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Related Concept Videos

Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Beams with Symmetric Loadings01:15

Beams with Symmetric Loadings

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The M/EI...
Beams with Unsymmetric Loadings01:17

Beams with Unsymmetric Loadings

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Differential Leveling01:12

Differential Leveling

Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
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Related Experiment Video

Updated: May 14, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

Deep Learning-Based Dynamic Time Division ISAC Beamforming for Vehicular Networks.

Junseok Lim1, Jaewoo So1

  • 1Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic approach for integrated sensing and communication (ISAC) systems in vehicles. The new method optimizes the balance between sensing accuracy and communication speed, outperforming fixed systems.

Keywords:
Cramér–Rao lower bound (CRLB)ISAC beamformingdynamic time divisionintegrated sensing and communications (ISAC)proximal policy optimization (PPO)vehicular communications

Related Experiment Videos

Last Updated: May 14, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Telecommunications

Background:

  • Integrated Sensing and Communication (ISAC) systems enable vehicular networks to combine data transmission and sensing.
  • Conventional ISAC systems use fixed time division ratios, creating a suboptimal trade-off between sensing accuracy and communication throughput in dynamic environments.

Purpose of the Study:

  • To propose a dynamic sensing-communication time division and ISAC beamforming scheme for vehicular networks.
  • To optimize the performance of ISAC systems in high-mobility vehicular environments by dynamically adjusting the sensing-communication split.

Main Methods:

  • Developed a dynamic sensing-communication time division and ISAC beamforming scheme.
  • Utilized a deep reinforcement learning framework with proximal policy optimization to determine optimal time division ratios and beamforming vectors.
  • Minimized the Cramér-Rao lower bound while ensuring a minimum effective communication sum rate.

Main Results:

  • The proposed dynamic scheme significantly improved sensing accuracy compared to fixed time division schemes.
  • The dynamic approach also enhanced communication sum rates.
  • Simulation results validated the superiority of the dynamic time division beamforming scheme.

Conclusions:

  • Dynamic adjustment of the sensing-communication split and beamforming is crucial for optimizing ISAC performance in vehicular networks.
  • The proposed deep reinforcement learning-based method effectively manages the trade-offs in high-mobility scenarios.
  • This approach offers a significant advancement over conventional fixed-ratio ISAC systems.