JDSU: Optimizing Small Cells and the Heterogeneous Network (HetNet)

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Optimizing Small Cells and the Heterogeneous Network (HetNet)

Small cells are proliferating and investment is growing in every aspect. So are the various new business models surrounding the technology—managed services maintaining the HetNet are being pitched to various operators on a global basis. In the UK, for instance, Vodafone and O2 agreed to collapse their cell sites into one network. RAN sharing is becoming common practice, creating macrocell and future small-cell backhaul infrastructure opportunities shared between operators.

Cable operators’ assets are well positioned to bring unique value to mobile operators seeking to deploy, fill coverage, or data offload with the development of a small-cell offer, with Time Warner Cable and Comcast showing interest in this space. Virgin Media UK is already offering small-cells-as-a-service and this will be an interesting space to watch going forward. With the added benefits that small cells bring, new charging and policy mechanisms are also being introduced. More importantly, with backhaul costs to small cells the same as to macrocells, the cost benefit rests heavily on backhaul. With this in mind, backhaul sharing is moving from an initial thought to definitive business case, and these are just a few of the business models that have sprung up to date.

Taking a snapshot of the news in the small-cell industry, 2012 wrapped up with Informa’s latest report showing that almost 98 percent of mobile operators believe that small cells are key to the future of their mobile networks with capacity coming to the fore. In Europe, for example, Vodafone Greece launched a location-based service driven by indoor picocells which whitelists traffic generated indoors, and this will be an interesting market to watch, not least for the impact of macro-economic conditions but in the value-add that location-based services provide.

On the pricing front, some interesting pricing models have come to the fore. These range from free to high upfront fees, as shown in the Informa Telecoms & Media table below.

Table 1. Pricing models for femtocell services. Source: Informa Telecoms & Media

Market Pricing Model Deployment Examples

Consumer

Add-ons for unlimited calling MoldTelecom, Sprint, Optus

Free femtocell Softbank, Vodafone (GR), SFR

Low upfront fee Vodafone (UK)

High upfront fee Vodafone (Italy, Hungary), Verizon

Monthly fee Sprint, Movistar, NTT DoCoMo

Enterprise High upfront fee All operators

White Paper: Optimizing Small Cells and the HetNet 2

Table 2. Femtocell deployments by target group. Source: Informa Telecoms & Media

The latest forecast from ABI Research shows that outdoor small cells will reach 500,000 units in 2013 and that the 1 W and below small-cell class will exhibit the highest growth, representing almost two-thirds of unit shipments in 2013, and continue to grow to overtake the higher-power 5-10 W microcell shipments during 2014.

On the deployment side, the big news was that the number of small cells deployed overtook the total number of macro cells in November 2012, with estimates that the number of small cells has surpassed 6 million while there are 5.9 million macrocells deployed since inception—and we are just in early stages of global deployment.

Table 3. Mobile data traffic. Source: Cisco 2012 VNI report

As of November 2012, 9 of the top 10 mobile operator groups (by revenue) offered femtocell services. In Japan, NTT DoCoMo announced the launch of a dual-mode 3G/LTE femtocell, future-proofing their investment.

Target Group Number of Deployments Examples

Consumer 26 Vodafone UK, AT&T, Cosmote

Enterprise 6 T-Mobile UK, Network Norway, Orange France

Consumer and Enterprise

8 Vodafone NZ, Verizon Wireless, Sprint

Public 5 Vodafone Qatar, SK Telecom, TOT Thailand

Rural 1 Softbank (using satellite backhaul)

Exabytes per Month

2011 2012 2013 2014 2015 2016

12

6

0

0.6 EBper

month

1.3 EBper

month

2.4 EBper

month

4.2 EBper

month

6.9 EBper

month

78% CAGR 2011-2016

10.8 EBper

month


Mobile traffic is predicted to grow anywhere from 20 to 50 times over the next 5 years. It is clear that urgent action is required to meet these demands. For operators to take urgent action, they need to look deeper, and two data points come to mind. The first is that almost 80 percent of data traffic happens indoors, while just 10 percent of cells handle almost 90 percent of data traffic.

When you couple this information, it becomes very clear that a multi-faceted approach is needed to solve this traffic growth. It becomes clear that optimizing those 10 percent of cells, whether macro or small, will have a major impact to the network capability. When combined with optimizing traffic for indoor scenarios, this will have a force-multiplier effect.

Get the Most Out of Your Macrocell NetworkWith billions already spent, operators first look at squeezing the most out of their existing macrocell networks. There are several strategies that are getting attention to this end.

Multicarrier — If a carrier finds that they have available spectrum, re-farming of spectrum offers a cost-efficient way to increase both coverage and capacity.

Sectorization — Allocating more sectors in one form or another reduces the need for new macrocell sites. By adding additional sectors, operators are getting increased capacity and, to a lesser extent, increased coverage but without densification in the macrocell network.

Figure 1. Different sectorization options. Source: Nokia Siemens Networks

80 percent of data traffic is indoors

90 percent of data traffic is handled by

fewer than 10 percent of cells


Tilt Optimization — The third macrocell optimization strategy is antenna tilt, which minimizes interference. This has the added effect of increasing capacity at a low cost, and as such should always be pursued before any further optimizations. It is a powerful optimization technique as it has the most direct impact on coverage and interference parameters of the network. When you couple this with the advent of remote electrical tilt (RET) antennas, tilt optimization lends itself well to antenna-based self optimizing networks (SON).

Figure 2. Impact of antenna tilt on coverage and interference. Source: Nokia Siemens Networks

Cloud RAN — C-RAN takes its form from a distributed base-station architecture by pooling baseband processors in a central location and distributing remote radio head ends which are connected through dark fiber to the baseband engines. The interface between the baseband and the remote radio has been standardized by a few industry consortia including CPRI (Ericsson) and OBSAI (Nokia).

Figure 3. Distributed wireless base-station architecture.

Source: FrankRayal.com

This approach provides marginal improvements in capacity and coverage where there is abundant fiber. In this case, operators benefit from reduced cost, reduced power consumption, smaller footprint, and lower maintenance cost.

Downtilting reducesinterference to neighbors

Uptilting increasescell coverage

Outdoor

Antenna

Optical Fiber Cable

OBSAI/CPRI Interface

RemoteRadioHead

Backhaul

Indoor

BasebandProcessing


Small CellsOperators are shifting their focus to a three-pronged approach to squeezing out more capacity and coverage:

• Moving the base station closer to the user equipment results in a higher-quality air interface, which provides better spatial efficiency

• Spectrum increase: more spectrum is being freed up in an attempt to meet demand

• Spectrum efficiency: moving to LTE delivers better spectrum efficiency

Figure 4. A three-pronged approach to capacity needs

With higher signal quality using small cells, more bits can be transmitted at the same time, which leads to better throughput. When you combine this with new spectrum it has a multiplier effect. Couple that with the spatial efficiency of small cells and you get the force-multiplier effect of a theoretical 1000x capacity increase as highlighted in Figure 4. Note that for completeness, other methods from various vendors get to the 1000x by 10x more performance and 10x more spectrum with 10x more cells.

Apart from the capacity increase, small cells enable:

• Better latency: users will experience faster download and upload times

• In-building coverage: small cells invariably provide better in-building coverage and this can represent a significant source of revenue for network operators

• Better cell-edge coverage: small cells provide better cell-edge performance than macro cells, resulting in better quality of experience


Informa published a report recently that highlighted the industry’s views on what the important factors are concerning small cells, summarized in Figure 5.

Figure 5. Factors affecting small-cell deployment

The Self-Organizing Network (SON) ImperativeA difficult factor when introducing and managing hetnets is that an operator is potentially facing not one dimension, but five dimensions of difficulty while trying to offer a seamless user experience.

Coexist with Macrocells• There are more handovers, requiring

efficient and higher capacity to handle the higher signaling traffic.

• There is more neighbor management, with neighbor lists across clusters of small cells and their larger cousins.

Coexist with Macrocells• Self-organizing Networks (SON)

capabilities coordinate between cells, harmonize parameters, maximizing performance of the entire network.

• Network continually monitors its own performance, traffic type and source, adapting itself automatically to achieve optimal performance, automatically coming into service.

Interference Management• As the number of cells increase in a

mobile network, there are more cell borders, leading to greater potential for interference.

• Automatically adjust its transmit power and scheduling of resources to avoid intercell interference.

• Power levels need to be carefully set to balance interference and coverage.

Backhaul• Careful planning and performance

management is required to avoid creating bottlenecks where capacity is restricted by insufficient backhaul upstream.

Vendor Interoperability• Open interfaces essential to optimize

performance between multiple vendors

• With such close interaction required between the different layers of a Heterogeneous Network, it is important that open standard interfaces are implemented. These allow different vendors products to be used in different parts of the network, so that the best products can be selected for different tasks.

Other

An operator could try to manage perhaps 100,000 small cells, and 1000s of macrocells, with some on UMTS, some on LTE, all on different vendors gear, sharing some RAN resources, and leasing backhaul from another player. How do you connect and manage the network and its issues? This is the big operational challenge operators are facing when going small.


A SON tries to address these challenges. A 2012 Infonetics report highlights the top reasons for implementing SON in Figure 6.

Percent of respondent operators

0% 20% 40% 60% 80%

Opex reduction

Improvement in capacity, quality,and network performance

Small cell usage in the network

Figure 6. Top reasons to deploy SON

Both the 3GPP and the Next Generation Mobile Network Alliance (NGMN) have working groups making progress attempting to standardize features and use cases. As an example, some NGMN requirements are listed below.

Performance management Self optimization

Real-time monitoring Fault management/correction (self healing)

Self configuration (installation) O&M-related SON (extension/upgrade)

Table 4. NGMN SON focus

SON was introduced as a 3GPP release 9 feature, and now there are over 30 use cases for SON. The breadth of these use cases, in terms of what they address within the network, is vast enough that most SON vendors focus on only a specific area. It will be interesting to see how the SON market matures as with the advent of LTE and its heterogeneous nature, perhaps it seems logical that operators might end up with multiple SON vendors, all part of the same network.

On the SON revenue front, most vendors are still in a pre-revenue stage with the likes of Intucell, Spidercloud, and Reverb networks leading the charge. Intucell was recently acquired by Cisco for $475M, showing the importance of this technology in this fast-moving space.

With device-assisted SON, user equipment can collect measurements on network performance. However, standardization of this feature has been slow, with the access network discovery and selection function (ANDSF) being the most prominent method for achieving this.

There is a growing school of thought that SON is equivalent to vendor lock-in, which may detract from the business case for HetNets. Perhaps a hybrid of SON solutions will allow for vendor differentiation—but at what expense to interoperability?


Adding Intelligence to Smart Cells Early signs in this space can be found in running things like network and application services. Intel recently conducted a trial on this subject and the results showed that deploying intelligence at the access point can radically change the traffic profile. This can make the small-cell business case far more attractive.

A summary of the benefits and methods are shown below.

Figure 7. Adding intelligence to smart cells

If an operator can radically change a traffic profile at a moment’s notice, there are two very compelling benefits. The first is that you can cut the amount of traffic being backhauled. The second is that the user experience can be greatly improved with faster web page and video downloads.

There are several ways the industry can achieve this.

Caching Content — Even with exponential traffic growth over the past and future years, analysis shows that there are many situations where multiple users actually access the same content. Some examples are popular TV shows, and a prime example is with viral videos such as “Gangnam Style” with over a billion viewings. Geographically-relevant data such as maps and restaurant guides are similar draws. In these situations, the network will be inundated with requests for the same content.

Caching can deliver a number of benefits if an operator can predict the content that the user will try and pull from the network. This is the concept of predictive caching. If the carrier predicts content and downloads it off-peak, the traffic profile can be evened out during the day, reducing load at peak times. When the user pulls content, whenever there is a hit, it actually comes from the cache. This saves on backhaul needs.

Caching content

Transrate videoWireless policy management

Cuts backhaul tra�c Improves the user experienceBene�ts

Method

Determining what content users will most likely

want to download

Managed o�oad Local transrate in the small cellHow

Running apps closer to users improves QoE

Caching and o�oading cut backhaul costs


SMALL CELL

Predictive Caching Proactive Caching

Users access the same content such as popular TV shows,YouTube videos, sports, maps …

• Carrier predicts the content and downloads it o�-peak

• Evens out tra�c pro�le during the day, reduces load at peak times

• Content downloaded once and cached for future requests.

• Reduces backhaul tra�c all day

User requests content

First play direct from YouTube

Subsequent plays from the cache

Carrier predicts content, downloads prior to request

First and subsequent plays from cache

The other method of caching is proactive. Here, the user downloads content from the network and, once content is downloaded, it is stored as it traverses the small cell. Any subsequent request for that content will be served by the small cell’s cache. This process is described below.

Figure 8. Content-caching mechanisms

Transrating Video — One forecast predicts that by 2015, video will consume 90 percent of mobile traffic, and it surpassed 50 percent just last year. Any intelligence that can be added to optimize this bandwidth hog would have far-reaching benefits. To this end, content-aware technology is making big inroads into mobile operators’ strategies. Content awareness brings valuable visibility by analyzing and understanding a packet’s contents. It also recognizes the type of application or service to which a packet belongs. For example, the technology has the ability to see the header and the payload, most notably Layer 4 through Layer 7. The data contained in these layers is then adapted to the user device and modified to optimize delivery. Video is one application that can benefit from content awareness. Inspecting packets from Layer 4 through Layer 7 reveals valuable information for mobile network operators.

Transrating comes in a couple of different variants. The first is where the codec is transrated in a dedicated node at the edge and then decoded right at the small cell, reducing the backhaul bandwidth needed. Bear in mind that reducing the peak load has the greatest impact on reducing backhaul costs. Transrating reduces a video down to a lower bit-rate codec while minimizing the impact on the video quality. More prevalent these days is doing the transrating on the fly, meaning adapting to network conditions (intelligent transrating). Figure 9 highlights different transrating mechanisms.


Figure 9. Video Transrating Mechanisms

Another transrating method is where the node is still at the edge encoding on the fly, and if a smartphone is sufficiently powerful and supports the same codecs, the transrated video is delivered to the smartphone directly without the smart cell taking the burden of transrating.

On the business front, a recent Tellabs report forecasts that transrating can save 30-50 percent of needed video bandwidth; this amounts to a considerable savings for operators’ backhaul costs. Later, we discuss managed offload, where Internet traffic such as video is routed away from the core of the network and sent through other means such as a home owner’s Internet connection.

Small Cell Backhaul While deploying large numbers of small cells near to consumers helps solve the capacity and coverage problem for the RAN, it also creates a new one for backhaul, which must provide connectivity at sufficient capacity and quality of service. Backhaul is perceived as the most critical factor for a small-cell platform, followed by the ability to self-optimize and cooperate with the macrocell network.

Figure 10. Barriers to entry for small-cell backhaul

Working with building owners for site,power, backhaul network connections

Percent of operators rating it a barrier

0% 10% 20% 30% 40% 50%

Source �ber backhaul connections

Determining if Line of Sight (LOS) orNon-LOS (NLOS) is needed for backhaul

Availability of suitable backhaulproduct form factor (size, color, shape)

Content-Aware Video Bit Rate Throttling

Smart Video Transrating for Bandwidth Reduction

• Delivers video consistent with the viewing rate• Analyzes the encoded video stream and estimates the video content in the bu�er of the subscriber device

Content-Aware Video Transrating• Frames and bit rate in the video stream are analyzed and reduced without noticeably degrading viewing quality • Network operators can support more users without a�ecting QoE

Device-Aware Video Transrating• Reduces the bit rate of the video to �t the speci�c device, saving bandwidth

Network-Aware Video Transrating• Real-time RF conditions are estimated and the video bit rate adapted to match varying bandwidth conditions • When network conditions are poor, the bit rate is reduced to minimize or eliminate potential stalls while

still maintaining good video frame quality


Ericsson recently published a report on backhaul trends. The report indicates that microwave domi-nates the backhaul space and, over time, fiber will replace copper. Another takeaway is that TDM backhaul is quickly being eroded and replaced with packet-based backhaul.

Figure 11. Projected changes in backhaul technologies. Source: TIA 2012|©Ericsson AB 2012

Table 5. Projected mobile broadband backhaul bandwidth demand. Source: TIA 2012|© Ericsson AB 2012

Another trend with backhaul is that of bandwidth to support the growing RAN capacity. An 80/20 rule applies here also: 80 percent of sites in 2010 had on average 20 Mbps, but by 2015 will grow to 100 Mbps. The remaining 20 percent of sites have much higher averages and are growing to as much as 1 Gbps.

In choosing the right backhaul transport technology, the number of choices can be a burden. The Layer 2 Ethernet solution is cost-efficient, but perhaps lacks reliability, scalability, and manageability. The Layer 2.5 MPLS technology however is a virtual-connection-oriented tunnel technology which easily adapts to various scenarios. A third option of Layer 3 routing provides a way to adapt to more complex traffic models. Some Layer 3 advantages coming to the fore are increased flexibility regarding IPSec as well as different services that can be routed separately such as Internet offload. An important aspect to Layer 3 transport is that it lends itself nicely to SON.

Inst

alle

d co

nnec

tions

0%

50%

100%

2010 2020

Microwave

FiberCopper

0%

50%

100%

2010 2020

Packet

PacketTDM

2011 2013 2015

80% of sites

20% of sites

few % of sites

20 Mbps

60 Mbps

150 Mbps

60 Mbps

100 Mbps

300 Mbps

100 Mbps

500 Mbps

1 Gbps

Figure 12. Layer technologies for mobile backhaul


With the exception of the last 100 meters, small-cell backhaul has more in common than not with macrocells. Key performance indicators for small cells should ideally be the same as with the macrocell network. Apart from reusing existing POPs, other existing network resources can also be leveraged. Small-cell traffic can be routed to the same concentrator in the network that the macrocell network already serves. Typically, the additional traffic from the small cells or the fact its origination differs doesn’t require additional switches or routers, at least on first pass. A recent Infonetics study shows that 86 percent of operators surveyed plan to backhaul small-cell traffic to nearby macrocell sites via a variety of locations including buildings, streetlights, and traffic and utility poles.

Copper Microwave Fiber

North America 85% 5% 10%

Europe 25% 65% 10%

Asia 10% 40% 50%

Table 6. Current regional backhaul methods

While fiber provides the most bandwidth, it cannot be cost effectively pulled to every lamp post, at least in many markets. Therefore, various forms of microwave, non line of sight (NLOS), standard microwave, and millimeter wave, will most often be the solutions of choice.

New, lower-cost wireless backhaul products with new features will be needed to support small cells. Existing microwave frequencies in the 6-42 GHz band cannot support discrete antennas, at least at the street level. New frequency bands are being considered for wireless backhaul such as 3.5 GHz, 60 GHz, and 80 GHz. This does not factor in the need for near- and NLOS-propagation characteristics in relation to small cells.

Figure 13. Small-cell RF bands of interest Source: RFMD

With public-access small cells typically needing only to support Ethernet interfaces, and the fact that typically, small cells support a maximum of 20-30 subscribers compared to the low 100s for macrocells, small-cell aggregation routers will inevitably reduce in complexity, power requirements, size, and, most importantly, cost—a huge factor in the small-cell business case. Heavy Reading, for example, is forecasting a reduction from a current 1U/2U solution down to <0.5U in the short term.

Iub vs. Iuh is also an important consideration, with much of the femtocell industry adopting the 3GPP standard luh interface for linking femtocell access points to the service provider’s network. Ericsson’s perspective is that all small cells, including femtocells, picos, and microcells should be linked by the Iub interface, which allows them to be integrated completely into the network just like macrocells, claiming less interference as it enables an operator to use the same spectrum for a small cell and a macrocell. However, the Iub interface was not popular among some femtocell proponents, particularly Nokia Siemens Networks, because its implementation is proprietary across vendors: you can’t attach a small cell over an Iub from another vendor.

<6 GHz

NLoS LoS

~6-42 GHz V Band E Band60 GHz 71-76, 81-86 GHz


Internet OffloadInternet offload is an important response to the growth in mobile traffic. The industry has, in fact, a long history of embracing technologies and approaches that could be categorized as Internet offload. 3GPP has done a lot of work with this approach over the years, starting back in release 6 with specs such as LIPA and SIPTO. Internet offload comes in several forms: WiFi, femtocell, and core network.

Figure 14. Internet offload standards

WiFi OffloadEricsson’s acquisition of the carrier-grade WiFi provider BelAir networks shows how important WiFi is to the industry. If WiFi integration is coming, the question for mobile operators is how they will adapt. Operators may continue to let the user-driven WiFi model prevail, where people offload on their own without much operator involvement. Or, they may take a more active role and offer managed offload solutions where they have more control over how, when, and what traffic is offloaded.

Operators are taking a long, hard look toward tighter integration of WiFi with cellular services. The first phase of deployment is that of hard offload. This is the current situation today, where a user simply roams into their home and content automatically offloads. The second phase is already being used by some operators such as T-Mobile and Orange, where the offload is based on SIM-based authentication. In phase three, there will be tighter integration between WiFi access and the core, offering seamless session mobility. This is where the industry seems to be heading.

Femtocell OffloadFemtocell offload differs from WiFi offload in two ways: femtocells are deployed in licensed spectrum and they are fully integrated with a carrier’s network. The core network integration is important because it means that the femtocells are transparent to all operator services. With femtocell offload, Internet traffic can be selectively offloaded through the owner’s general internet connection.

I-WLAN

UMA/GAN

Direct Tunnel

ANDSF

Femtocell LIPA

SIPTO

IFOM

3GPP Rel 6 3GPP Rel 10

WiFi O�oad Femto O�oad Core Network O�oad• Current user driven

connections to WiFi• Next wave is SIM-based

Authentication (T-Mobile, Orange)

• Next will be where WiFi access is integrated into the Core (session mobility)

• Coverage & O�oad main drivers

• A push to integrate with WiFi AP

• LIPA / SIPTO• Packet Switch O�oad by

deploying O�oad Gateways behind RNC

• RAN Aware Tra�c shaping


Core Network OffloadCore network offload reflects selected IP traffic offload (SIPTO) standards. The intent is to distribute packet gateways so that traffic is not concentrated on a handful of nodes.

Iu-PS offload involves deploying Internet offload gateways behind an RNC or group of RNCs. This has the effect of splitting out Internet traffic bound for the operator’s core network. In the same vein, the industry has started to leverage this architecture to push content caches and content optimization closer to the end user not just in the small cell itself, but at the edge of the core where greater control can be achieved. This is where an offload gateway is served either directly from the Internet (for example, from an Akamai server) or from a local server such as a mobile CDN collocated with the gateway.

An unintended consequence of core network offload is that by diverting traffic from the core, it becomes harder for the operator to meter usage, bill for traffic, and apply traffic management. This is also an issue for content caching. A proposed solution is to make the offload gateway also function as a traffic management device, which means integrating deep packet inspection and policy enforcement capability mirroring the core network capability. For this architecture to be optimized, the traffic management function needs to be increasingly RAN aware. However, this has the added consequence that the offload gateway should have access to standardized RAN and policy management interfaces which are still being standardized. This lets it adapt dynamically to load conditions on the network.

SummaryOptimizing current macrocell and future heterogeneous networks requires a multi-dimensional approach. It begins with an operator cost-efficiently optimizing an existing macrocell network. The next step is densification of the network with the addition of small cells to the existing infrastructure.

Capacity and coverage are driving this approach, and further enhancements to the resulting HetNet should be made with the goals of cutting backhaul costs and improving the customer’s quality of experience. The combination of using small cells and turning them into smart cells has a force multiplier effect both on backhaul saving and quality of experience.

The importance of SON is highlighted by the multi-dimensional issues associated with densification and HetNets. There is some industry standardization work going on, with the goal of reducing small-cell backhaul with techniques such as offload perspective as well as local caching, video optimization, Wi-Fi integration, and various Internet offload techniques.

If an operator is to improve a customer’s quality of experience while managing the exponential growth in their network’s complexity, they will need a highly-integrated solution that is fully adaptable to network conditions as they happen. Adding intelligence to the network and having RAN resources direct their efforts to where it’s needed is fast becoming a necessity where once inflexibility ruled.


References• Small Cell Market Status Dec 2012, Small Cell Forum.

• Deployment strategies for Heterogeneous Networks, Nokia Siemens Networks.

• Intelligent Small Cell Trial Case Study, Intel

• Mobile Video Optimization Concept and Benefits, Tellabs

• Small Cell Backhaul Requirements, Version 1.0, 04-June-2012

• Heavy reading, Small Cell Backhaul: What, Why and How. July 2012

• Heterogeneous Networks, TIA 2012 | © Ericsson AB 2012

• O2, Vodafone allowed to hop onto each other’s towers — http://www.theregister.co.uk/2012/10/01/o2_voda/

• Integrated Wi-Fi/Picocell Platform Specification WR-SP-IWP-I01-120724 — http://www.cablelabs.com/specifications/WR-SP-IWP-I01-120724.pdf

• Infonetics Research: Small Cell Operators Face Myriad Operational and Financial Challenges — http://finance.yahoo.com/news/infonetics-research-small-cell-operators-120700233.html


Product specifications and descriptions in this document subject to change without notice. © 2013 JDS Uniphase Corporation 30173411 000 0313 SMALLCELLSHETNET.WP.NSD.TM.AE March 2013

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JDSU: Optimizing Small Cells and the Heterogeneous Network (HetNet)