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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
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Superimposed CSI Feedback Assisted by Inactive Sensing Information.

Mintao Zhang1, Haowen Jiang1, Zilong Wang1

  • 1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for massive MIMO systems using inactive sensing data to improve channel state information (CSI) feedback. This approach enhances uplink bandwidth efficiency and data recovery by reducing interference without extra hardware.

Keywords:
channel state information (CSI)delay–Doppler (DD) domaininactive sensing informationsensing-assisted communicationsuperimposed CSI feedback

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Area of Science:

  • Wireless communication
  • Signal processing
  • Machine learning

Background:

  • Massive MIMO systems use superimposed CSI feedback to optimize uplink bandwidth.
  • Superimposed feedback causes interference, degrading CSI and data recovery.
  • ML methods mitigate interference but are resource-intensive.

Purpose of the Study:

  • To propose a novel superimposed CSI feedback method using inactive sensing information.
  • To enhance CSI recovery accuracy and uplink data transmission in massive MIMO.
  • To reduce system resource burden compared to existing ML methods.

Main Methods:

  • Utilizing previously unutilized inactive sensing data (location, speed, path) at the base station.
  • Developing a new modal data type for interference suppression without additional hardware.
  • Applying denoising in the delay-Doppler domain based on derived prior information.

Main Results:

  • Significant enhancement in downlink CSI and uplink data sequence recovery accuracy.
  • Improved performance compared to classic and novel superimposed CSI feedback methods.
  • Demonstrated robustness against parameter variations.

Conclusions:

  • Inactive sensing information can effectively enhance superimposed CSI feedback.
  • The proposed method improves performance and is resource-efficient.
  • This is the first approach to leverage inactive sensing data for superimposed CSI feedback.