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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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An Enhanced Decoding Algorithm for Coded Compressed Sensing with Applications to Unsourced Random Access.

Vamsi K Amalladinne1, Jamison R Ebert2, Jean-Francois Chamberland2

  • 1Qualcomm Technologies, Inc., San Diego, CA 92121, USA.

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Summary
This summary is machine-generated.

This study introduces an enhanced decoding algorithm for concatenated codes in unsourced random access (URA) systems. The new method improves error performance while reducing computational complexity for sensor networks.

Keywords:
coded compressed sensingconcatenated codessuccessive cancellation list decodingunsourced random access

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Information Theory

Background:

  • Unsourced random access (URA) is crucial for next-generation distributed sensor networks.
  • Concatenated coding structures are vital in URA for accurate codeword recovery by base stations.
  • Current URA algorithms often use independent decoders, balancing complexity and performance.

Purpose of the Study:

  • To present an enhanced decoding algorithm for concatenated codes in URA systems.
  • To demonstrate the potential for simultaneous improvement in error performance and reduction in computational complexity.
  • To evaluate the benefits of this enhanced algorithm on existing URA applications.

Main Methods:

  • Development of an enhanced decoding algorithm for concatenated codes (inner codes + outer tree-based code).
  • Application of the enhanced algorithm to two established URA algorithms.
  • Performance characterization through numerical simulations.

Main Results:

  • The enhanced decoding algorithm shows potential to improve error performance.
  • The algorithm also demonstrates a decrease in computational complexity.
  • Simulations confirm performance benefits when applied to existing URA algorithms.

Conclusions:

  • The enhanced decoding algorithm offers a significant advancement for URA systems.
  • This approach provides a practical solution for balancing performance and complexity in sensor networks.
  • Further application and validation of the algorithm are supported by simulation results.