The management of cellular networks, particularly within the environment rapidly advancing
to 6G, presents considerable challenges due to the highly dynamic radio environment. Traditional
tools such as Radio Environment Maps (REMs) have proven inadequate for real-time network
changes, underlining the need for more sophisticated solutions. In response to these challenges, this
work introduces a novel approach that harnesses the unprecedented power of state-of-the-art image
classifiers for network management. This method involves the generation of Network Synthetic
Images (NSIs), which are enriched heat maps that precisely reflect varying cellular network operating
states. Created from user location traces linked with Key Performance Indicators (KPIs), NSIs are
strategically designed to meet the intricate demands of 6G networks. This research delves deep
into a comprehensive analysis of the diverse factors that could potentially impact the successful
application of this methodology in the realm of 6G. The results from this investigation, coupled with
a comparative assessment against traditional REM usage, emphasize the superior performance of this
innovative method. Additionally, a case study involving an automatic network diagnosis scenario
validates the effectiveness of this approach. The findings reveal that a generic Convolutional Neural
Network (CNN), one of the most powerful tools in the arsenal of modern image classifiers, delivers
enhanced performance, even with a reduced demand for positioning accuracy. This contributes
significantly to the real-time, robust management of cellular networks as we transition into the era
of 6G.
