Integration of LTE and Wi-Fi using Link Aggregation

Kanika Anand, Manish Damani, Manmeet Singh Khurana, Electrical and Computer Engineering Department,

University of Florida, Gainesville

Abstract The modern mobile systems are facing severe

challenges due to the current spectrum scarcity. The current

cellular deployment ranges mainly from 700MHz to 2.6GHz

spectrum. The ever-increasing MBB (mobile broadband) traffic

load has lead to a pressing need for additional spectral resources

for cellular systems. Operators are facing the challenge of

catering to soaring traffic with the increasing number of people

using mobile broadband services. While mobile broadband

system (MBB) in licensed spectrum is highly strained due to

limited capacity, the unlicensed spectrum (Wi-Fi) enjoys high-

speed data. Wi-Fi operates in the unlicensed frequency spectrum

of 2.4GHz and 5GHz and is a potential contender for efficient

and high-speed data traffic. In this paper, we aim to provide a

performance evaluation of integrated LTE and Wi-Fi systems as

a solution to current spectrum scarcity and show some of the

challenges being faced.

KeywordsLTE, WiFi, Integration, Link aggregation, network coexistence, Carrier aggregation, NS3

I. INTRODUCTION

First lets see how the wireless communication standards have evolved from first generation to the fourth. From Fig. 1 we can see that with the evolution of standards from 1G to 4G, new features were added over time.

Fig. 1. Evolution of wireless communication systems

Currently we are using 4G, which is named as Long Term Evolution (LTE). LTE has several advantages as compared to earlier generations of GSM, CDMA, and HSPA. It gives faster data rates, supports flexible bandwidths (1.4, 3, 5, 10, 15, 20 MHz), faster scheduling, Multiple Input Multiple Output (MIMO) antenna systems and self-organizing networks.

In recent times, the use of smartphones, tablets and other handheld wireless devices has caused an exponential increase in wireless capacity demands [4]. According to CISCO there will be about 20 billion devices connected to Internet. Since the licensed spectrum is limited, these ever-increasing Internet subscribers have over burdened it. This has resulted in licensed spectrum to be very limited and is becoming very scarce.

Fig. 2. Available Unlicensed Spectrum [5]

One of the most promising techniques for dealing with the lack of available spectrum is the concept of LTE-WiFi aggregation. There are two types of integration: -

1. Link Aggregation

2. Carrier Aggregation

Fig. 3. LTE-WiFi aggregation techniques

As shown in Fig.3 we can integrate the LTE and Wi-Fi in a link aggregation or carrier aggregation. In link aggregation [2], whenever the load increases, the LTE traffic is routed/switched to the WiFi through LTEs Packet Gateway (PGW). It is to be noted here that the LTE is working in its unlicenced spectrum only and its switched to WiFi as and when the need arises.

In carrier aggregation [3], the LTE works on licensed as well as unlicensed (Wi-Fi) spectrum. So instead of switching to Wi-Fi when the traffic surges, LTE itself operates on unlicensed spectrum. So LTE is sharing the Wi-Fi spectrum and hence its called carrier aggregation.

From Fig 4 we can see the architecture of link aggregation [6].

Fig. 4 Link Aggregation Architecture

The User Equipment (UE) is operating in an environment of multiple carriers and Access Points. The air interface of the UE is operated on the LTE but the network has the decision-making capability to off load the data traffic on the nearby wireless AP. The Mobility Control Gateway (MC-GW) of WiFi AP is connected to LTE via Packet Data Network Gateway (P-GW). Here, LTE acts as an anchor and the link from Wi-Fi Access Points (AP) are aggregated at the device. Operators will be able, not only to connect the Wi-Fi hotspots to LTE network, but also manage the WiFi resources. Wi-Fi and LTE APs dont have to be collocated, which means operators can get going pretty quickly without major changes to the existing LTE and Wi-Fi infrastructure.

Access Network Discovery and Selection Function (ANDSF) do the discovery and selection of the WiFi Access Points. It is decision-making entity within the evolved packet core (EPC) whose main function to assist the UEs to discover, select and offload the traffic. Fig explains more of the ANDSFs functions in detail.

Fig. 5. ANDSF Functions

In our project, we have tried to simulate link aggregation using NS3 simulator. The project flow was maintained as per the following algorithm in Fig.5

Fig. 5 Algorithm Flow Chart

II. REFERENCE PAPER DESCRIPTION

A. Abstract : WHEN CELLULAR MEETS WI-FI IN WIRELESS SMALL CELL NETWORKS

The deployment of small cells overlaid on existing

macrocellular systems is seen as a key solution for offloading

traffic, optimizing coverage, and boosting the capacity of

future cellular wireless systems. The next generation of small

cells is envisioned to be multimode which will be capable of

transmitting simultaneously on both licensed and unlicensed

bands. This constitutes a cost-effective integration of WiFi and

cellular radio access technologies to efficiently cope with peak

wireless data traffic and quality of service requirements. The

paper discusses about the means by which small cells self-

organize and automatically route their traffic flows across

different radio access terminals [1].

Fig. 6. An illustration of a macrocell deployment

The paper talks about introducing a fully distributed and

dynamic traffic offloading framework, in which small cells

automatically steer their traffic to WiFi RATs, depending on

the traffic type, quality of service (QoS) requirements, network

load, and interference levels. Small cells are assumed to have a

wired backhaul connection to the core network. This developed

framework, called as cross-system learning, endows small cells

with self-organizing capabilities allowing them to transmit

simultaneously on LTE and WiFi bands.

The paper also introduces a concept of Reinforcement learning

(RL) which is an area of machine learning where a number of

decision makers are able to make autonomous decisions to

optimize a certain cumulative objective function. A key design

criterion in RL is to develop strategies to strike a balance

between exploring the network and exploiting it. In the context

of cellular and WiFi integration, the goal is to devise an

intelligent and online learning mechanism to optimize its

licensed spectrum transmission, and WiFi by offloading delay-

tolerant traffic.

Fig.7 Flow chart of cross-system learning procedure

The cross-system learning framework is composed of the

following interrelated components: -

Subband selection, power level allocation, and cell range expansion bias: Every small cell learns over

time how to select appropriate subbands with their

corresponding transmit power levels in both licensed

and unlicensed spectra, in which delay-tolerant traffic

is steered toward the unlicensed spectrum.

Proactive scheduling: Once the small cell acquires its subband, the scheduling decision is traffic-aware,

taking into account QoS requirements like

throughput, delay tolerance, and latency.

B. Citation

Chosen paper: - When Cellular Meets Wi-Fi in Wireless Small Cell Networks. Authors: - (a) Mehdi Bennis, University of Oulu. (b) Meryem Simsek and Andreas Czylwik, University of Duisburg-Essen. (c) Walid Saad, University of Miami. (d) Stefan Valentin, Bell Labs, Alcatel-Lucent. (e) Merouane Debbah, SUPELEC.

Publication source: Communications Magazine, IEEE (Volume:51 , Issue: 6 ), Page: 44-50, Dates: June 2013, Citations: 12

III. SIMULATION DESCRIPTION

In communication and network behavior study, simulator is a powerful device to model the real network using multiple network entities (client, server, packets), mathematical formulas, scripting and coding. For all the simulator options we had like NS-3, NS-2, Matlab, we chose to work with NS3. Our choice of simulator was influenced by various factors.

NS-3 is a new software development effort focused on improving upon the core architecture, software integration, models, and educational components of NS-2. The project commenced in July 2006 and the first release was made on June 30, 2008.

NS-3 has pre-defined modules [11] for LTE, Wi-Fi, and uses python/C++ scripts to run simulations.

NS3 comes with bundled NetAnim animator, which can be used to show the real time movement of packets over wired/wireless network.

NS3 is highly industry relevant thereby giving us an edge and an add-on in our profile.

We adopted modular approach dividing our project in 5 modules. First 2 modules involved background study, NS-3 installation, and running initial scripts (including trace files generation). Over the course of next 3 modules, we studied and implemented LTE module [12] (with a basic x2 handover), Wi-Fi module (with WiFi LAN handover) and finally the link aggregation between LTE and WiFi.

A. Module 1 and 2

We started with the installation of 3.19 version of NS-3 in Ubuntu environment. Apart from pre-defined modules in NS-3 there also are predefined example files, which helps, in the thorough understanding and the workflow. first.cc [9] is an example file which scripts the packet transfer between two point-to-point nodes. second.cc [10] is a script for connecting nodes to the wired LAN. In order to learn the packet transfer and topology we ran these two files and observed the output as in Fig.8 and Fig.9..

Fig. 8. Output: first.cc

Fig. 9. Output: second.cc

Parts of these two file codes have been included in our final program.

B. Module 3

This module dealt with the study of LTE [8] and the behavior

and challenges of basic x2 handover. The Lena x2 handover

occurs between two eNB nodes. In order to see what actually

goes on while the program is being run, we used the tracing

concept. Using tracing helps keep the track of nodes and

program flow. The following code was added to enable the

tracing feature in NS3.

Fig. 10. Tracing Codes

In order for these codes to work, we have to include a library

named ns3/mobility-module.h. To the LTE handover part, we used lena-x2-handover.cc file, which illustrates a handover between 2 eNB nodes. Using this script we learnt

how to

Define LTE nodes (eNB)

Create a RemoteHost

Create an Internet Stack

Install Mobility model eNBs

Install LTE Devices on eNBs and UEs

Install the IP stack on the UEs

Add X2 interface

Implement X2-based Handover We also learnt the gdb debugger at this stage for debugging our

code. Since debugging involves the deeper workflow and

understanding of the C++, therefore it was quite challenging

for us.

Fig. 11 Output: lena-x2-handover.cc

C. Module 4

In this module we learnt the working of WiFi [7] and how WiFi could be connected to LAN. The script file used here is WiFi_LAN_Scenario.cc. Again tracing feature was used to get in-depth understanding on how the simulation was running. This script helped us understand

Defining WiFi nodes

Setting default channel model

Creating WiFi physical objects

Configuring MAC parameters

Defining bounds for mobile nodes

Routing model

Creating point-to-point connection between client and server

The output of this script can be seen in the Fig 12.

Fig. 12. Output: WiFi_LAN_Scenario

D. Module 5

After working on all four modules and learning the architecture, flow and behavior of the LTE and WiFi, we worked on our main objective i.e. LTE-WiFi Integration. The script made and used for implementing this is LTE_WiFi_Integration.cc. As explained earlier, there can be two types of integration i.e. Link and Carrier Aggregation.

We used Link Aggregation for our project and we used the concept of PGW i.e. Packet Data Network Gateway. This is where the WiFi and LTE are integrated and a seamless off loading is done between the two. The layout of the code is as follows: -

1. We defined four nodes (One WiFi access point node wifiApNode, Two WiFi Station Nodes wifiStaNodes and one LTE enbNode).

2. Used a WiFi helper class to set the default WiFi standard.

3. Created a channel model using YansWifiChannelHelper class.

4. Configured MAC parameters using NqosWifiMacHelper class

5. Mobility was configured wherein one WiFi station node was assigned random mobility model whereas others were assigned stationary mobility model.

6. Bounds of Mobility model were defined. The bound was set to study the handover in a defined area.

7. IPv4 addresses were assigned using InternetStackHelper class.

8. LTE radio access network (RAN) was defined using an object of the ltehelper class. This also triggers the EPC configuration.

9. RemoteHost which will act as a server, was created with properties defined like Internet stack, point to point interface with UE (acting as a client).

10. Uplink port was defined between Client and Server.

11. Maximum packet size was set to 1000000 and Transmission time was set to 500 milliseconds for the client. These are set parameters for the handover to take place.

12. With packet size and transmission time as parameters, the handover will take place from LTE node to WiFi Access Point if condition is met. We can see the handover happening place in the following figure.

Fig.13 Output: LTE_WiFi_Integration.cc

From the output figure, we can see that the packets are moving between the nodes implying that the handover is taking place. But we were unable to see the final destination of the received packets. So we made some changes to the present script so that we could have a look at the transmitted and received packets along with the throughput. But again we did not get any value for received packets and throughput. This is due to the fact that our packets are being lost between the Tx and Rx but still their final destination couldnt be located.

Fig.14. Output 2: LTE_WiFi_Integration.cc

IV. CONCLUSION

A. Opinions

We tried to integrate LTE WiFi using the NS3 simulator. This

is a long and a complex task, which involves numerous

functions and a deeper knowledge of the NS3. Although we

achieved the packet movement between the LTE WiFi nodes

indicating that the handover is occurring. We are in the

process of achieving next step would to retrieve the lost

packets and get the throughput value. We believe that NS3

helped us a lot for achieving our goal and no other simulator

could have been better for this project.

B. Challenges

We faced various hinders during our project. First, we didnt have any background experience with NS3 or C++, so we had

to start from the scratch. Another issue was to become familiar

with gdb debugger, to know where the errors were in our

program and how to deal with them. The issue of defining

bounds for the mobility of the mobile nodes was another

problem, which took almost a weeks of our time.

Finally there were compatibility issues, which we figured out

gradually. The 3.19 version of NS-3 doesnt support [13] 802.11n standard and so we had to install 3.20 version to deal

with it.

C. Division of Labor

We were successfully able to divide the project in three equal

parts. The first two modules dealt with the background study

of the project and the simulator installation. It was done

together as a team. Though we worked and helped each other

in a collaborative effort, still each one of is responsible

individually for the following tasks as defined.

Team

member

Responsibilities

Kanika

Anand

LTE Module Study, Implementation of LTE

module in NS3 and Final Simulation.

Manmeet

Singh

Khurana

Wi-Fi Module Study, Implementation of Wi-

Fi module in NS3 and Final Simulation.

Manish

Damani

Integration of LTE & Wi-Fi Module and

Final Simulation.

ACKNOWLEDGMENT

This project is a partial requirement towards completion of the

course, EEL6591 Wireless Networks. We thank Dr. Janise

Mcnair and Mr. Gokul Bhat for guiding us with every little

details about the work and providing valuable inputs

throughout the time of working on this project.

REFERENCES

[1] B. Mehdi, S. Meryem, C. Andreas, S. Walid, V. Stefan, D. Merouane,

When Cellular Meets Wi-Fi in Wireless Small Cell Networks, IEEE, Communications Magazine, vol. 51, Issue 6, pp. 44-50, June 2013

[2] K. Yuji, S. Junichi, K Takeshi, O. Masato, T. Ryuichi, LTE-WiFi Link Aggregation at Femtocell Base Station, IEEE, World Telecommunications Congress, pp. 1-6, June 2014

[3] N, Rupasinghe, I. Guvenc, Licensed-assisted access for WiFi-LTE coexistence in the unlicensed spectrum, IEEE, Globecom Workshop, pp. 894-899, 2014

[4] https://www.qualcomm.com/media/documents/files/lte-unlicensed-coexistence-whitepaper.pdf

[5] http://www.huawei.com/ilink/en/download/hw_327803

[6] http://www.slideshare.net/zahidtg/45g-integration-of-lte-and-wifi-networks

[7] https://www.nsnam.org/docs/models/html/wifi.html

[8] https://www.nsnam.org/docs/models/html/lte.html

[9] https://www.nsnam.org/doxygen/first_8cc.html

[10] https://www.nsnam.org/docs/release/3.18/doxygen/second_8cc.html

[11] https://www.nsnam.org/docs/manual/html/

[12] https://www.nsnam.org/docs/tutorial/html/

[13] https://groups.google.com/forum/#!forum/ns-3-users