**5. Conclusion**

154 Mobile Networks

traffic. In this case, it gives less jitter and increase throughput. TELKOM usually adds 20% on top of bandwidth requirement to engineer xDSl line speed, hence 1 Mbps line speed is used. It can be shown from Table 6 that the performance metrics of 1 Mbps line is closed to 20 Mbps.

20 Mbps 83.58 23.53 0.53 2.45 13.30 19.53 1 Mbps 78.93 22.93 0.34 2.47 12.23 20.75 800 Kbps 84.70 20.26 0.37 2.12 13.45 24.96

20 Mbps 49.33 25.38 0.14 0.47 7.38 3,3 1 Mbps 49.33 25.38 0.14 0.47 7.38 3,3 800 Kbps 54.81 27.21 0.16 1.25 6.68 9.9

We use video conference as reference in order to see the effect of background traffic from PC to the femto service performance, in this case video conferencing. It can be seen from Table 7, as

20 Mbps 81.83 24.97 1.25 2.47 13.92 18.05 2 Mbps 84.25 23.73 0.29 2.99 13.62 35.22 1,5 Mbps 85.28 26.48 0.38 4.43 15.70 29.95 1,2 Mbps 82.26 23.19 0.33 5.94 14.64 41.11 1 Mbps 89.07 27.85 0.12 3.96 13.87 45.52

20 Mbps 56.67 58.92 0.82 0.66 7.02 3.95 2 Mbps 52.00 25.33 0.18 0.95 7.35 14.08 1,5 Mbps 27.43 25.02 0.07 1.22 8.45 16.88 1,2 Mbps 47.08 26.92 0.13 1.67 7.00 16.85 1 Mbps 28.57 25.45 0.05 1.32 10.20 17.57 Table 7. H264 Video Conference Performance in the presence of background traffic from PC

soon the background traffic exist, the packet loss of video quality increase from 2.47% to 3.98% and the jitter double from 20.75 ms to 45.52 ms. In order to improve the video performance we increase the xDSL bandwidth profile from 1 Mbps to 1.2 Mbps, 1.5 Mbps and 2 Mbps. When it set to 2 Mbps, the packet loss is below 3%, and the jitter is also below

Table 6. H264 Video Conference Performance without PC background

**Average Video Quality for 5 minutes observation Bitrate (kbps) Packet loss (%) Jitter (ms) Tx Rx Tx Rx Tx Rx** 

**Average Audio Quality for 5 minutes observation Bitrate (kbps) Packet loss (%) Jitter (ms) Transmit Receive Transmit Receive Transmit Receive** 

> **Average Video Quality Bitrate (kbps) Packet loss (%) Jitter (ms) Tx Rx Tx Rx Tx Rx**

**Average Audio Quality Bitrate (kbps) Packet loss (%) Jitter (ms) Transmit Receive Transmit Receive Transmit Receive** 

**Mode** 

**Mode** 

**Mode** 

**Mode** 

In this chapter the xDSL characteristics has been explained including xDSL attainable rate and transmission delay. Based on TELKOM study result, the performance of ADSL2, ADSL2+ and VDSL2 under FTTE, FTTC and FTTB configuration have been discussed. The backhaul quality model for xDSL has been proposed in order to accommodate both technical limitation of xDSL (in example cable length and constaint in xDSL uplink bandwidth) and current penetration rate of bandwidth subscription profiles both in Indonesia and Europe. DSL backhaul quality model is derived in order to address different qualities of backhaul. The model has been used in FREEDOM project in elaboration of cooperative RRM, scheduling and system level simulation which need to take into account the backhaul quality.

According to the performance measurement result, it can be concluded that the femtocell performance can be affected by internet traffic in the xDSL modem. This case mostly happens when the users instantly plug in the femtocell to their broadband connection without knowing that the traffic from a FAP and PC could compete to each other without any priority or separation. This is also true if the xDSL service provider is a separate company, and there is no service level agreement with the femtocell service provider in order to maintain end-to-end QoS.

In order to achieve better performance for femtocell, the customer should have additional bandwidth to accommodate both traffic. Alternatively an integrated modem-femtocell

**0**

**8**

*China*

**Sum-of-Sinusoids-Based Fading Channel Models**

In this chapter, we propose an extended reference model and two novel sum-of-sinusoids (SoS) models (statistical and deterministic simulation models) propagation models considering the Rician K-factor and vehicle speed ratio in Vehicular Ad Hoc Networks (VANETs). Our models consider comprehensive scene of wave propagation, including I2V (infrastructure-to-vehicle) channels with a LOS or NLOS environment, IVC (inter-vehicle communication) channels with a LOS or NLOS environment. The analysis of the statistical properties of the proposed models show that the statistics of the new models match those of the reference model at a large range of normalized time delays. The proposed models show improved approximations to the desired auto-correlation and faster convergence with

VANETs have been envisioned for safety, traffic management, and commercial applications, such as information notices of hazardous road conditions, traffic congestion, or sudden stops, furthermore, commercial services (e.g., data exchange, infotainment, rear-seat multiplayer games) (Boban et al., 2009)-(Abbas et al., 2010). Recently, the Quality of Service (QoS) and network security are used to determine the feasibility of such applications. To achieve the desirable QoS and network security, many techniques focusing on the design and optimization of routing protocol in VANETs have been proposed in (Lwinmuller et al., 2006)-(Saleet et al., 2010). However, the fundamental issue centers on the accurate description of the characteristics and mechanism of wave propagation in VANETs, so it is essential to establish a reasonable radio propagation model for VANET channels, which help to understand the characteristics of the channel and take some effective steps to improve the

Direct communication between vehicles in VANETs may be supported by the deployment of Mobile Ad Hoc Networks (MANETs), which does not rely on fixed infrastructure and can accommodate a constantly evolving network topology (Boban et al., 2009; Vahid, 2009). More recently, infrastructure-to-vehicle (I2V) and inter-vehicle communication (IVC) links are being evaluated for a variety of applications. I2V channels is similar to the traditional cellular systems, the base station is stationary, and only the mobile terminal are in motion. However,

the increase of Rician K-factor and vehicle speed ratio.

**1. Introduction**

**2. Background**

QoS and network security.

**with Rician K-factor and Vehicle Speed Ratio in**

**Vehicular Ad Hoc Networks**

*School of Information Engineering, Nanchang University*

Yuhao Wang and Xing Xing

solution (home-gateway with femtocell capability) can be introduced in order to have jointscheduling between xDSL modem and femtocell.

Implementing backhaul aware scheduling (BAS) in the femtocell can be another alternative in order to minimize the impact of bottleneck in the backhaul to the femtocell service performance [4]. This study was driven by the limitation of backhaul capacity (e.g., bottleneck or congestion) caused by other traffic (e.g., IPTV or Internet access in xDSL modem) which affects the performance of FAP in serving requested traffic from femtocell users. The admission control is incorporated with the scheduling method to treat all kinds of traffic served by the FAP. With BAS, the FAP can decide whether the backhaul capacity is enough or not to support existing session. The simulation results show that with BAS, the performance of FAP can be improved especially for peak backhaul conditions compared to FAP without BAS.
