An indispensable part of our daily routine, smart devices are causing many wireless networks to suffer, and not only due to the insatiable user demand for data. These “always on”, signaling-hungry devices do not communicate with the wireless network in a smart or efficient way. This signaling issue has been amongst the least publicized up to now.

Smart devices give the impression of being “always on”, whereas in reality they repeatedly “wake up” to ping the network for updates and then go back to “sleep”. A 3G device can be in different radio states, which can be simply described as High, Low, Standby, and Idle. In brief, the High state supports data throughput with the most network resources reserved and the device battery burden at its peak, while Idle is where devices stay dormant, save power, and are periodically active.

Signaling accounts for as much as 60% of an operator’s network costs. Applications such as mobile email or social networking demand constant updates and a smart phone’s Signaling may be 8 times less efficient than a PC /laptop dongle, and does not bring in any extra revenue. Signaling Overload due to Smart devices (SOS) will become an even bigger headache as smart devices get cheaper, more user-friendly, and provide access to more/better content (apps).

Many mobile industry experts have been surprised by the remarkable success that smart devices have enjoyed in the last few years. Having become an indispensable part of our daily routine, these devices now dominate the news headlines on a regular basis.

As the mass appeal of smart devices increases, issues relating to their operation and performance gain in significance. This article focuses on one of these issues, which has probably been amongst the least publicized up to now: Signaling Overload due to Smart devices or SOS.

Smart phones have been the mobile device success story for the last 2 to 3 years, and the trend is set to continue. The demand for smart phones has been driving the growth of handset sales, and specialized vendors such as Apple and RIM now find themselves in the Top 5 mobile device manufacturers.

In addition to mobile handsets, other smart devices appear to be quite successful: wireless tablets (such as the iPad) have been launched, with sales likely to exceed the 50 million-unit threshold as early as 2011. Yet, this smart device boom, which has led to 600 million mobile broadband subscriptions in 2009, has come at a price.

As one of the main drivers behind the mobile data explosion phenomenon, smart devices are also the reason why many wireless networks worldwide are now suffering, and not only because of the insatiable user demand for data. Indeed, these always-on, signaling-hungry devices are not currently communicating with the wireless network in a smart or efficient way, which is causing concern for many 3G network operators. The situation may actually deteriorate with next-generation mobile systems (such as LTE) and when wireless devices are embedded in every consumer machine.

It therefore seems that, at least with regard to network signaling, smart devices may have been smart-looking, fashionable gadgets that have dazzled the public rather than the intelligent, efficient contraptions that their naming implies. But is the Signaling Overload due to Smart devices an issue of real concern? An SOS for mobile network operators? Or an exaggerated myth?

Everything Flows: A Mobile State of Flux
Before looking in detail at the issue of Signaling overload due to smart devices, it would be useful to consider the different radio connection states that a 3G device can be in, according to the 3GPP standards (Holma and Toskala, 2010). This is particularly important to understand why smart devices have an adverse impact on network signaling.

A 3G device can be in one of the following states in terms of Radio Resource Control (RRC) connection -- strictly speaking, the Idle state is not a Connected state:

High (Also described as DCH or Cell DCH or HSPA). In this state, the device is communicating with the network in a dedicated fashion, which requires the most network resources and the highest device power (compared with the other states).

Low (Also described as FACH or Cell FACH). In this state, the device communication with the network requires some network resources (also supporting data throughput at low rates) and some device power, but has a lesser burden than the High state.

Standby (Also described as URA, URA PCH or Cell PCH). In this state, the network is aware of the device, but there is no requirement for network resources to be consumed (Cell PCH is different from URA PCH, but considered similar herein for simplification reasons).

Idle. In this state, the network is aware of the device, but the device is in sleep mode, not really communicating with the network and therefore requiring the least amount of network resources and consuming the least power (compared with the other states).

Figure 1 illustrates these different states in a simplified fashion. It also depicts the high level relationship of each state with the device power (battery) and data (throughput/speed) performance as well as the burden on network resources.

Figure 1. A simplified radio state transition for mobile (3G) devices.

In summary, with regard to device power, data throughput and network resources, the state of the mobile device can be:

• High, to achieve the highest possible data throughput (and shortest latency), with the most network resources reserved and the device battery burden at its peak.

• Idle, to stay dormant and save power by only being periodically active, with data transmission not supported.

So, a mobile device will generally switch from one state to another as depicted in Figure 1, depending on the use scenario. Typically, transition to a more active state will be triggered by data packets (with the High or Low state selected based on data volume), and transition to a less active state will be triggered by device inactivity (based on an Inactivity timer). It is this state transitioning that causes increased Signaling from smart devices, particularly when the device is in sleep mode and needs to “wake up” and move to the High state.

The original aim of the 3GPP standard was for devices to move to the power-efficient Standby (PCH) rather than the Idle state after data transmission is over. The Idle state was to be avoided as transitions to the High state require a packet connection set-up, and hence increased latency and network Signaling. However, the Standby state has not been deployed in many mobile networks, while the configured Inactivity timers for DCH and FACH are relatively long. This is why fast dormancy, which is referred to in the next section, has been considered by some device vendors.

Always (?) On
The emergence of smart devices has monopolized the industry headlines, mainly with regard to user data throughput and network capacity demands. To a large extent, this was inevitable as mobile networks were not designed to support the currently experienced data tsunami. Much emphasis has also been put on the “always on” mode of operation of smart devices. What is often not appreciated though is that these devices give the impression of being “always on”, whereas they repeatedly “wake up” to ping the network for updates and then go back to “sleep”.

The use pattern for smart devices can be summarized as follows:

• Users tend to pick up their smart device for a short period and in a highly unpredictable manner, to make use of an application that requires manual action (e.g., to browse the Internet for the latest news or find the nearest restaurant to go for lunch).

• Mobile applications rely on updates via network server polling, often at user-specified internals that can be as short as a minute, while push-enabling applications (including Instant Messenger) require a TCP connection and the use of “heartbeat” or “keep-alive” messages, as often as every 30-60 seconds.

It is this constant signaling need that led some device vendors to consider fast dormancy before the Standby state was deployed. This device-proprietary feature overrode the long (battery draining) Inactivity timers, by going directly from High to Idle quickly after data transmission ended. The idea behind fast dormancy is simple: The device sends a Signaling Connection Release Indication or SCRI to simulate a signaling connection failure, release the RRC connection, and move to the Idle state, where power consumption is low.

However, this direct transitioning proved problematic, due to the non-standardized, device-dependent implementation of fast dormancy, and the fact that the simulated signaling connection failures could not be distinguished from actual failures. Fast dormancy increased Signaling by increasing the amount of Idle-High state switching, which requires the set-up of a packet connection. It also made it impossible for the device to stay in the preferred (Low) state.

The constant updates that many popular applications (such as mobile email or social networking) require from the network are the reason why smart devices are often described as chatty. Indeed, each such update can generate as many as 30 Signaling messages, equivalent to what a voice call would require. In these terms, it is easy to understand what kind of an impact a device that asks for updates every minute or so would have on the mobile network. More importantly for operators, such updates cost in terms of Signaling “currency”, but do not bring in any extra revenue (at least, for the time being).

According to mobile network operators’ figures, a smart phone generates more Signaling messages per megabyte (MB) than a PC /laptop dongle. The average smart phone user consumes from 10 to 25 times less data than the average mobile broadband user (with the introduction of LTE, this figure will probably be adjusted by a factor of 2 to 3.

At the same time, mobile dongles only connect to the network three times as often as smart devices. In other words, a smart device may make 8 times as many connection attempts per MB as a dongle (i.e., may be 8 times less efficient than a dongle in terms of Signaling). (See Figure 2.)

Figure 2. A simplified -- normalized -- illustration of how smart phones and dongles rate in real-life networks with regard to: data (MB) consumption; number of connection attempts; number of connection attempts per data (MB).

Operators now see Signaling traffic in their networks outgrow data traffic by 30%-50%. And, as the number of chatty smart devices increases, so will the impact on network signaling.

Of course, the effect on different networks will vary, and there may be many mobile networks where SOS has not been noticeable… yet.

Distress Signaling
Mobile network operators are concerned about the impact of chatty devices, even though their bandwidth requirements are modest compared with heavy data users. This impact cannot be concealed: mobile network/device issues have been widely publicized, especially with popular smart devices such as the iPhone.

Some of the publicized issues have to do with the described constant Signaling between smart devices and the network, which places a significant burden on the network resources and can lead to sudden battery draining or applications working slowly on the device. The Signaling burden may not be seen today in many networks, but as the number of smart devices increases so will the likelihood of network "meltdown".

Operators are aware of the danger. Smart device traffic is predicted to increase by 10,000% in the next 5 years. The impact of smart devices is expected to be an even bigger headache as devices get cheaper, become more user-friendly, and provide access to more/better content (apps). Smart phone sales were expected to exceed 250 million in 2010, after 170 million were sold in 2009. According to analysts, smart phones will outsell PCs by 2012, and there will be more smart phones than PCs by 2013. And the emergence of machine-to-machine (M2M) communications will also exacerbate the Signaling challenges.

There are some more worrying trends for operators:

• With the European Union ready to impose a cap on roaming fees, a leading mobile operator recently announced a flat-rate data package for smart phone customer roaming in Europe. As more and more operators are likely to follow a similar strategy, the issue of signaling overload may thus be "exported" to countries where mobile networks are able to cope with local demand but not with additional smart devices. And this could well lead to many unhappy mobile users (even though the reason will not be roaming data fees).

• The number of applications for use with smart devices has increased rapidly in just a few years, and is set to increase further. Apple is now claiming 300,000 mobile apps for the iPhone, while competitor platforms/devices are doing their best to catch up. With more applications trying to connect to the mobile network, more signaling issues are likely to occur.

• The use of social networking applications is rising. There are now 200 million mobile Facebook users, who are typically twice as active as fixed-line Facebook users. According to GSMA, 50% of the UK mobile data traffic is social networking related. More importantly, many applications may involve more than 1 person, especially as users start using their smart device in the same way they would use their PC /laptop.

• The consideration of smart devices in financial transactions or health schemes (patient monitoring), as part of an all-encompassing mobile life or m-life world vision, could lead to additional signaling concerns too. This also applies to many other areas of great interest for mobile network operators, such as location-based services, which are expected to expand further in the future.

• As broadband is to become a human right, support for mobile broadband may need to be extended in many countries. Although not limited to smart devices, such a development could be a challenge for mobile networks that have not faced problems up to now.

It is also important to note the commercial impact of signaling overload. According to leading network infrastructure vendors, as much as 60% of the network resources may be dedicated to supporting connection attempts and only 40% to data throughput. In economic terms, the signaling-related expense may thus account for 60% of an operator's network cost. So, while the amount of data that a mobile user consumes is driving the operator revenues, it is the number and length of times that the user's device gets connected to the network that actually cost more.

The impact of smart device signaling is not restricted to network operation and efficiency. User experience is also of great significance, as signaling can directly/indirectly affect the mobile subscriber, from the ability to connect to the network to how long the device battery lasts for.

Subscribers expect a lot from their smart devices, and are likely to be disappointed and use them less or even churn if these do not live up to expectations. Ultimately, the bottom line for operators is inevitably related to Signaling Overload. Signaling Overload due to Smart devices is a significant issue for mobile network operators. Of course, the SOS urgency will differ from operator to operator and from device (or application) to device (or application). Operators are now interested in methodologies and tools that will help them quantify the issue for different devices/applications in their network.

SOS = HELP!
Reactive or proactive methodologies can be used to understand the impact of smart devices on the Signaling 'health' of the mobile network. Three ways to measure the signaling temperature of the network or run a signaling 'check-up' are briefly described below.

Strategy A: Real-Time Monitoring.
Mobile network operators have been interested in real-time or near-real-time monitoring systems for a while. From cell focused network performance management (based on post-processing cell measurements), to call/user specific evaluation tools (based on detailed data from network traces and/or GPS), such systems have been deployed worldwide with considerable success.

These monitoring schemes are also related with the elaborate network optimization (and self-optimization) mechanisms that operators are now looking into, as part of next-generation systems.

Real-time monitoring systems are useful in identifying various network issues, including Signaling Overload due to smart devices. However, such systems are generally costly and complex, due to the large amount of considered data and the processing speed they may need to provide. More importantly, their use for SOS prevention is limited, as they rely on a posteriori analysis. In other words, real-time monitoring systems cannot prevent the launch of devices/applications that may wreak network havoc. Even if test devices are considered for this purpose, these could have a negative impact on the live mobile network and on its users.

Strategy B: Field Testing.
Field testing (also known as drive testing, if performed outdoors) has been a popular way for mobile network operators to understand how their network is performing. Field testing is a useful tool especially when a new -- and not so well tested or mature -- technology is considered, such as LTE.

Field testing can be used by operators to check the signaling performance of smart devices too. Indeed, the information that is recorded in a field trial can help operators understand exactly how a device/application is behaving in their network. However, field testing is subject to the dynamic network conditions and is not statistically bullet-proof. More importantly, similar to real-time monitoring systems, field tests can be costly, have limited preventative use and may also affect the performance of the live network.

Strategy C: Lab Testing.
Lab (laboratory) testing has been the proactive approach to testing mobile devices/applications for a number of years. Lab testing has enabled operators and device manufacturers to test devices in a controlled and repeatable environment, which is immune to statistical uncertainty. Based on systems that simulate a cell or a number of cells, lab testing has helped identify device issues early and accelerate their time-to-market in a cost-effective fashion.

Figure 3 depicts in a simplified manner the setup of the leading network simulator solution for device interoperability testing. The simulated network system shown at the top can be driven locally or remotely via a PC /laptop and is connected to the mobile device under test via an RF cable. As shown in Figure 3, the device interoperability tests can also be run in an automated mode via remote control and without manual intervention.

Figure 3. A simplified (Anite SAS) system diagram for smart device interoperability testing in the lab.

This setup can be extended to consider RF fading, and hence simulate the dynamic mobile network environment in an even more realistic fashion. In addition, mobile applications can be tested by connecting the system to internal or external data servers. In these terms, it is possible to quantify the impact of chatty smart devices on network signaling, and test devices/applications thoroughly with regard to SOS, before these are launched.

Using a network simulator, different smart devices can be evaluated and their behavior in terms of signaling measured as part of comprehensive device performance / comparison tests. Network simulation can reveal how long a smart device stays in a particular state, how many transitions occur in representative test scenarios for commonly used mobile apps, and ultimately the potential signaling impact of the device and its applications on the real-life mobile network.

Such signaling tests can be considered together with battery, acoustic quality, latency, data throughput, or other tests that have been run in the lab for a while to make the assessment of smart devices more representative. This kind of testing is also expected to become part of the device acceptance schemes that many Tier 1 operators have introduced to enhance the quality of smart devices launched on their network and gain the advantage over their competitors.

In brief, lab testing can help operators assess the signaling impact of smart devices before they are deployed and with no/minimal need to test them on the live network. In terms of cost versus benefit, lab testing appears as the most attractive means to understand if the introduction of a new device/application will have an adverse effect on the mobile network and to decide on whether action should be taken at an early stage.

Smart Devices, Smarter Interoperability
As the issue of Signaling overload due to smart devices is being acknowledged across the industry, mobile network operators are looking into ways not only to understand what the impact is, but also to mitigate/address this impact. In effect, operators are trying to find solutions to make the interoperability of smart devices with the mobile network smarter. Four such solutions are outlined below:

Solution 1. More Network Resources
Adding network resources appears to be the simplest way to counter the issue of signaling overload. At the same time, it is one of the least favored solutions by mobile network operators, for obvious -- cost -- reasons. What may be more appealing to operators is to consider additional resources indirectly, by enhancing their network architecture. And this is where femto cells, the low-power wireless access points that connect devices to the mobile network by using residential DSL or cable, come to the fore.

If the issue of Signaling Overload is seen from a user profile/location viewpoint, femto cells would be able to reduce the signaling load for mobile subscribers who are stationary, for example in an office or home environment. In fact, 80% of mobile traffic is located indoors. As a consequence, the impact of smart devices for professionals and subscribers that like to use a large number of "always on" applications could be significantly reduced.

Solution 2. New Technology Features
One of the most recently talked-about HSPA+ features is Enhanced Cell FACH (a 3GPP Release 7 DL feature and Release 8 UL feature). With Enhanced Cell FACH implemented, smart devices will be able to receive and send high-speed packet data that it has not been possible to receive/send before. Furthermore, the device transition time from Idle will be greatly reduced. In other words, with Enhanced Cell FACH, smart devices will not require a dedicated channel for receiving / sending small amounts of data, including emails. This will also have a positive effect on the device battery performance, and hence the overall mobile user experience.

Fast Dormancy, effectively the standardized version of the feature discussed earlier in this article, is a related 3GPP Release 8 feature. With the new standardized feature implemented, smart device switching will now be under the control of the mobile network, and take into account both the need to save battery life as well as the signaling impact of state transition. It is far from surprising that, according to device manufacturers, Fast Dormancy is one of the few features requested by all mobile network operators.

In accordance with 3GPP, Fast Dormancy can be supported by devices that are not Release 8 compatible, as it involves limited changes to the relevant radio protocols. It is also important to point out that there are more 3GPP Release 7 and Release 8 features of interest in terms of signaling (including Cell PCH ) or battery life efficiency that have been or will be implemented in mobile networks, and which are not commented herein.

Solution 3. More Network Intelligence
Mobile network operators have been looking into "intelligent" solutions for many years. The consideration of self-organization and self-optimization in 4G network discussions / standards is a tangible sign of what operators would like to see in the near future.

As part of such "intelligent" network monitoring solutions, the ability to have an accurate real-time or near-real-time picture of the network and take action where needed is of great interest. In such a way, operators would be able to address situations where additional resources are required due to Signaling Overload. Furthermore, operators could take unilateral action with regard to devices and/or applications that have an unexpectedly negative impact on the network performance.

Solution 4. More Emphasis on the Ecosystem
The benefits of establishing an ecosystem to address specific issues have been experienced by many mobile network operators. A representative example is the introduction of operator-driven device acceptance programs, which has encompassed Tier 1 operators and mobile device manufacturers in an ecosystem that has substantially reduced the time to market new devices.

Similarly, the issue of Signaling Overload due to smart devices is an opportunity for operators, manufacturers, and application developers to work together, as partners. Device manufacturers would think twice before implementing any feature that may be saving battery life but could have a negative impact on the network performance. Likewise, Apps stores would enhance their mobile-application-approval procedures by incorporating real-life network related criteria, which the current acceptance guidelines consider only to a limited degree.

Such a modus operandi would be preferable to the alternative option of unilateral action from network operators. Indeed, an operator has the power to shut down services that degrade the mobile network or to charge more for specific services or maybe to deliver email messages only on a periodic basis. Ultimately though, these decisions should be the last resort, and only for cases where the ecosystem fails to deliver on its promise.

The solutions to the issue of Signaling Overload described here are far from an exhaustive list. Other solutions that have been discussed include the consideration of common network access points (so as to reduce the number of packet data connections) or the setting of more appropriate network timers (so that devices stay in a particular state as much as necessary, neither too short nor too long). However, these solutions are generally regarded as service-specific or difficult to implement without having an adverse effect on the user experience.

In general, mobile networks are expected to consider features that will improve latency, data rates, and power efficiency in different RRC states as well as the general state transition behavior of smart devices. Achieving the optimal balance between staying in or moving from/to a radio state, in terms of the associated network signaling resources and device battery life, will be one of the most important challenges to address for smart devices. Mobile network operators simply cannot afford to ignore this SOS.

Dr. Konstantinos Stavropoulos, IOT Product Manager, Anite Wireless, is responsible for SAS, Anite's network simulator for mobile device interoperability testing. His 13 years of experience span antenna array systems (MIMO/smart antennas), research and mobile network planning / optimization, and software product development and management. Dr. Stavropoulos is an IET member. Anite is an international software and solutions company focused on the provision of test and operational systems in the wireless market and reservation and e-commerce solutions to the leisure travel industry. For more information, visit www.anite.com.

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