Amazon's announcement that it had sold more electronic books than the print variety during the pre- Christmas shopping season was firm evidence, if any was needed, that the era of the e-book has finally arrived.

For those who still remained unconvinced, the Consumer Electronics Show (CES) in January brought a slew of new e-readers and tablets, with HP, Dell, Lenovo and many lesser-known companies all unveiling new products.

So far, most of the e-reader excitement has been generated by the hardware vendors, particularly by the Kindle device from Amazon. But NDS is cementing its position as a technological innovator, as the first software vendor to launch an end-to-end solution for the e-publishing industry.  Building on its experience as the world's leading provider of security and middleware systems for pay-TV, NDS has created a rich, multi-level solution for the e-reader which addresses the needs of all players in the e-publishing ecosystem, from device manufacturers and publishers right through to the end user.
The role of publishers is key to the success of any e-publishing solution, as ultimately, e-readers need content to display on their screens; which in turn provides publishers – of books, newspapers and magazines – the opportunity to regain at least some of the ground they lost during a decade of ineffective dabbling on the Internet.

The digital revolution has not been kind to print publishers, who have seen sales plummet in recent years, along with an even more precipitous decline in advertising across all print media. Publishers have good reason to be wary of the Internet and the devices that display the electronic versions of their products – more often than not without regard for copyright.

Alongside the threat, however, the digitization of print media also holds out the promise of desperately-needed new revenue streams – a facet made possible by the NDS solution. Crucially, the NDS system supports advanced advertising techniques, including targeted and contextual advertising, telescopic and click-through ads, and advanced purchasing models such as the ability to rent, gift or lend content.

The inadequacy of Internet advertising as a replacement for lost subscription revenue was the key lesson the publishers took from their Internet experience. The NDS e-publishing solution will enable them to return to a two-pronged revenue strategy, while utilizing the full advertising potential of the electronic platform.

Publishers are determined not to repeat the mistakes they made in giving away their content for free on the Internet. Thus, their move into e-publishing creates a requirement for robust and effective e-publishing technology solutions, above all for Digital Rights Management (DRM,) the technology that enables publishers to realize revenue from the content published on an e-reader or similar device. To fully address those needs, the NDS solution includes protection of the service, to allow controlled access to content based on user rights, protection of the device, to prevent modification, and protection and authentication of the content itself.

Proving true to the end-to-end solution, NDS is also able to provide the e-reader user interface, which can be based on the look-and-feel of the print product, and a variety of advanced capabilities. These include proven measurement and analytical technologies, which enable service providers to better understand their users' reading habits and thus to tailor their services to their customers. And to complete the package, NDS also provides consulting services for publishers who would like to better understand the technical issues in the value chain. Indeed, before defining an e-Publishing strategy, a lot of issues need to be addressed, such as content aggregation and reformatting, security and control of the content on the end device in a retail market etc. NDS expertise is this domain is well recognized and NDS' consultancy services are well positioned to support Publishers in making the right decisions.

For now, many publishers seem to be content working with the established e-reader hardware vendors. But such cooperation is, in all likelihood, a holding tactic. Many publishers are said to be dissatisfied with the deals they are getting from the hardware vendors (typically 30% for the hardware vendor, according to published reports) and they dislike the vertical nature of the business, they would prefer that their content be available across multiple e-readers and other mobile devices.

Eventually, the publishing industry is likely to follow the lead of the Hearst Group, which launched its own e-reader at CES. "We are going to create an entity by publishers, for publishers," group president Kenneth Bronfin told the Wall Street Journal, adding that the company would also establish a portal with its content available for paid download to a variety of electronic devices.

Other companies that are reported to be planning to head in the same direction include Time Inc., Conde Nast and News Corp.

Publishers have a long road ahead of them before the print revenue they have lost to the Internet can be recovered on the e-reader. But they seem to be determined to see it through, and they will need the support of strong and innovative technology partners if they are to succeed. NDS is just such a partner.

Strategies for profiting from mobile data growth

The mobile data industry has evolved rapidly over the past two years, with the impact of growing 3G penetration, lower cost smartphones and USB laptop dongles, together with the popularity of mobile applications and flat-rate data plans. This has resulted in huge growth in data traversing operators' networks.  The market has now reached a chaotic and critical point with network congestion being felt by operators and consumers alike.

In response, operators are introducing a toolkit of network congestion management strategies that will reduce costs and improve economies of scale by balancing traffic requirements across networks and implementing real time usage controls. 

Policy control, data traffic offload, evolution to 4G, and network optimisation will incrementally reduce data delivery costs by more than 60 per cent over the next three years. A holistic approach that takes into consideration traffic growth, subscriber behaviour, and application trends is vital to long term success.

Policy control  –  how, when and under which circumstances subscribers can access  networks, applications and services – will contribute cost savings of over 10 per cent, equating to over $15 billion in savings by 2013 in the US market alone. 

Policy control provides real-time network, application, and subscriber policies that allow operators to manage mobile data growth and deliver personalised services on a far more refined level than was possible in the past. It helps operators prioritise traffic based on an individual users' subscription. So effective is policy control in reducing traffic peaks that data throughput in the busiest times can be reduced by 15 to 20 per cent, according to Chetan Sharma Consulting.

Shifting data traffic off a congested mobile network and onto another access technology fundamentally changes the economics of delivering that data. Offload is being implemented by operators globally to manage the total data throughput with, typically, two flavours: offload to Wi-Fi and offload to femtocells. In some regions, WiMAX deployments are also crucial to an offload strategy.

Operators deploying a data traffic offload strategy, using service control to ensure transparent and secure subscriber access, can expect annual network cost savings of about 25 per cent per annum by 2013. 

Infrastructure evolution to 3.5G (HSPA) and 4G (LTE) lowers the cost-per-bit for data throughput on the network, thereby reducing overall costs. Network cost is lowered dramatically with each incremental technology deployment, with the evolution to HSPA and then LTE saving just under 20 per cent in costs, according to Chetan Sharma Consulting.

Cost reduction is only one side of the equation. Operators are now creating new service models that move away from unsustainable flat-rate plans towards tiered and usage-based pricing underpinned by subscriber, service, and policy control as shown in the table.

Flexible, dynamic, and personalised pricing models that reflect subscribers' preferences and context, bandwidth and application usage, and network conditions are the wave of the future. 

Comparative cost reduction strategies, when placed alongside the new service models now being introduced, aid the development of sustainable business models for the mobile industry.

But pricing models will ultimately determine future success and growth in the sector. Unsustainable all-you-can-eat data plans will evolve to include flexible pricing models based on time-of-day, individual usage patterns, casual usage, application preferences, and location.

It is ultimately the responsibility of mobile operators to introduce these models with quality of service guarantees that are based on users modifying their behaviour. With that will come order from the mobile data chaos.

Innovative service models

Service models are evolving in a data-centric mobile world as a result of massive growth in data throughput. Flat-rate data plans are unsustainable for the heaviest users and innovation inevitable: 

l Speed-rated: These plans offer operators the ability to increase revenue from the heaviest users by placing these subscribers on the most expensive tariffs, implemented through effective policy control on the consumer side.
l Time-based: Telecom Italia Mobile has successfully deployed time-based mobile data plans. The model implements tiered pricing based on the number of minutes a user spends on the data network.
l Bandwidth usage and application specific: Next generation policy control solutions enable operators to implement controls and pricing based on bandwidth usage or specific traffic types. Operators can flexibly charge for heavy bandwidth services such as video or peer-to-peer in real time. SmarTone-Vodafone, for example, is delivering tiered services in Hong Kong based on bandwidth usage and time, as well as applications on-demand using Bridgewater's policy control and subscriber data management capabilities.
l Time of day: Operators in mature markets have seen a clear time-of-day usage pattern emerge for mobile data. Similar to other utilities, they can charge more at peak times according to  network capacity , or conversely, offer consumers incentives to download during  quiet network times.  Underpinned by policy control, dynamic and transparent pricing enables operators to effectively manage peak loads.
l Location-based service models: Traffic patterns over the past two years demonstrate that the most congested cell sites are in urban centres. Implementing charging models based on congestion is commonplace -London's congestion charge zone for example. Could operators implement a similar model on their mobile networks if guaranteed quality of service is the outcome?
l Quality of service models: Guaranteed QoS comes at a cost to operators, especially in mobile networks where bandwidth is necessarily a shared resource. But the emergence of ‘bandwidth boost' models- whereby a user is offered a short-term increase in bandwidth for a set fee for example – provide the opportunity to implement service level agreements.
l Ad-funded solutions:  Mobile advertising is beginning to emerge as a revenue source for operators. With subscriber data privacy concerns now being addressed, mobile advertising could create new revenue streams for the operator, personalized offers for the consumer, and more brand awareness for the advertiser .
l Mobile commerce driven: Japan offers insight into a commerce-driven mobile data market, with an open ecosystem driving adoption and consumer spending on services. Leading mobile Internet players including Yahoo! Japan have developed a viable market for content, services and mobile advertising in partnership with mobile operators.

Operators need to take into account the total cost of ownership of their next generation  backhaul strategy, and that includes spectrum costs. But with microwave spectrum costs set to rise, it will take careful planning to ensure operators have the best solution, says Alan Solheim

The promise of new services and new revenue streams is driving the adoption of next generation radio access technologies such as HSPA+ and LTE. These technologies promise a mobile internet experience that is virtually the same as broadband access at work or at home,. creating an entirely new profile for the traffic coming from the base station and, in turn, the backhaul network.

HSPA+ networks require 50 to 100 Mbps per base station and LTE networks require 100 to 200 Mbps per base station – an order of magnitude more than traditional 2G or 3G networks. In addition, this traffic is predominantly IP based. Traditional TDM radio systems can not handle this backhaul traffic, and leased E1 circuits are not cost effective. The viable backhaul solutions going forward are owned or leased services based on fibre or packet microwave radio.

While fibre provides almost unlimited bandwidth, the majority of the base stations do not have fibre connections. In addition, depending on the amount of radio access spectrum that is available to the mobile operator, additional base stations will have to be deployed in order to deliver the expected bandwidth per user. It has been estimated that operators with 50 MHz of radio access spectrum will have to double their base station density in order to deploy advanced 3G services. These new base stations must be placed where they can provide the most effective re-use of the radio access spectrum, and only by chance will they have fibre connections at the most desirable locations.

The net result is that in order to use fibre as the backhaul medium, lateral runs from the nearest fibre point of presence must be installed in the vast majority of cases. The cost of these lateral fibre runs are proportional to the distance, and the cost per meter is proportional to the population density – highest in the city centers where demand for 3G+ and 4G services is greatest.

The business case for packet microwave, on the other hand, is almost distance insensitive. The cost to purchase and install a packet microwave link is relatively constant up to a distance of several kilometers; longer than the typical base station spacing. The combination of these two facts means that a significant portion (majority) of the backhaul network will be implemented using packet microwave. This, coupled with the already heavy use of microwave for the GSM network and other private networks, will result in congestion, especially in the city centres, for the RF channels required to support the new packet microwave deployments.

In response to these concerns, many of the world's telecommunications regulators have implemented new measures to more carefully manage the available microwave spectrum. Several European countries including France and Russia have essentially eliminated larger channel bandwidths (56 MHz and above) in order to encourage greater efficiency in smaller channels . The Office of Communications, the independent regulator in the UK, is addressing spectrum congestion with a pricing strategy that favors higher frequencies and smaller channel sizes.

With most regulators adopting similar pricing strategies, it is clear that larger channels and lower frequencies are cost prohibitive for most operators. While all other cost elements will remain relatively fixed, spectrum cost is proportional to the size of the channel. If the operator has to double the channel size of the backhaul link the cost of spectrum – already one of the dominant cost contributors – will grow linearly. Looking at future capacity requirements, where hundreds of megabits or more will be needed, careful spectrum utilization planning will be essential in order to ensure the ongoing economic viability of these backhaul networks.

Despite the significant impact to the total cost of ownership (TCO), spectral efficiency is often a secondary consideration when evaluating microwave backhaul solutions. The following chart (Figure 2) represents the backhaul business case sensitivity to various cost elements. These costs are varied according to ranges found in existing backhaul deployments, illustrating the potential impact that each can have (positive or negative) to the operator's total cost of ownership.

As shown, spectrum cost has a much greater impact on the operator business case when compared to items such as equipment and installation costs. The wide range of potential impact is due to a combination of pricing variation and the degree of efficiency in existing deployments. Equipment cost, while important, is often overemphasized in the buying decision as it represents a small fraction of the TCO. This highlights the importance of selecting a microwave solution that minimizes key operating expenses such as spectrum licensing. While all other cost elements will remain relatively fixed, spectrum cost is set to rise dramatically, becoming the dominant ongoing expense for operators  deploying broadband mobile networks.  This is due to the fact that many existing microwave backhaul solutions will not scale sufficiently within existing spectrum allocations, resulting in additional spectrum investment for operators.

Fortunately, next generation packet microwave systems address many of these elements with capabilities such as all-outdoor deployment, reduced antenna sizes, and most importantly a suite of technologies which deliver a dramatic improvement in spectral efficiency relative to previous microwave systems. As shown in the figure below the introduction of higher order modulation, adaptive modulation, Cross Polarization Cancellation (XPIC) and now baseband bandwidth optimization techniques has increased the spectral efficiency by almost a factor of 10 over the past decade. These techniques provide the capability for mobile operators to deliver bandwidth suitable for LTE base stations within their existing 7 or 14 MHz backhaul channel allocations. This avoids the need to spend the time and money on re-engineering the RF portion of the backhaul network, eliminates the concern over backhaul spectrum availability for base station upgrades, and reduces the total cost of ownership by up to 40%, resulting in cost savings that are much higher than the cost of the new equipment.

The demand for mobile broadband services is seemingly insatiable, driving the rapid adoption of 3G and 4G networks. Packet microwave is a preferred technology to provide the backhaul for these networks due to speed of deployment, simplicity and cost. The backhaul technology decision will be much more than about the box cost, but will have to include the total cost of ownership including spectrum lease costs. While the availability of spectrum for the backhaul network of next generation mobile networks is a concern, advancements in the spectral efficiency of packet radios are providing the answer.

About the author of this article: Alan Solheim is VP, Product Management, DragonWave

Selecting the best Network Intelligence strategy will help operators meet the needs of rapidly changing business models, writes Keith Cobler

Network operators today are faced with many challenges that are impacting their businesses, some of which include increased market competition, technology challenges and a more demanding customer base, to name a few. Many operators have been blind-sided by these and other industry challenges and are searching for viable solutions. Business models of the past no longer work in this new market environment. Operators today recognize the importance of running their businesses based on actual data that can be analysed and evaluated in real-time. To do this, network operators are relying on network intelligence solutions that correlate high-level business objectives with what is actually occurring at the network level.

Different from Business Intelligence solutions, Network Intelligence solutions start by capturing the raw data and events that take place at the network level and transform this data into actionable information. They rely on the collection of real-time, high-quality events that transverse multiple network technologies as the basis from which meaningful information can be obtained. To some degree, the old adage of ‘garbage in – garbage out' applies since the quality of information at the output is very much dependent on the data-in and how that data is transformed into information.
In general, Network Intelligence solutions consist of three integrated components: (1) collection agents that collect data from network interfaces and elements, (2) a network correlation layer that ties together related network events and (3) analysis capabilities that empower multiple departments within a network operator with key information that allows them to manage better their individual departments and their business as a whole.

In terms of solutions, there are many solutions on the market but few that cover all three components of collection, correlation and analysis. Based on this, operators who implement a Network Intelligence strategy may decide to piece together the top-to-bottom solution using different vendors, or will look to a single provider to supply all the necessary components.

From Data to Information
A Network Intelligence solution needs to collect events at the network level and then correlate or tie them back to specific business objectives or desired outcomes. Within the network, events and transactions are captured and stored as a call data record, or CDR, which is generally used as the primary data source for calculating key performance indicators (KPIs) by network, service or customer assurance or other OSS/BSS application.

Today's Network Intelligence systems rely on signalling and media data as the primary data sources from which intelligence can be derived. Some Network Intelligence systems may also use data supplied from network elements or from other OSS/BSS systems. Different network technologies and topologies also play a factor in the types of data that can be collected and used. As networks continue to evolve, the trend has been towards combining elements and functionality into as few elements as possible in an effort to reduce costs. From a Network Intelligence perspective, this has created some new challenges in terms of getting access to the data; e.g., in LTE networks, a new network element called the E-NodeB (Enhanced Node-B), is a combination of the Node-B base-station and the RNC as a single element, thus physically eliminating the Iub interface.

Data collection devices for Network Intelligence solutions consist of probes (passive or active), element feeds and software agents. The primary difference between passive and active probes is that passive probes are non-intrusive, meaning they do not interfere or insert themselves into the data path, but rather capture the data using a mirrored port. Active probes, on the other hand, inject a test signal into the network and then measure the response of the network to that input. Software-based collection agents are generally used when physical probe deployment is impractical due to size or cost constraints.

The second key layer of a network intelligence system is the correlation/mediation layer. The purpose of this layer is to correlate all data sources end-to-end across the network and then to write this data to a CDR for post-processing. Doing this is not as simple as it sounds since most networks today are not based on a single, homogenous technology but rather have evolved and consist of a patchwork of legacy and next-generation technologies.  And to correlate a call or session end-to-end across multiple network technologies requires a sophisticated protocol correlation engine that can piece together the protocols across every leg of the connection, or in the case of IP, derive this correlation from the IP packets themselves.

After the data has been collected and correlated, the last layer of a network intelligence system is the processing of the data into meaningful, accurate information that can be used by different individuals and organizations within the carrier.

At the heart of information analysis is the KPI, or key performance indicator. In order to correlate accurately a desired business outcome with events that occur within the network, considerable attention must be paid to identify and define KPIs correctly that are meaningful and accurate and indeed drive desired business results. This is never an easy task given the underlying complexities at the network level and the interdependence between events and variables that describe these events. For Network Intelligence solutions, it is the attention paid to modelling a desired outcome accurately and efficiently, which requires an in-depth understanding of what and when to measure, that defines the value of that system.

Different from counter values that describe a given state, KPIs are formulae that give greater insight and are based on multiple inputs such as cumulative counter values, constant values, timer values and even other KPIs that have already been computed. It would be a mistake to compare one KPI to another just by name alone without knowing the exact definition of the KPI, the criteria used to select its inputs, and any other information that may impact the KPI's accuracy or manor in which it can be used.

Linking your desired business outcome to events at the network level is the basis of network intelligence. To do this, a model of the system needs to be developed that links the desired business outputs to the dependent variables at the network level. In most cases, model development is a science in itself with many considerations that are beyond the scope of this article, but in general, the process consists of three basic steps: (1) model the system, (2) compare – modelled vs. actual and (3) optimize the model. Some network intelligence systems that are available today provide off-the-shelf KPI packages, which others provide the capabilities for users to create and modify their own. In most network intelligence implementations, it is usually a combination of the two approaches that provides the most cost-efficiency and flexibility.

Another key consideration at the analysis level is the ability to provide information in real-time to those individuals or departments who need it. In addition, the format and display of this information should be tailored to the individual groups or individuals that use it. For example, whereas network operation teams may receive their information in the form of real-time dashboards and alarms in the NOC, product planning teams may require a dashboard or report that looks at more historical or geographical trends. The point being that although a Network Intelligence system leverages a common data stream, different departments within the network operator require to see the information in a format that is most beneficial and meaningful to them.

What to consider
In general, there are two classes of network intelligence solutions available today. The first class of solution is the vertically integrated solution that contains all three functions of collection, correlation and analysis and is based on an open architecture. These vertically integrated solutions are usually provided by a single vendor, but at the same time are designed using open architectures with the required hooks required to support 3rd party hardware and software. The second general class of solutions is referred to as partial or point solutions because they tend to focus only on a single layer of a Network Intelligence solution and not the entire top-to-bottom integration you find in vertically integrated solutions. Although it is a bit of an apples-to-oranges comparison, vendors of point solutions will often sell their customers that their hardware or software components can be integrated with other 3rd party hardware and software.

The chart above includes a summary of the two general classes of Network Intelligence solutions that are available today. For each of the three components that make up a Network Intelligence solution, listed are some of the key attributes that define each of these. Depending on your specific circumstances, this list may be expanded or modified considerably – and is only intended to be a rough guide for comparison.

Ultimately, the important points to keep in mind when selecting any type of Network Intelligence solution are:
1 Does the solution accurately and effectively correlate my desired business outcomes to what is occurring at the network level?
2 Does the solution provide an actionable path for different groups or individuals within the company to identify an issue and take action that has a positive business impact?
3 Does the solution have a justifiable Return-on-Investment (ROI)?

Summary
Network operators today are faced with tremendous challenges. In order to reduce the risk and better ensure that their business objectives and strategies can be achieved, network operators are moving towards Network Intelligence solutions as a means to achieve their business objectives based on what is actually happening at the network level. In short, network intelligence solutions consist of three components: (1) collection agents – that collect data from the network, (2) correlation layer or engine – that ties together relevant data from across the network and saves it in an accessible data file and (3) analysis packages – that turn the data into meaningful information that can be used by multiple departments within the network operator. The two general classes of solutions available to the market today are vertically integrated solutions and point, or partial solutions. Determining which type of solution best meets your needs is not simple, and comes down to decisions related to budget, resources and what types of systems and technologies you currently have in place. But the bottom line is that for whatever Network Intelligence system you choose to go with, it must have the ability to drive positive business results based on accurate and real-time information.