides graceful tradeoffs between data rate and signal-to-noise ratio for robust short message communications among power- and complexity- constrained devices.

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Proceedings ArticleDOI

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References
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Book

Digital Communications

Digital communications

J.E. Mazo
TL;DR: This month's guest columnist, Steve Bible, N7HPR, is completing a master’s degree in computer science at the Naval Postgraduate School in Monterey, California, and his research area closely follows his interest in amateur radio.
Book

OFDM for Wireless Multimedia Communications

TL;DR: In this paper, the authors present a comprehensive introduction to OFDM for wireless broadband multimedia communications and provide design guidelines to maximize the benefits of this important new technology, including modulation and coding, synchronization, and channel estimation.
Journal ArticleDOI

Compressive Sensing [Lecture Notes]

TL;DR: This lecture note presents a new method to capture and represent compressible signals at a rate significantly below the Nyquist rate, called compressive sensing, which employs nonadaptive linear projections that preserve the structure of the signal.
Journal ArticleDOI

Subspace Pursuit for Compressive Sensing Signal Reconstruction

TL;DR: The presented analysis shows that in the noiseless setting, the proposed algorithm can exactly reconstruct arbitrary sparse signals provided that the sensing matrix satisfies the restricted isometry property with a constant parameter.
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