Cross-System Traffic Steering between LTE and WiFi Networks: A Case Study

Ranplan Wireless Network Design Ltd.

Upper Pendrill Court, Papworth Everard,

Cambridge, CB23 3UY, UK

Email: info@ranplan.co.uk Web: www.ranplan.co.uk

Abstract The deployment of WiFi access point (AP) overlaid on cellular systems is seen as a key solution for offloading traffic to boost the capacity of future wireless system. Before multi-mode LTE and WiFi networks are deployed, the performance boost that arises from steering traffic between LTE and WiFi networks needs to be evaluated. In this whitepaper, we showcase how a simple set of criteria can help improve the performance of LTE and WiFi networks.

1 Introduction

Mobile data traffic is experiencing an exponential growth, and this trend will continue in the coming years [1]. It is predicted that mobile traffic will increase up to 1000 times in the next decade. Because of this, the current mobile networks are facing great capacity challenges.

An efficient, cost-effective integration of cellular (e.g. 3G/LTE) and WiFi technologies, referred to as inter-RAT, has recently attracted significant interest from academia, industry, and standardization bodies alike [2][3]. It is envisioned that WiFi and cellular deployment will exhibit complementary benefits that can be leveraged for an efficient integration. On the one hand, due to the uncontrolled, unlicensed nature of WiFi, the competition for resources among a large number of hotspot users, notably when other device ( laptops, tablets and dongles) transmit on the same unlicensed band, can yield dramatic poor throughput. In such scenarios, offloading some of this traffic to the well-managed cellular network, operating over the licensed spectrum, can improve the performance. On the other hand, the inherent constraints of cellular networks, particularly due to cross-tier and co-tier interference, motivate offloading some of the traffic to the WiFi networks, so as to alleviate the interference and ease congestion. With the deployment of multi-mode system operating on both the WiFi and licensed bands, smart traffic offloading strategies that harness the benefits of both worlds must be developed.

But how to evaluate the cross-system performance and find an optimum hand-over configuration between different systems is a challenge. Ranplan provides an all-in-one indoor & outdoor wireless network planning tool ˗ iBuildNet®, which can perform simulations between cellular and WiFi systems as a precursor to meet this feature request. As a result, Ranplan’s planning tool accurately simulates wireless network performance for both coverage and capacity as well as enabling optimal cross network designs.

In this document, cross-system traffic steering capacity simulation in iBuildNet® is introduced and some case studies are supported to flexibly plan the wireless network so as to meet the high capacity requirement.

2 Cross-System Traffic Steering Criteria

iBuildNet® supports cross-system traffic steering. At its most basic level this allows a UE to remain connected to the LTE network while off-loading data traffic to a WiFi network if the following criteria are met.

a. If the LTE system load is lower than , which is a threshold defined by the operator, then the UEs will be assigned to either LTE or WiFi networks randomly according a predefined ratio of associated UEs. This is classified as the low traffic scenario.

b. If the LTE system load equals to or greater than , then the UEs should be off-loaded to WiFi if the following handover rules are qualified by the WiFi node. This is classified as the high traffic scenario.

Rule 1: the UE comes in range of a WiFi access point with an RSSI >= -70dBm or an SINR of 5dB

Rule 2: the WiFi system load is less or equal to 50%.

Rule 3: the service can be well supported by WiFi.

c. If the traffic off-loading is unqualified then the UE will remain fully connected to the LTE system.

3 Cross-System Traffic Steering: A Case Study

The performance of the developed cross-system traffic steering framework is validated in an LTE/WiFi scenario in iBuildNet®, where an indoor multi-mode small cell and an outdoor-indoor HetNet deployment scenario are simulated. For the sake of comparison, the following benchmark scenarios are considered.

Indoor Multi-mode Small Cell Scenario

Cellular-only: The small cell LTE network is the only serving network of all UEs in the building

Cellular + WiFi (load-Based): Access is performed based on the load, and a portion of UEs are served by WiFi APs

Cellular + WiFi (Coverage-Based): Same as Cellular + WiFi (Load-based) except that the access method is based on the reference signal received power (RSRP) criterion

Outdoor-indoor HetNet Scenario

Cellular-only: one 3-sector macrocell base station, which serve both outdoor and indoor area

HetNet (Load-Based): scenario with macro LTE system and WiFi small cell system, where macro cells serve the outdoor area, and WiFi APs serve the indoor area for traffic offloading

The cross-system configuration is shown in Figure 1, and the simulation evaluation parameters are listed in Table 1 and 2.

Table 1: Cross-system simulation parameters

Parameter Assumption

Wireless system FDD-LTE system with co-channel deployment, and WiFi system with 1,6,11 channels deployment

Carrier frequency 2.6GHz for LTE, 2.4GHz for WiFi

Band width 20MHz

Cell Tx power Small cell: 17dBm, Macro cell: 43dBm

Channel model Ranplan Radio Propagation Simulator (RRPS)

No. of UEs 200 in indoor scenario, and 1000 for outdoor area and 500 for indoor area in outdoor-indoor scenario

Traffic model Table 2

Scheduler algorithm Proportional Fair

MIMO mode CLSM 2x2

Antenna height 2.4m for indoor cells, and 45m for outdoor cells

Antenna coverage radius 20m for indoor cells, and 2000m for outdoor cells

Table 2 UE mix traffic

Traffic Model Percentage of UEs of Indoor Scenario

VoIP 20%

Web Browsing 50%

HD Video Streaming 20%

FTP 10%

Figure 1 Cross-system simulation configuration in iBuildNet

3.1 Indoor multi-mode deployment scenario

A typical indoor office scenario is used to analyse the network deployment, as shown in Figure 2.

Figure 2 Indoor scenario

Figure 3 shows the UEs’ connection relationship. The report can clearly and visually identify the UEs connected to the WiFi system and those UEs remain connected to the LTE system.

Figure 3 Cross-system traffic steering report

Figure 4 gives the PDSCH SINR statistics and the CDF of three deployment scenarios. We can see that WiFi offloading boosts the overall performance when the multi-mode transmission is deployed, particularly for the load-based scenario as compared to the coverage-based scenario.

Figure 5 gives the cell capacity statistics of three deployment scenarios. As shown in Table 3, due to WiFi traffic offloading, the multi-mode transmission can improve the system performance by 21.5% and 17.7% for load and coverage-based scenarios, respectively.

Multi-mode Small Cell

(a) 3D office building

(b) 2D floor plan

Ground floor First floor

(a) Ground floor (b) First floor

data hand over region

UE served by WiFi system

UE served by LTE system

Figure 4 PDSCH SINR statistics

Figure 5 Cell capacity statistics (Kbps)

Table 3 System simulation result statistics

Scenario System Downlink Capacity (Mbps)

User Downlink Average Throughput (Mbps)

Cellular only 1506.6 7.533

LTE + WiFi (Load based)

1830.6 (21.5%) (WiFi offloading: 365.7)

9.153

LTE + WiFi (Coverage based)

1773.9 (17.7%) (WiFi offloading: 301.2)

8.869

8.1%

2.3%3.2%

11.3%

14.3%

55.4%

5.3%

0.01%5.2%

1.1%1.7%

8.9%12.7%

66.5%

3.7%

0.02%

6.1%2.4%

2.9%

8.4%

11.5%

64.0%

4.7%

0.04%

(a) Cellular only (b) Load based

(c) Coverage based

(a) Cellular only (b) Load based

(c) Coverage based

3.2 Outdoor macro and indoor HetNet deployment scenario

A typical outdoor/indoor dense urban scenario is used to analyse the network capacity. Twelve macro sites with 3 sectors are deployed in a 2.4*2.4 km2 area, and a WiFi system with 18 cells is deployed in an enterprise building with 18 storeys. Each cell targets to cover 1 storey, as shown in Figure 6.

(a) 3D building of an urban scenario

(b) 2D floor plan

Figure 6 outdoor-indoor scenario

Figure 7 shows the UE PDSCH SINR statistics and CDF of Macro Only and HetNet scenarios. We can see that as the indoor area can be covered by the small cells, the indoor SINR can be greatly improved compared with the macro only scenario, which results in an improvement of the system performance.

Macrocell

Macrocell

In-building

Capacity analysis area

Site 2

Site 3

12

3

321

321

Site 1

Capacity simulation area

Figure 7 PDSCH SINR statistic results

Figure 8 shows the cell throughout statistics of the capacity analysis area, i.e. macro cell 10, 18, 19, and all WiFi APs. We can see that the indoor WiFi system can offload network traffic by up to 39.5%, but only slightly affects the performance of macro cells, as shown in Table 4.

Comparing outdoor-indoor HetNet scenario with indoor multi-mode scenario, due to the indoor weak signal coverage from outdoor macro cells, WiFi small cells can greatly improve the indoor UE performance, and then offload more traffic.

Table 4 Indoor system result statistic

Scenario Indoor System Downlink Capacity (Mbps)

Macro only 303.4

LTE + WiFi (Load based)

543.6 (55.8%) (WiFi offloading: 214.8/39.5%)

20.5%

4.1%

10.3%

14.0%

10.0%

15.3%

25.0%

0.6%

13.7%

3.7%

8.6%9.4%

7.4%

40.1%

16.1%

0.9%

(a) Macro only

(b) Load based

Figure 8 cell throughput statistic (Kbps)

4 Conclusions

This whitepaper introduces the cross-system performance evaluation via iBuildNet® simulation tool when performing the wireless network design and analysis. From the simulation and analysis results, the coverage and capacity performance can be evaluated to assess if the wireless network can meet the design requirements.

In the future investigation, we will extend the current cross-system criterion into a dynamic traffic offloading framework, in which multi-mode small cells or HetNet seamlessly steer their traffic between cellular system and WiFi systems, depending on the traffic type, network load, and interference levels.

(a) Macro only

(b) Load based

REFERENCE [1] “Cisco visual networking index: Global mobile data traffic forecast update, 2013-2018,” white paper, online available: http://tinyurl.com/b9berc. [2] Qualcomm, “A comparison of LTE advanced HetNets and WiFi,” Whitepaper, [Online]. Available: https://www.qualcomm.com/media/documents/files/a-comparison-of-lte-advanced-hetnets-and-wi-fi.pdf [2] Mehdi Bennis, Meryem Simsek, Walid Saad, Stefan Valentin, Merouane Debbah, and Andreas Czylwik, “When cellular meets WiFi in wireless small cell networks,” [Online]. Available: http://arxiv.org/pdf/1303.5698 [3] NGMN, “Next generation mobile networks radio access performance evaluation methodology,” Jan. 2008 [4] 3GPP TR 25.996, “Spatial channel model for Multiple Input Multiple Output (MIMO) simulations,” v11.0.0, 2012.09

About Ranplan Ranplan produces iBuildNet® which is the most advanced and powerful wireless network planning and optimization tool on the market today for indoor, indoor-to-outdoor, and outdoor-to-indoor small cell/HetNet RF design, network simulation, optimization and deployment. iBuildNet® revolutionizes the design of 2G/3G/4G(LTE), WiFi, and IoT networks for single buildings such as offices and shopping centres, as well as combined indoor-outdoor facilities including campuses, CBDs, urban districts, stadiums, airports and stations. With powerful 3D modelling, advanced algorithms and highly-automated processes, iBuildNet® delivers optimal network design by optimising coverage & capacity whilst significantly reducing network planning time and minimising equipment & installation costs. For more information please visit Ranplan website at www.ranplan.co.uk.