Accurate RAN Planning Saves Billions
Background
During the 1990’s, RAN planning was a simple affair of drawing circles on a map, then choosing the site for a tower or a rooftop installation. With only one or two frequency bands, the tools for maximizing coverage were fairly simple. But anybody that still thinks about RAN planning tools in that way needs an update. Today, RAN planning tools are very complex, with multiple bands to consider in both coverage and capacity planning. High resolution 3D map data are used to precisely predict wireless coverage in multiple generations. In-building models are merged with outdoor models to reflect the impact of indoor small cells or DASs. Artificial Intelligence has come into play recently, as the number of optimization dimensions has grown beyond the ability of human users to achieve the best results. With 5G network slicing, a new requirement is coming into the market: Operators need to model the implementation of a network slice, with dedicated resources for specific customers or applications. The challenge here is to assess the network’s ability to support a network slice within minutes, to be able to respond quickly to a customer request.
Accuracy creates more capacity
The use of massive MIMO has made accurate RAN modeling extremely important. During the days of SISO mobile links, every 1 dB of SNR improvement could result in 0.1 to 0.2 bps/Hz in improved spectral efficiency. But we have improved our mobile links. Today, as the signal-to-noise ratio improves, we can scale up to higher QAM levels and higher order MIMO, achieving much higher spectral efficiency. In a massive MIMO system with 4-layer multi-
user MIMO in the downlink, a 1 dB improvement in SNR yields 0.5 bps/Hz of additional spectral efficiency (for users in the typical range of 0 to 10 dB SINR). That means that today’s RAN planning tools have a much bigger impact on capacity performance than ever before. The SINR achieved by each user depends directly on the RAN planning tools and their use. When the network is set up, a sophisticated 3D model is used to predict the RSRP (reference signal received power) at each location. After network deployment, drive testing is used to
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