Predicting radio channel behavior in an environment where wireless networks are deployed has always been an important but challenging task for engineers due to the fact that radio wave propagation models fulfill two contradictory requirements: (1) Predicting radio path loss with the highest accuracy and (2) Doing so while being computationally efficient to allow operational usage with large radio networks.
With the first 2G wireless network deployments in the 1990s, propagation modeling based on empirical models, with additional corrections based on analytical methods, became very popular. This was mainly because more advanced modeling techniques would have required more computing power than was available at that time, as well as a highly accurate, but often unaffordable, geographical database describing terrain and buildings. These models lacked accuracy because propagation phenomena were modeled in a simplistic manner. Furthermore, such models could not accommodate different propagation environments without recalibration, making them costly and very complex to deploy operationally.
Over the years, advancements in propagation modeling techniques, improvements in the underlying hardware performance, and the affordability of highly accurate geodata, have enabled the use of more advanced models in the design and optimization phases of commercial wireless networks. Today, advanced propagation models based on a tridimensional deterministic approach, such as the Planet Universal Model, as well as the use of 3D geodata have been largely adopted and have proven to be extremely accurate in dense urban environments as well as in other areas where accurate predictions are difficult (e.g., hilly terrain, low antennas). This type of advanced modeling solution is able to automatically adapt its behavior to various propagation environments by using the modeling algorithm that provides the best accuracy. This versatility solves one of the key difficulties in the propagation modeling field, which is the robustness of the propagation model when used in different propagation contexts.
With the deployment of broadband wireless networks such as LTE, operators must respond to the new radio modeling challenges that engineers are facing in order to benefit from the new access technologies, and to differentiate their service offering. LTE network rollouts are introducing new challenges including:
- Increasing accuracy in dense urban areas. In order to offer broadband data services in the most profitable areas, operators must overlay a new radio access layer onto an existing 2G/3G network that is already very dense. In doing so, the focus shifts from coverage to SINR, effectively driving the requirement to improve prediction modeling accuracy in urban areas. This requires adapting propagation modeling to dense environments and 3D geodata usage in order to accurately predict the performance of those new access layers from both a coverage and capacity point of view in order to optimize new investments.
- Supporting indoor coverage. When providing indoor coverage and data services to users located in buildings, it is crucial to maximize the return on investment by maximizing capacity and network quality in those strategic areas. This comes with complex challenges however as the signal levels tend to be insufficient at street level and in low floors, due to penetration losses, while the level if interference tends to be too large in the upper floors given the lack of signal containment. Accurately modeling wave propagation in order to tackle those challenges then becomes key to planning a network that will behave optimally for in-building users.
- Predicting more radio channel characteristics. Traditionally, radio propagation for network planningand propagation models was limited to modeling the mean path loss, along with a provision for suitable fade margins as a way to plan a good quality network for a defined probability of service. With the introduction of advanced antenna technologies in 4G, such as MIMO, it is important to accurately model additional radio channel characteristics as otherwise system performance will be impacted. In particular, large scale-fading characteristics, space-time channel dimension, delay spread, DoA (Direction of Arrival) distribution, etc. should be modeled more accurately as a way to more precisely predict their impact on the performance level of technologies such as diversity, spatial multiplexing, cyclic prefix, beamforming, etc. This will in turn ensure operators make the right engineering decisions and extract the most value out of new radio access technologies.
Mentum Planet 5.0 represents a major step forward in this direction, with the introduction of the new Planet Antenna Format, which fully supports all of those notions, as well as the support for antenna algorithm, beamforming models, MIMO simulation, etc., now all native parts of the application. Mentum is further investing and researching this area and is committed to continue leading the evolution of propagation modeling, alongside its partners, Orange Labs and CRC (Communication Research Center Canada).