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Related Concept Videos

Cut-off Frequency of BJT01:17

Cut-off Frequency of BJT

717
Cut-off frequencies in Bipolar Junction Transistors (BJTs) mark the transition between the signal's pass band and stop band, influencing their performance in amplifying or attenuating frequencies. These frequencies are crucial for designing BJTs to meet specific operational requirements in electronic circuits.
Alpha Cut-Off Frequency: Pertinent to the common-base configuration, the alpha cut-off frequency defines the upper-frequency limit at which the current gain, alpha, remains stable. As...
717
Frequency Response of BJT01:24

Frequency Response of BJT

850
The frequency response of a Bipolar Junction Transistor (BJT) in a common-emitter configuration is critical to its functionality, especially in applications involving amplification of alternating current (AC) signals. This response can be analyzed through low-frequency and high-frequency equivalent circuits, considering various internal parameters and external conditions.
Low-Frequency Response: At low frequencies, the behavior of the BJT is determined by its DC bias point, which is set by the...
850
Characteristics of Series Resonant Circuit01:24

Characteristics of Series Resonant Circuit

258
Series resonance occurs in a circuit containing inductive (L), capacitive (C), and resistive (R) elements connected sequentially. At the resonance frequency, the inductive and capacitive reactances are equal in magnitude but opposite in sign, effectively canceling each other. This causes the circuit's impedance is minimal, primarily determined by the resistance R. The resonant frequency of an RLC circuit is defined as:
258
Small-Signal Analysis of BJT Amplifiers01:21

Small-Signal Analysis of BJT Amplifiers

1.1K
Small signal analysis is a fundamental approach used in electronics to understand how a Bipolar Junction Transistor (BJT) amplifier processes signals. In the active region, the BJT is designed for linear amplification. The transistor's behavior under these conditions is governed by its instantaneous base-emitter voltage VBE, a sum of the DC bias VBE, and a small AC signal VBE, resulting in the collector current iC. Here, the collector current has a DC component and an AC component.
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BBR-n+ congestion control: Real-time performance with smart exit and advanced AQMs.

PloS one·2026
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TCP BBR-n interplay with modern AQM in Wireless-N/AC networks: Quest for the golden pair.

PloS one·2024
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Related Experiment Video

Updated: Jul 8, 2025

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
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TCP BBR-n: Increased throughput for wireless-AC networks.

Muhammad Ahsan1, Sajid S Muhammad1

  • 1Department of Electrical Engineering, National University of Computer & Emerging Sciences, Lahore, Punjab, Pakistan.

Plos One
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

A new algorithm, BBR-n, significantly improves throughput and reduces latency in Wi-Fi 5 networks. This Bottleneck Bandwidth and Round-trip propagation time (BBR) enhancement outperforms standard BBR v2 and older protocols like Cubic and Reno.

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Area of Science:

  • Computer Networking
  • Network Performance Optimization
  • Wireless Communication

Background:

  • Google's Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm models network paths for optimal bandwidth and minimal latency.
  • BBR v2 aimed to improve upon BBR v1, addressing fairness and retransmission issues.
  • Standard BBR v2 exhibits limitations in IEEE 802.11ac (Wi-Fi 5) networks, causing reduced throughput and increased latency due to underutilized frame aggregation.

Purpose of the Study:

  • To address the performance limitations of BBR v2 in Wi-Fi 5 networks.
  • To propose a novel congestion control algorithm, BBR-n, for enhanced Wi-Fi 5 performance.
  • To demonstrate superior throughput and reduced latency compared to existing algorithms.

Main Methods:

  • Development of the BBR-n (BBR new) algorithm.
  • Real-time experimental validation on a physical testbed.
  • Performance comparison using the Flent tool.

Main Results:

  • BBR-n achieved more than double the throughput compared to generic BBR v2 in Wi-Fi 5 networks.
  • BBR-n demonstrated reduced latency compared to BBR v2.
  • Significant performance gains were observed over loss-based variants like Cubic and Reno.

Conclusions:

  • BBR-n offers a substantial improvement over BBR v2 for Wi-Fi 5 networks.
  • The proposed algorithm effectively overcomes the limitations of standard BBR v2 in this specific wireless environment.
  • BBR-n presents a viable solution for optimizing throughput and latency in modern wireless networks.