BASE STATION APPLICATION OPTIMIZER

Ronit Nossenson

RNWC, Katsanelson Street, Kfar-Sava, Israel

Keywords: LTE, Access network architecture, Backhaul bottleneck.

Abstract: Expectation and requirements for future wireless communication systems continue to grow and evolve.

Long-Term Evolution (LTE) is a recent effort taken by cellular providers and equipment vendors to step

into wireless broadband market. The key enhancements target an introduction of new all-IP architecture,

enhanced link layer and radio access. In LTE, one of the recurring problems is the bottlenecked backhaul

links, connecting the cell sites with the core network. The basic idea behind the Base Station Application

Optimizer is to replace the traditional base station with a smart entity, capable of analyzing and optimizing

the user data in the application level. In particular, such unit can prevent unnecessary data from travelling

though the bottlenecked backhaul network. The benefits of such entity are reduced latency, jitter and

network deployment costs.

1 INTRODUCTION

Cellular operators are competing traditional

broadband operators by offering mobile broadband

access and IP services such as rich multimedia (e.g.,

video-on-demand, music download, video sharing)

to laptops, PDAs, smart-phones and other advanced

handsets. They offer these services through access

networks such as High-Speed Packet Access

(HSPA), Evolution-Data Optimized (EV-DO) and,

in the near future, Long-Term Evolution (LTE).

These access networks promise to deliver

performance comparable to today’s ADSL services,

but with the added benefit of mobility and

ubiquitous coverage. The new technologies offer

mobile operators significantly improved data speeds,

short latency and increased capacity.

Traditionally, most of the backhaul lines,

connecting the cell sites with the core network, use

TDM (E1, T1) lines, each providing up to 2 Mbps

capacity. Though acceptable for voice and low data

rate applications, E1 capacity is inadequate for

higher data rates. Obviously, the direct result of the

backhaul bottleneck is low utilization of the radio

channels and an unsatisfying user experience.

Enormous backhaul upgrade is required to new

technologies such as Microwave, Metro Ethernet,

cable, or xDSL to satisfy the high bandwidth

demand. This upgrade is expected to be extremely

expensive and its cost casts a real doubt on the

profitability of enhanced network deployment. As a

result, the operators are seeking data reduction

solutions integrated with their network upgrades.

The biggest cost challenge facing wireless service

providers today is the backhaul network (Donegan,

2006).

The basic idea behind the suggested Base Station

Application Optimizer (BS-OPT, for short) is to

replace the traditional Base Station entity with a fast

and smart entity, capable of analyzing and

optimizing the user data in the application level. In

particular, such unit can use its location in the

operator network to prevent unnecessary data from

travelling though the backhaul and core networks.

Note that the suggested optimization is discussed

here in the context of the base stations of LTE

networks ("eNode-B") but it can be performed on

any base station or access point (e.g., IEEE 802.16,

IEEE 802.11).

2 LTE ARCHITECTURE

LTE is the next major step in mobile radio

communications, and is introduced in 3rd

Generation Partnership Project (3GPP) Release 8.

LTE uses Orthogonal Frequency Division

Multiplexing (OFDM) as its radio access

technology, together with advanced antenna

technologies.

119

Nossenson R. (2010).

BASE STATION APPLICATION OPTIMIZER.

In Proceedings of the International Conference on Data Communication Networking and Optical Communication Systems, pages 119-124

DOI: 10.5220/0003022601190124

 SciTePress

Figure 1: LTE/SAE High-Level Network Architecture.

When the evolution of the radio interface started, it

soon became clear that the system architecture

would also need to be evolved. Therefore, in

addition to LTE, 3GPP is also defining IP-based, flat

network architecture: System Architecture Evolution

(SAE) as presented in Figure 1. The LTE–SAE

architecture and concepts have been designed for

efficient support of mass-market usage of any IP-

based service. The architecture is based on an

evolution of the existing GSM/WCDMA core

network, with simplified operations. In the User

Plane (UP), for instance, there are only two types of

nodes (Base Stations and Gateways); while in

current hierarchical networks there are four types

(Node B, RNC, SGSN, GGSN). The gateway

consists of two logical UP entities, Serving Gateway

(S-GW) and Packet Data Network Gateway (PDN-

GW). Flat architecture with less involved nodes

reduces latencies and improves performance.

Another simplification is the separation of the

Control Plane (CP), with a separate Mobility-

Management Element (MME). A key difference

from current networks is that it is defined to support

packet-switched traffic only.

The only node in the Evolved Universal

Terrestrial Radio Access (eUTRAN) is the eUTRAN

Node-B (eNode-B, eNB in Figure 1). It is a radio

base station that is in control of all radio related

functions in the fixed part of the system. Typically,

the eNode-Bs are distributed throughout the

networks' coverage area, each residing near the

actual radio antennas. The interface between the

eNode-B and the gateways is the S1-U; the interface

between the eNode-B and the MME is the S1-C. The

interface between peers eNode-Bs is the X2. The

backhaul links are implementation of these three

interfaces and any required aggregation.

A noteworthy fact is that most of the typical

protocols implemented in today's Radio Network

Controller (RNC) are moved to the eNode-B. The

eNode-B is also responsible for header compression,

ciphering and reliable delivery of packets. On the

control plane, functions such as admission control

and radio resource management are also

incorporated into the eNodeB. Benefits of the RNC

and Node-B merger include reduced latency with

fewer hops in the media path, and distribution of the

RNC processing load.

The Policy and Charging Resource Function

(PCRF) is the network element that is responsible

for Policy and Charging Control (PCC). It makes

decisions on how to handle the services in terms of

QoS, and provides information to the PDN-GW, and

if applicable also to the S-GW, so that appropriate

bearers and policing can be set up.

The Home Subscription Server (HSS) is the

subscription data repository for all permanent user

data. It also records the location of the user in the

level of visited network control node, such as MME.

The IP Multimedia Sub-system (IMS) is service

machinery that the operator may use to provide

services using the Session Initiation Protocol (SIP).

For additional information on LTE network see

(Holma and Toskala, 2009, Dahlman et al. 2007).

3 BASE STATION APPLICATION

OPTIMIZER

We suggest a simple solution to the backhaul

bottleneck problem. The traffic load on the backhaul

links can be reduced by replacing the traditional

Base Station entity with the BS-OPT, a smart entity

capable of analyzing and optimizing the user data in

the application level. This section describes the

suggested solution architecture, support for user

mobility and finally discusses possible benefits and

limitations.

3.1 Architecture for the Base Station

Application Optimizer

Two options are considered for the new BS-OPT

architecture as described in Figure 2. In the first

architecture the BS-OPT is a stand-alone entity, not

integrated inside the base station, probing and then

analyzing and manipulating the traffic. The second

architecture is an integrated unit inside the base

station. Pros and cons of each solution are discussed

below.

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Figure 2: Data flow with the Base Station Application

Optimizer.

The stand alone architecture is described in

Figure 2A. The solution in this case is a separate

hardware and software package. It is located after

the base station ("bump in the wired") and is capable

to probe its entire traffic with very low delays. Note

that for each base station we should associate its

corresponding optimizer.

The benefit of this structure is mainly the

reduced dependence on the base station evolution.

This allows a complete freedom in the solution life-

cycle. Additionally, it provides a flexible network

structure: the solution can be installed at part of the

operator network and not on the entire network.

However, stand alone architecture means that we

should face the challenge of very-fast probing,

analysis and manipulation of the traffic. In addition,

it means multiple installations which are very

expensive. Furthermore, the most critical drawback

of this architecture is difficulties in supporting user

mobility. To support user mobility a new interface

should be defined between peer optimizers which

result in higher system complexity and cost.

The solution in the case of integrated

architecture is a software package installed on the

base station as presented in Figure 2(b). This

architecture saves the probing time and allows an

elegant and simple application solution. User

mobility is supported easily using, for example, the

regular buffer forwarding procedure defined by the

Figure 3: Protocol Stack for the User Plane with BS-OPT

and gateway optimizer.

3GPP specifications (3GPP TS 36.300, 2009) for

LTE as described in Section 3.2 below. However,

the main disadvantage of this architecture is the

required collaboration with base station evolution.

To avoid the difficult multiple installations and

to obey the high-speed requirement we recommend

implementing this solution as integrated software

within a base station (Figure 2 (b)).

Regarding LTE networks, the suggested protocol

stack of eNode-B with application optimizer is

described in Figure 3. Additionally, we study the

potential benefits of applying supporting

optimization algorithms at the operator gateway. The

corresponding suggested protocol stack of the

gateway is presented in Figure 3 too.

3.2 User Mobility Support

The LTE handover procedure is described in Figure

4. Using the integrated architecture of the BS-OPT,

user mobility can be supported as follows. At step 8,

the source eNode-B performs buffer forwarding of

the user buffered packets to the target eNode-B. The

application optimization layer provides the user data

to the lower layers. Thus, all optimized user data

will be forwarded as usual.

Regarding the user data which is currently in the

optimization process, we have two options: either to

cancel the optimization process as a part of step 18

(release resources), or continue with the

optimization and buffer forwarding. The source

eNode-B continuing processing resources might

save more processing resources at the target eNode-

B. For example, if objects from a particular web

page are already stored in the source eNode-B cache,

it will be better to transfer them from the source

eNode-B to the target eNode-B and to avoid the

double page request from the server.

We believe that both options should be

implemented and the choice between them should be

made on-line dependent on the specific traffic

characterization.

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121

Figure 4: Message chart of the LTE handover procedure.

The control plane messages (solid and dot-dashed arrows)

and the flow of the user packets (dashed arrows) are

reported (3GPP TS 36.300, 2009).

3.3 Benefits and Limitations

The essential benefit of application layer

optimization in the base station (BS-OPT) is data

reduction at the backhaul bottleneck. This can help

in reducing the total backhaul upgrade costs and

improving the user experience by providing higher

actual data rates and shorter delays.

A major latency and data reduction can be

achieved by implementing an application cache at

the BT-OPT. Cache such as web cache, P2P or

streaming cache can reduce the traffic significantly

as a function of the cache size and the user

behaviour. Furthermore, researches have shown that

users do not tend to move a lot while consuming

data applications. In fact, according to (Halepovic

and Williamson 2005), users have high probability

(over 85%) to be connected to the same cell- the so-

called "home-cell". Obviously, such pattern of user

behaviour increases the cache hit rate dramatically.

Additional data reduction can be done by

replacing many live video streaming between each

user and an internet live video streaming server (see

Figure 5(a)) with a single live video stream between

the BS-OPT and the streaming server, and only then

delivering uni-streams to the specific users between

the BS-OPT and the users' in the cell (see Figure

5(b)). The BS-OPT should replace the Internet

Group Management Protocol (IGMP) role and

establish the users multicast group memberships.

The BS-OPT can control the transformation of the

single video stream into multiple uni-streams

according to the multicast group members list.

Meaning, it can hold a group table with all multicast

group members' details similar to IGMP.

Using location information, BT-OPT can help in

optimizing P2P traffic using algorithms similar to

P4P (Xie et al. 2008). The location information can

help optimizing the traffic path between peers and

reduce the file download latency while reducing

network resource consumption as demonstrated in

Figure 6B. However, implementing this feature

required capabilities of IP routing with mobile users

within the BT-OPT, such as suggested in Cellular IP

(Valko, 1999).

Additional data reduction and improvement in

the user experience can be achieved by fitting the

proper picture resolution/format, video format and

transfer rate to the handset capabilities and to the

available resources in the cell. Another example of

simple possible data reduction at the BT-OPT is file

compression. Many handsets do not support

compression formats. Once the User Agent (that is,

the browser application) of the handset reports that it

does not support compression formats, the web

servers avoid the file compression and response with

acceptable file format. The BT-OPT can overwrite

the relevant HTTP header to reflect compression

format support resulting in a compressed file

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Figure 5: Using BT-OPT to optimize live video traffic.

Figure 6: Using BT-OPT to optimize P2P traffic.

transmission by the web server. The BT-OPT can

then decompress the files and transmit them to the

handset according to the original file format. A

supporting Central Optimization Entity at the

operator gateway can provide additional data

reduction such as compression of files that were not

compressed by the web servers or Delta compression

of any data traffic between the gateway and the BT-

OPT.

However, implementing Deep Packet Inspection

(DPI) technology in the base station is simple in

concept but complex in practice. Conceptually,

inspecting a packet to determine subscriber and

application type and then acting on that information

looks easy. However, traffic rates and rapidly

evolving applications add complexity. Based on

present data rates, packet rates are already

staggering. Each LTE user UL/DL channel can carry

millions of packets per second. At that speed, there’s

only ~100 nsec to receive and inspect each packet,

determine its application, perform the optimization

algorithms, modify it if necessary, and forward it to

the proper destination according to the optimization

plan. As a result, the base station must include

strong multi-core, multi-threaded processors for

packet inspection.

4 PERFORMANCE

EVALUATION

Performance of the BT-OPT is evaluated by

modifying the Qin-long et al. LTE/SAE model (Qiu

et al., 2009) for ns2 network simulator. Since we are

still working on the simulations, we can only discuss

preliminary results at this stage. The major

modifications (see Figure 7) of the network model

include extending the e-NodeB to support HTTP

caching, IP routing, and streaming multicasting

management. The data reduction on the backhaul

link is measured by comparing the sum of the

packets (uplink and downlink) on the queues

between the gateway and the OPT- eNodeB (S1

interface) with the sum of the packets on the queues

between the gateway and the traditional eNodeB,

under the same traffic generation. Other

performance parameters, such as average delay and

jitter are measured using the ns2 statistics.

Traffic scenarios include 5, 10 and 20 UEs with

different traffic QoS classes. Peer-to-peer traffic is

simulated by file download between peers UE in the

cell. Regarding the streaming traffic, we see that as

the number of users in the multicast group becomes

larger, the performance improves dramatically, as

expected. Regarding the web traffic, the

performance improvement highly depends on the

assumed cache hit rate. To improve our

understanding on the solution's limitation, currently

we are trying to evaluate the effect of the additional

processing time at the OPT-eNodeB on the

performance parameters.

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Figure 7: Simulation model.

5 CONCLUSIONS

In this paper we presented a novel solution to the

backhaul bottleneck problem of wireless broadband

networks: the Base Station Application Optimizer.

Benefits of the BS-OPT are reduced backhaul

upgrade costs and improved user experience by

providing higher actual data rates and shorter delays.

However, implementing fast Deep Packet Inspection

(DPI) technology in the base station is complicated

and requires careful design.

Future work includes additional simulations to

improve the evaluation of the system potential and

its limitations.

REFERENCES

3GPP TS 36.300, 3rd Generation Partnership Project;

Technical Specification Group Radio Access Network;

Evolved Universal Terrestrial Radio Access (E-

UTRA) and Evolved Universal Terrestrial Radio

Access Network (E-UTRAN); Overall description;

Stage 2 (Release 8), FRANCE, 2009-03.

Dahlman, E., Parkvall, S., Sköld, J., and Beming, P., 2007,

3G Evolution: HSPA and LTE for Mobile Broadband,

Elsevier, United Kingdom.

Donegan, P., 2006, Backhaul Strategies for Mobile

Carriers, In Heavy Reading , Vol. 4 No. 4,

http://www.heavyreading.com

Halepovic, E., and Williamson, C., 2005, Characterizing

and modeling user mobility in cellular data network,

Proceeding of the 2nd ACM international workshop

on Performance evaluation of wireless ad hoc, sensor,

and ubiquitous networks, Montreal, Quebec, Canada.

Holma, H., and Toskala, A., 2009, LTE for UMTS –

OFDMA and SC-FDMA Based Radio Access, John

Wiley & Sons Ltd, United Kingdom.

Qiu, Q., Chen, J., Ping, L., Zhang, Q., and Pan, X., 2009,

LTE/SAE Model and its Implementation in NS 2,

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and Sensor Networks, Fujian, China, pp. 299-303.

Valkó, A. G. 1999. Cellular IP: a new approach to Internet

host mobility. SIGCOMM Comput. Commun. Rev.

29, 1 (Jan. 1999), pp. 50-65.

Xie, H., Yang, Y. R., Krishnamurthy, A., Liu, Y. G., and

Silberschatz, A, 2008, P4P: provider portal for

applications. SIGCOMM Comput. Commun. Rev. 38,

4 (Oct. 2008), pp. 351-362.

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