A highly heterogeneous 25-km2 LTE network where Incelligent enables 25% faster downloads, 10% less session failures and 45% less energy consumption.
The whole area highly varies in usage patterns, including high speed private and public transport vehicles, pedestrians in touristic and commercial spots, transportation hubs, business centres and entertainment attractions. It provides an interesting, densely populated area (dense urban / urban area type) incorporating industrial – corporal establishments on the sides of the highway that differentiate it from normal freeways (reason for low range and high range cellular demand). The following figure highlights the key user classes identified.
This use case assumes the so-called "Syggrou avenue", a high speed highway in the middle of Athens in Greece, with measured length 4.6 Km and a total covered area of 25 km2. Syggrou highway connects the city center to the seaside high traffic entertainment areas. The figure on the right shows the network layout for this use case.
In general, Syggrou highway belongs to a non-homogenous area that requires application of location/time/weather specific network settings and optimization. cells with dedicated sectors for highway coverage. We assume the extension of the network with the deployment of pico cells along the corporal area for traffic demand. It is interesting to denote some of the particularities of the area, that at the same time call for special handling in the cellular network.
Syggrou Usage Classes:
• Syggrou Highway’s car flow is subject to periodic traffic jams while in working days / hours.
• Periodic traffic bursts are happening at the two traffic hot spots located at the top and the bottom of the area.
• Week-end sports match (Sunday evening) result in rush hour traffic near the southern area hot spot and high traffic demand due to the concentration of users.
• The average achieved speed in Athens's streets is proportional to the weather conditions (slippery roads, congestion etc.).
• Traffic jam in Syggrou highway has a dramatic impact to the cellular network in 2020 traffic projections (www Access, FTP and YouTube video) due to the change of the served population of the dedicated traffic sectors and the increase in internet demand from the vehicle passengers’ terminals.
Incelligent learns the user mobility and network usage patterns and after processing a week's data of network performance and weather conditions. After this point it knows how the network state will evolve at each spot and how to optimize service quality and network operation. In this use case Incelligent applies traffic steering between the pico - cells and the relevant macro - sector. The figures below summarize the benefits from the exploitation of knowledge in the optimization decisions in the network segment in question: