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

^{1}

^{2}

^{*}

## 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

- Nagate, A; Ogata, D.; Fujii, T. Cell Edge Throughput Improvement by Base Station Cooperative Transmission Control with Reference Signal Interference Canceller in LTE System. In Proceedings of the IEEE 75th Vehicular Technology Conference (VTC Spring), Yokohama, Japan, 6–9 May 2012; pp. 1–5. [Google Scholar]
- Capozzi, F.; Piro, G.; Grieco, L.A.; Boggia, G.; Camarda, P. Downlink packet scheduling in LTE cellular networks: Key design issues and a survey. IEEE Commun. Surv. Tutor.
**2013**, 15, 678–700. [Google Scholar] [CrossRef] - Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN). Overall Description; Stage 2; TS 36.300; 3rd Generation Partnership Project (3GPP): Valbonne, France, 2012. [Google Scholar]
- Evolved Universal Terrestrial Radio Access (E-UTRA). Physical Channels and Modulation; TS 36.211; 3rd Generation Partnership Project (3GPP): Valbonne, France, 2012. [Google Scholar]
- Choi, J.-G.; Bahk, S. Cell-throughput analysis of the proportional fair scheduler in the single-cell environment. IEEE Trans. Veh. Technol.
**2007**, 56, 766–778. [Google Scholar] [CrossRef] - Andrews, M.; Kumaran, K.; Ramanan, K.; Stolyar, A.; Whiting, P.; Vijayakumar, R. Providing quality of service over a shared wireless link. IEEE Commun. Mag.
**2001**, 39, 150–154. [Google Scholar] [CrossRef] - Stolyar, A.L.; Ramanan, K. Largest weighted delay first scheduling: Large deviations and optimality. Ann. Appl. Probab.
**2001**, 11, 1–48. [Google Scholar] [CrossRef] - Basukala, R.; Ramli, H.M.; Sandrasegaran, K. Performance analysis of EXP/PF and M-LWDF in downlink 3GPP LTE system. In Proceedings of the First Asian Himalayas International Conference on Internet (AH-ICI), 3–5 November 2009; 2009; pp. 1–5. [Google Scholar]
- Singh, D. Performance Analysis of QOS-aware Resource Scheduling Strategies in LTE Femtocell Networks. Int. J. Eng. Trends Technol.
**2013**, 1, 2994–2999. [Google Scholar] - Boudreau, G.; Panicker, J.; Guo, N.; Chan, R.; Wang, N.; Vrzic, S. Interference coordination and cancellation for 4G networks. IEEE Commun. Mag.
**2009**, 47, 74–81. [Google Scholar] [CrossRef] - Himayat, N.; Talwar, S.; Rao, A.; Soni, R. Interference management for 4G cellular standards [WIMAX/LTE UPDATE]. IEEE Commun. Mag.
**2010**, 48, 86–92. [Google Scholar] [CrossRef] - Lee, D.; Li, G.Y.; Tang, S. Intercell interference coordination for LTE systems. IEEE Trans. Veh. Technol.
**2013**, 62, 4408–4420. [Google Scholar] - Zhang, X.; He, C.; Jiang, L.; Xu, J. Inter-cell interference coordination based on softer frequency reuse in OFDMA cellular systems. In Proceedings of the International Conference on Neural Networks and Signal Processing, Nanjing, China, 7–11 June 2008; pp. 270–275. [Google Scholar]
- Qian, M.; Hardjawana, W.; Li, Y.; Vucetic, B.; Shi, J.; Yang, X. Inter-cell interference coordination through adaptive soft frequency reuse in LTE networks. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, 1–4 April 2012; pp. 1618–1623. [Google Scholar]
- Lee, P.; Lee, T.; Jeong, J.; Shin, J. Interference management in LTE femtocell systems using fractional frequency reuse. In Proceedings of the 12th International Conference on Advanced Communication Technology (ICACT), Gangwon-Do, Korea, 7–10 February 2010; pp. 1047–1051. [Google Scholar]
- Kim, T.-H.; Lee, T.-J. Throughput enhancement of macro and femto networks by frequency reuse and pilot sensing. In Proceedings of the IEEE International Performance, Computing and Communications Conference (IPCCC), Austin, TX, USA, 7–9 December 2008; pp. 390–394. [Google Scholar]
- Risi, C.; Wassie, D.A. Inter-cell interference modeling in LTE systems. Wirel. Pers. Commun.
**2013**, 72, 389–404. [Google Scholar] [CrossRef] - Rahman, M.; Yanikomeroglu, H. Enhancing cell-edge performance: A downlink dynamic interference avoidance scheme with inter-cell coordination. IEEE Trans. Wirel. Commun.
**2010**, 9, 1414–1425. [Google Scholar] [CrossRef] - Chaudhuri, S.; Pradeep, K.; Das, D. Maximizing spectral efficiency for cell edge users in LTE small cell network. In Proceedings of the IEEE International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, India, 3–5 April 2014; pp. 1312–1317. [Google Scholar]
- Li, Y.P.; Hu, B.J.; Zhu, H.; Wei, Z.H.; Gao, W. A delay priority scheduling algorithm for downlink real-time traffic in LTE networks. In Proceedings of the IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, China, 20–22 May 2016; pp. 706–709. [Google Scholar]
- Park, W.-H.; Cho, S.; Bahk, S. Scheduler design for multiple traffic classes in OFDMA networks. Comput. Commun.
**2008**, 31, 174–184. [Google Scholar] [CrossRef] - Alfayly, A.; Mkwawa, I.; Sun, L.; Ifeachor, E. QoE-based performance evaluation of scheduling algorithms over LTE. IEEE Globecom Workshops (GC Wkshps), Anaheim, CA, USA, 3–7 December 2012; pp. 1362–1366. [Google Scholar]
- General Packet Radio Service (GPRS). Enhancements for Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Access; TS 23.401 V10.6.0; 3rd Generation Partnership Project (3GPP): Valbonne, France, 2011. [Google Scholar]
- Abbena, E.; Salamon, S.; Gray, A. Modern Differential Geometry of Curves and Surfaces with Mathematica; CRC Press: Boca Raton, FL, USA, 2006. [Google Scholar]
- Chand, P.; Mahapatra, R.; Prakash, R. Energy efficient radio resource management for heterogeneous wireless network using CoMP. Wirel. Netw.
**2016**, 22, 1093–1106. [Google Scholar] [CrossRef] - Lee, K.K.; Chanson, S.T. Packet loss probability for real-time wireless communications. IEEE Trans. Veh. Technol.
**2002**, 51, 1569–1575. [Google Scholar] [CrossRef] - Institute of Telecommunications, Vienna University of Technology. LTE System Level Simulator, v1.6 r885. Available online: http://www.cs.odu.edu/~rnagella/LTE-simulation/LTEsystemDoc.pdf (accessed on 15 July 2016).
- Hentilä, L.; Kyösti, P.; Käske, M.; Narandzic, M.; Alatossava, M. MATLAB implementation of the WINNER Phase II. Channel Model ver1.1. Available online: http://projects.celtic-initiative.org/winner+/phase_2_model.html (accessed on 15 July 2016).
- Lte Physical Layer Framework for Performance Verification; TSG-RAN1 R1–070674; 3rd Generation Partnership Project (3GPP): Valbonne, France, 2007.
- Evolved Universal Terrestrial Radio Access (E-UTRA). Physical Layer Procedures; TS 36.213 V11.0.0; 3rd Generation Partnership Project (3GPP): Valbonne, France, September 2012. [Google Scholar]
- Jain, R.; Chiu, D.-M.; Hawe, W.R. A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer System; Eastern Research Laboratory, Digital Equipment Corporation: Hudson, MA, USA, 1984; Volume 38. [Google Scholar]

**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 |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**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