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Updated: Sep 26, 2025

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Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM.

Andrei Stefan Nedelcu1, Fabian Steiner2, Gerhard Kramer2

  • 1Optical and Quantum Laboratory, Huawei Munich Research Center, 80992 Munich, Germany.

Entropy (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

A new Quantized Coordinate Minimization (QCM) algorithm offers high information rates and low complexity for downlink precoding in multi-path channels. Its performance rivals advanced methods, making it efficient for 5G systems.

Keywords:
coarse quantizationcoordinate descentinformation ratesmassive MIMOprecoding

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Area of Science:

  • Wireless communication systems
  • Signal processing
  • Information theory

Background:

  • Downlink precoding is crucial for optimizing data transmission in multi-path wireless channels.
  • Orthogonal frequency-division multiplexing (OFDM) and low-resolution signaling present unique challenges for precoding efficiency.
  • Existing precoding algorithms like SQUID, MAGIQ, and maximum safety margin precoding have limitations in performance or complexity.

Purpose of the Study:

  • To propose and evaluate a novel Quantized Coordinate Minimization (QCM) algorithm for downlink precoding.
  • To compare the performance of QCM against established precoding techniques in multi-path MIMO channels.
  • To assess the impact of channel estimation and low-resolution signaling on precoding effectiveness.

Main Methods:

  • Development of the Quantized Coordinate Minimization (QCM) algorithm for low-resolution downlink precoding.
  • Performance evaluation using information rate calculations under pilot-aided channel estimation and blind detection.
  • Bit error rate (BER) analysis using 5G low-density parity-check (LDPC) codes to validate information-theoretic results.
  • Simulations examining performance degradation with imperfect channel knowledge at the transmitter.

Main Results:

  • The proposed QCM algorithm achieves high information rates, comparable to the MAGIQ algorithm.
  • QCM demonstrates the lowest computational complexity, measured by the number of multiplications.
  • Simulations confirm that QCM and SQUID exhibit similar performance degradation under imperfect channel knowledge, akin to high-resolution zero-forcing precoding.

Conclusions:

  • QCM is an effective and computationally efficient algorithm for downlink precoding in low-resolution signaling environments.
  • The algorithm's performance is robust, maintaining high information rates even with channel estimation challenges.
  • QCM offers a practical solution for advanced wireless systems like 5G, balancing performance and complexity.