# Enhanced Quality of Service of Cell-Edge User by Extending Modified Largest Weighted Delay First Algorithm in LTE Networks

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. System Model

_{HOL,i}denotes HOL packet delay, which is the waiting time between the packet arrival time and the time it is transmitted successfully. δ

_{i}represents the probability of packet loss and τ

_{i}is the value of delay threshold. The delay threshold τ

_{i}for user i is based on user applications. Table 1 shows the threshold values with priority for different types of data. Here, voice has the highest priority and (Transmission Control Protocol) TCP-based service has the lowest priority. Therefore, the data packet of TCP-based service would be discarded first in a congested queue.

_{i}(t) /${\overline{r}}_{i}(t-1)$ for better system performance, which can be defined as weighted transmission rate ψ

_{i,j}in Equation (2). Here, r

_{i}(t) is the instantaneous transmission rate of user i at transmission time interval (TTI) t and ${\overline{r}}_{i}(t-1)$ is the previous average transmission rate. Time t is removed from the rest of the paper to reduce the complexity. For convenience, Table 2 shows the symbols used in this paper.

_{i,j}

^{EMLWDF}denotes the metric value of the EMLWDF algorithm, γ

_{i,j}is the SINR of user i RB j and R

_{i}is the distance between eNB and user i. The higher the metric m

_{i,j}

^{EMLWDF}value for a particular user in Equation (4), the higher the chance for that user to get the required RBs. Based on Equations (4) and (5), it can be seen that the variable W

_{i}will vary according to the location of the user i. If the distance of the user i from eNB is more than the radius of the inner region Ω, the proposed EMLWDF algorithm will consider the distance R

_{i}and SINR γ

_{i,j}of user i RB j to calculate the metric m

_{i,j}

^{EMLWDF}. Otherwise, the user will be considered as a cell-centered user and the EMLWDF algorithm will not consider the distance and SINR of that particular user. After exceeding the inner region value, the higher the distance from eNB and the less SINR of the user, the higher the metric value and thus higher the possibility to get RBs.

_{i,j}of user i on RB j, where G

_{i,j}and P

_{i,j}are the channel gain and transmit power of serving eNB, respectively [25]. σ

^{2}is the white noise power spectral density and I is the inter-cell interference. The users are affected by various noise and interference in the real-life wireless environment. Large scale errors occur when the users are receiving the data packets. Therefore, this paper implements the two-state Markov Model to measure the probability of packet loss δ

_{i}[26]. Success state and failure state are the two states in this model. The success state has low error probability and that helps to transmit packet successfully. On the other hand, the failure state has higher error probability. Thus, packets cannot be transmitted, which makes this state unusable. If the packet transmission fails, the packet is retransmitted until it exceeds the delay threshold value. After exceeding the threshold value, the packet will be discarded and the next packet transmission will begin. The packet-dropping operation has a significant impact on the probability of packet loss. This paper considers that the success state is the usable system scenario as packets can be transmitted successfully in this state. The delay threshold and error probability are considered in the simplified equation of success state, which is described in the following Equation (8):

_{i}is the error probability and S

_{i}is the probability from failure state to success state. The ε

_{i}is assumed as a small value and ε

_{i}should be smaller than S

_{i}in this state to make the system usable. According to [26], δ

_{i}can be measured precisely by assuming a constant value of ε

_{i}and S

_{i}along with changing the delay threshold value τ

_{i}. Therefore, this paper considers this for success state ε

_{i}= 0.01, S

_{i}= 0.1 and τ

_{i}based on the different user applications. The ε

_{i}is comparatively larger than S

_{i}in the failure state due to the high error probability and δ

_{i}can be defined as δ

_{i}≈ 1 ≈ ε

_{i}, which can make the system unusable. Considering the error probability and the probability from failure state to success state in Equation (8) and after doing simplification, the Equation (4) can be written as follows:

## 4. Results and Discussion

## 5. Limitation and Future Works

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Table 1.**Delay threshold value for different types of data [23].

Types of Data | Delay Threshold (ms) | Priority |
---|---|---|

Gaming | 50 | 3 |

Conversational Voice | 100 | 2 |

Live Video streaming | 100 | 7 |

TCP-based (HTTP, FTP) | 300 | 8 |

Symbol | Definition |
---|---|

i | user |

j | RB |

t | time (TTI index) |

r_{i} | instantaneous transmission rate of user i |

D_{HOL,i} | HOL Delay of user i, RB j |

δ_{i} | probability of packet loss of user i |

τ_{i} | delay threshold of user i |

Ω | radius of the inner region |

R_{i} | distance from the eNB to user i |

S_{i} | probability from failure rate to success rate of user i |

ε_{i} | the error probability of user i |

γ_{i,j} | received SINR of user i, RB j |

G_{i,j} | channel gain from serving eNB of user i, RB j |

P_{i,j} | transmit power of serving eNB for user i, RB j |

I | inter-cell interference |

σ | white noise power spectral density |

Parameters | Values |
---|---|

System bandwidth | 20 MHz |

Operating frequency | 900 MHz |

Scenario | Random deployment (Urban) |

Number of users | 10, 20, 30, 40, 50, 60 |

User speed | 5 kmph |

eNB power transmission | 46 dBm |

MSC index | 29 available MSCs as in 3GPP [30] |

Traffic model | Video, VoIP, HTTP |

Scheduler | PF, MLWDF, EMLWDF |

Simulation time | 1000 TTI |

Traffic Model | Algorithm | Cell-Edge User Throughput (%) | Average User Throughput (%) |
---|---|---|---|

Video | PF | 88.59 | 27.23 |

MLWDF | 46.04 | 11.57 | |

VoIP | PF | 72.17 | 31.80 |

MLWDF | 39.33 | 4.86 | |

HTTP | PF | 77.30 | 44.71 |

MLWDF | 13.93 | 19.34 |

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**MDPI and ACS Style**

Chayon, H.R.; Dimyati, K.B.; Ramiah, H.; Reza, A.W. Enhanced Quality of Service of Cell-Edge User by Extending Modified Largest Weighted Delay First Algorithm in LTE Networks. *Symmetry* **2017**, *9*, 81.
https://doi.org/10.3390/sym9060081

**AMA Style**

Chayon HR, Dimyati KB, Ramiah H, Reza AW. Enhanced Quality of Service of Cell-Edge User by Extending Modified Largest Weighted Delay First Algorithm in LTE Networks. *Symmetry*. 2017; 9(6):81.
https://doi.org/10.3390/sym9060081

**Chicago/Turabian Style**

Chayon, Hasibur Rashid, Kaharudin Bin Dimyati, Harikrishnan Ramiah, and Ahmed Wasif Reza. 2017. "Enhanced Quality of Service of Cell-Edge User by Extending Modified Largest Weighted Delay First Algorithm in LTE Networks" *Symmetry* 9, no. 6: 81.
https://doi.org/10.3390/sym9060081