SmartPhone traffic imapct on battery and networks

Everyone enjoys the smartphone revolution - users, developers, networks operators, device vendors and network equipment vendors such as Ericsson. However, there are challenges since the network systems have not been optimised for smartphone from start. Until recently, the key optimisation objectives for mobile broadband networks have been peak rate and throughput, which are still important properties. The advent of mass-usage of smartphones, and the related traffic, has shown that also other properties of the 3G radio and networks are important. In particular, the high frequency of data activities, sometimes with moderate volumes of data transferred, has lead to both a high battery drain, and increased the signaling traffic in the system, due to the transitions between the standardised states of the 3G radio.

As for battery, different apps and different usage patterns will load different parts of the device differently. Generally though, the display is a large power consumer when using the device actively, and the 3G radio is a large power consumer for inactive devices (i.e. impacts standby time).

As for the signaling traffic, i.e. messages between the device and the mobile network for mobility and radio resource management, a smartphone user generally generates more signaling per transferred MB than a PC user. With the large number of smartphones, the aggregated effect can be large. However, it should be noted that the impact on the network depends very much on its original dimensioning. Therefore, some networks have seen large impact, whereas in others this has not been notice

Understanding the 3G radio state machine

The radio connection between a single device and the 3G network can be in one of several states:

• High - also referred to as HSPA or Cell_DCH
• Low – also referred to as FACH or Cell_FACH
• Standby – also referred to as URA , URA_PCH or Cell_PCH
• Idle

The purpose is to balance different properties. In the higher radio states, the initial delay to transmit is shorter, and the throughput is higher. On the other hand, more of the shared resources are reserved in the network, potentially blocking other users. Also, the battery drain is higher in these states (even if there is no actual transmission). Conversely, the lower states (Idle, Standby/URA) are using something called sleep mode to conserve power, by letting radio circuits be active only at periodic occasions. But in these states no data transmission can occur.

hus, there is a need to change states as required by the traffic to a device. Typically, upswitching to a higher state is triggered by any data packet (state Low/FACH or High/HSPA selected based on data volume), and downswitching is triggered by an Inactivity timer, in several steps. It is this state switching that causes increased signaling from smartphones. In particular the switching up from Idle state involves significant signaling, and significant delay before data can be transmitted. Therefore, the equally power-efficient Standby/URA state is deployed in more and more networks, allowing more state transitions and better battery life for the same amount of signaling.

There is one caveat here, and that is the so called Fast dormancy mechanism implemented by device vendors before any Standby/URA state was deployed. It overrides the initially very long (battery-draining) inactivity timers in many networks, by releasing the connection and going directly to Idle, quickly after data transmission ends. This not only increases the signaling (by increasing the amount of switching between Connected and Idle states), but also makes it impossible to go to the preferred state for battery efficiency (the Standby/URA state). Therefore, 3GPP has specified a formalized fast dormancy feature, which allows the device to trigger battery-savings, but retains control in the network to efficiently use the radio state machine available. As a gapfiller, devices are encouraged to use the pre-standard Fast dormancy selectively, e.g. only in networks with long inactivity timers, and networks are encouraged to configure shorter (battery-efficient) inactivity timers, together with the Standby/URA state.

In the future, networks will gradually be enhanced with new features to improve latency, data rates and power efficiency in different states, and improving transition between states. The fundamental tradeoff between downswitching from a state (increase signaling, increase data traffic latency) or remaining in a state (costs more battery and radio resources) will still remain. Therefore, the developers can gain, today and in the future, by designing application traffic optimized for the 3G radio.

Conclusions

The smartphone success is enjoyed by the users, the mobile industry and the developers. However, the chatty pattern of smartphone traffic poses new challenges, primarily for the device battery, but also for signaling between devices and the mobile network.

The measures to meet these challenges are in the hands of different parties:

• Operators and infrastructure vendors:
  Dimensioning and tuning of mobile networks, and deploying features for optimizing battery and signaling.
  E.g. Standby/URA state and short Inactivity timers.
• Device vendors:
  Device behaviour in cooperation with networks (managing fast dormancy)
• Developers:
  Impact application traffic to be radio- and batteryefficient
  • Minimize chattiness of traffic
  • Optimize background traffic
  • Small & infrequent keepalives
  • When transmitting – transmit it all

Looking forward to your comments.

参考：https://labs.ericsson.com/developer-community/blog/smartphone-traffic-impact-battery-and-networks