Abstract--In a previous paper, we have shown that a recurrent neural network (RNN) can be used to detect cellular network radio signal degradations accurately. We unexpectedly fou nd, though, that accuracy gains diminished as we added layers to the RNN. T o investigate this, in this paper, we build a parall el model to illuminate and understand the internal operation o f neural networks, such as the RNN, which store their internal state in order to process sequential inputs. This model is wi dely applicable in that it can be used with any input domain where the inputs can be represented by a Gaussian mixture. By looki ng at the RNN processing from a probability density function perspective, we are able to show how each layer of the RNN transforms the input distributions to increase detection a ccuracy. At the same time we also discover a side effect acting to limit the improvement in accuracy. T o demonstrate the fidelity of t he model we validate it against each stage of RNN processing as well as the output predictions. As a result, we have been able to explain the reasons for the RNN performance limits with usef ul insights for future designs for RNNs and similar types of neu ral network. In the latest generation of cellular networks, 5G, the emergence of sophisticated new techniques such as large scale MIMO and multicarrier operation has resulted in rapid growth in the total number of radio access network (RAN) configuration parameters. This carries with it a considerab le risk in terms of potential misconfiguration and is likely to significantly add to the workload for network management teams. Fortunately the recent emergence of powerful machin e learning techniques has the potential to counter this by ale rting operators to issues which might not otherwise be apparent an d providing assistance to resolve them in a timely manner. In our earlier work [1], we showed that it is possible to apply a recurrent neural network (RNN) to address an issue of particular concern to mobile network operators, namely how to detect cell performance degradations which are not being reported to the network control centre but are impairi ng the quality of service perceived by the users.

Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.