Page 242 - Big data - Concept and application for telecommunications

5 Big data - Concept and application for telecommunications

6.8 Big-data-driven resource management

Network operators should be aware of their long-term deployment objectives in terms of network capacity,
coverage, and the number and locations of base stations. They also need new resource allocation strategies
in order to fulfil different traffic demands or requirements across the entire coverage area. To achieve these
goals, operators have been monitoring network QoS through driving tests with smartphones. Measurement
results are gathered from selected smartphones or specific driving test phones in their networks, which are
analysed by specialized software. However, this is not cost-effective due to excessive consumption of time
and human resources, and is also inaccurate due to the limited number of test samples.
Thus, the use of big-data analytics can provide a new way to tackle these problems. Network analytics involve
monitoring and analysing real-time and history data across users, mobile networks and service providers.
Network resources can be dynamically allocated in real time by analysing network resource data, traffic data
and user information.

By utilizing data analytics, changing resource requirements from one location to another in a specific period
become predictable. In addition to network data, behavioural and sentiment analyses from social networks
and other sources require consideration to predict where and how users may use the mobile network. For
example, when a social event such as a marathon takes place in a city, some places like the streets on the
race route may attract large crowds of people, resulting in potential congested traffic in these locations
during the event. Hence, with this predicted information from data analytics, operators can allocate more
radio resources to the hotspot in such a way that the peak traffic can be absorbed smoothly without
sacrificing user QoE.

Users often travel from one place to another around the city (e.g., work in the central business district during
the day and live in a suburb at night). This causes the traffic of each cell to fluctuate significantly at different
times of day, which is dubbed the ''tide effect.'' If resources are allocated to each cell with a fixed
configuration, resource utilization must be underestimated, and it is difficult for users in the hotspot to obtain
good QoE during peak hours. On the other hand, a great deal of resources may be wasted in idle times at low
traffic locations. Current and historical data can be utilized by data analytics to predict traffic for high-density
areas in the networks. Then, with the radio access network (RAN) architecture, predictive resource allocation
in centralized baseband units may help to accurately serve the right place at the right time (i.e., knowing
when and where peak traffic arises), causing minimum disruptions to services.

6.8.1 Big-data-driven resource allocation based on user interest
By evaluating closeness (both geographic and social) among users in the same base station, users with high
closeness are categorized into a cluster. Users in the same cluster can share a wireless channel and receive
the same content, which not only improves the data rate for cluster users, but also saves base station
resources.

Figure 6-7 depicts the use case of big-data-driven resource allocation based on user interest.

Figure 6-7 – Big-data-driven resource allocation based on user interest

234 Network and infrastructure