# Research of Wireless Congestion Control Algorithm Based on EKF

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## Abstract

**:**

## 1. Introduction

- (1)
- First, due to the influence of link state, medium competition, signal strength, and various interference factors, the available bandwidth of the wireless network changes significantly in a dynamical and random manner. These make it impossible for TCPW to determine the actual amount of data to be sent, according to the change of bandwidth in real time. Hence, it leads to a significant decrease in the transmission performance of wireless network.
- (2)
- In addition, the weight of the filter used by the original bandwidth estimation algorithm is fixed in TCPW [12] and an improved algorithm (TCPW-M) [13], so the bandwidth cannot be estimated stably. By introducing the filtering technology into the estimation of the bandwidth, the impact of the frequent jitter of the rapid change of the wireless network bandwidth is effectively reduced. Thereby, a relatively smooth bandwidth estimation value that meets the actual situation is obtained. Thereafter, in order to ensure the smoothness of the estimated bandwidth, the filtering technique in the TCPW bandwidth estimated (TCPW BE) algorithm utilizes randomly varying weights. However, the estimation value of the method is high and the accuracy is insufficient. Then, the TCPW BE occupies excessive bandwidth.
- (3)
- Finally, there are many reasons for packet loss, and wireless network congestion is only one possibility. In addition, signal attenuation or external interference can cause noise to be lost. When the link environment of the wireless network is poor and noise packet loss occurs frequently, TCPW does not take different operations according to the packet loss type. As long as the packet is lost, TCPW is called to reduce the slow start threshold and the size of the congestion window. As a result, the throughput of the system is severely reduced and the performance of the wireless network is affected to a large extent.

## 2. Research Background and Related Works

## 3. Model of Bandwidth Prediction Based on Extended Kalman Filtering (EKF)

#### 3.1. Analysis of Available Bandwidth

#### 3.2. System Modeling

**Definition**

**1.**

**Definition**

**2.**

^{−2}or 10

^{−5}, while the non-diagonal elements are set to zero.

#### 3.3. Bandwidth Estimation

#### 3.3.1. Prediction Process

#### 3.3.2. Calibration Process

## 4. Congestion Control Algorithm

Algorithm 1. Wireless congestion control based on EKF |

//This algorithm would be executed after the predicted available bandwidth was updated using the extended Kalman filter algorithm |

Input: $ssthresh$, $cwnd$, ${A}^{\tau}$, $Rt{t}_{min}$, $MSS$ |

Output: $cwnd$, $ssthresh$ |

Begin |

//Adjust the congestion window and threshold based on the latest bandwidth estimates |

For Every Receive ACK Do |

Set $Rt{t}_{smo}=Rt{t}_{all}/n$ |

If ${C}_{act}>{C}_{opti}$ Then |

Set $Rt{t}_{min}=Rtt$ |

Else |

Set ${C}_{act}=cwnd/Rt{t}_{smo}$ |

Set ${C}_{opti}=cwnd/Rt{t}_{min}$ |

Compute ${F}_{np}=(Rt{t}_{smo}-Rt{t}_{{}_{min}})\times {C}_{act}$ |

If ${F}_{np}<f$ Then |

Set $ssthresh=cwnd$ |

Set $cwnd=cwnd-{F}_{np}$ |

Else If Fast_Recovery==True Then |

Set $ssthresh=\frac{{A}^{\tau}\times Rt{t}_{\mathrm{min}}}{MSS}$ |

If $cwnd>ssthresh$ Then |

Set $cwnd=ssthresh$ |

Else |

Set $cwnd=cwnd$ |

If Retransmission==True Then |

Set $cwnd=1$ |

Set $ssthresh=\frac{{A}^{\tau}\times Rt{t}_{\mathrm{min}}}{MSS}$ |

End |

## 5. Simulation Experiment and Result Analysis

#### 5.1. Experimental Environment

#### 5.2. Experimental Process

#### 5.3. Fairness and Friendliness

#### 5.4. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Flow chart of control scheme based on extended Kalman filtering and bandwidth estimation (CSEKB).

Parameter | Range of Values |
---|---|

Bottleneck bandwidth | 1 Mbps–20 Mbps |

Packet size | 1 KB/Packet–150 KB/Packet |

Number of FTP streams | 5–50 |

One-way delay | 5 ms–10 ms |

Send rate | 20 Packets/s |

BER | [0.01, 0.05] |

Simulation time | [0, 150 s] |

Protocol | N | Mean | Std. Deviation | Std. Error |
---|---|---|---|---|

CSEKB | 20,000 | 11.89 | 0.0278 | 0.0052 |

EBE | 20,000 | 10.01 | 0.0291 | 0.0053 |

CUBIC | 20,000 | 9.89 | 0.0317 | 0.0057 |

TCPW | 20,000 | 7.38 | 0.0301 | 0.0054 |

Protocol | F | Sig. |
---|---|---|

TCPW | 47,981.733 | 0.000 |

CUBIC | 52,787.032 | 0.000 |

EBE | 63,841.245 | 0.000 |

CSEKB | 71,608.750 | 0.000 |

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**MDPI and ACS Style**

Wang, H.; Tang, J.; Hong, B.
Research of Wireless Congestion Control Algorithm Based on EKF. *Symmetry* **2020**, *12*, 646.
https://doi.org/10.3390/sym12040646

**AMA Style**

Wang H, Tang J, Hong B.
Research of Wireless Congestion Control Algorithm Based on EKF. *Symmetry*. 2020; 12(4):646.
https://doi.org/10.3390/sym12040646

**Chicago/Turabian Style**

Wang, Hui, Junyong Tang, and Bo Hong.
2020. "Research of Wireless Congestion Control Algorithm Based on EKF" *Symmetry* 12, no. 4: 646.
https://doi.org/10.3390/sym12040646