at have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play. NETWORK DATA APPLICATIONS 8.4.1 SELF-OPTIMIZING NETWORKS SON concepts have been around for some time, with some of the earliest drivers coming from the "Next Generation Mobile Network Alliance" [Ngmn08] that documented a variety of use cases. In 2012, AT&T launched a nationwide deployment of SON for automatic neighbor relations (ANR), load balancing, and self-configuration features, which is a subset of the major typical SON features as shown in Table 8.7. Table 8.7 also illustrates some of the types of radio access data being collected for SON, as well as the typical update rates for SON actions taken with the data. To put the data collected and action update rate a bit in perspective, AT&T currently collects 79 billion network measurements a day, resulting in approximately 1.4 million adjustments of the network. In comparison to typical "human adjustments" to tune the network, a good engineer may be able to make on average 10 adjust- ments per day; this would take approximately 140,000 engineers to accomplish the same volume of changes, which of course is not even practical. Nevertheless, the ability to collect, process data, and make network changes is only use- ful if those changes have a positive impact on network quality improvement. AT&T's SON implementation when implemented resulted in a 10% improvement in call retainability, a 10% improvement in throughput, and a 15% reduction in overloading [Cell12]. Perhaps, even more impressive, when analyzing the improvement in call retainability, was the 50%-75% reduction of dropped calls due to "missing neighbor cells" which was achieved using the ANR SON fea- ture as shown in Figure 8.16. Similarly, Figure 8.17 illustrates the typical improvement seen when the load-balancing SON feature was introduced into a cluster of cells, which ultimately can be realized as increased network capacity. Network Data and Optimization TABLE 8.7 Popular SON A