Here's how you can optimize mobile communications networks using problem-solving skills.

Optimize your problem-solving skills in mobile communications networks by using AIOps tools. Gather comprehensive data from network operations, including performance metrics, user behavior, and traffic patterns. AIOps tools can automatically collect and analyze this data in real time. Use AIOps to detect patterns and identify anomalies that could indicate underlying issues. Machine learning algorithms can pinpoint deviations from normal behavior more quickly and accurately than traditional methods.

estimating traffic by having an accurate traffic pattern and extracting required features using AI to tune related parameters will be helpful to have an intelligent network

In my experience solving issues over the telecom network must include a clear understanding of the problem to analize, If you do not known your enemy then you cannot defeat them, once, you found the problem, then make use of analysis traffic tools like tcpdump, wireshark, system Logs, System Alarms and Events, usually congested Link, Errors over diameter links beetwen neighbors, it could by, OCS systems, PCRF, HSS and so on. Basically you must know who are your neighbors, how they interact towars you in the communication link and found what is failing, Pay special attention to nodes KPIs also.

In my expereience, using hieracheal approach to seperate the problem into a smaller domain is effective to reduce the analysis effort. First starting from the PM/FM data to reduce the issue towards specfic domain and area (nationwide vs specfic configuration) whether it's RF coverage, Handover, capacity, traffic ...etc. Once identified, the focus should go to configuration management to the common configuration being the culpurit. With domain expertise, lets say if it is user scheudling issue, the right level of logs can be collected to pin down the root casue. Developing the approach requires domain knowledge which hopefully one day we can see AI helping.

Use AIOps to automate traffic routing decisions. Based on real-time data and predictive insights, AIOps can dynamically adjust routing paths to optimize performance and reduce latency.

Generally speaking the higher the freuquency, the cleaner the carrier and the smaller the coverage. So ideally, UE is being pushed to the higher layers whenever possible until capacity is closer to it's limits. By then, an optimized traffic management stratgey should steer traffic towards the lower layers. Unforuntaly, most networks are low band grids results in low bands being the most congested first escpeially at cell edge. Yet, this create the opporunity for more aggressive traffic balancing towards higher bands. There is alot of focus from RAN vendors of traffic steering as the very first application of AI and xAPPs/rAPPs which is very important in large venues like stadiums for example.

Network slicing maximizes mobile network capacity and enhances spectrum efficiency by creating multiple virtual networks within a single physical network. Each slice is customized to meet specific service requirements, such as bandwidth and latency. By categorizing different types of traffic, such as IoT devices, video streaming, and critical communications, resources can be dynamically allocated based on real-time demand and priority. AIOps tools enable this dynamic allocation, ensuring that high-priority slices, like emergency services, receive more resources when needed. This approach optimizes the use of available spectrum, leading to improved network performance and efficiency.

Massive MIMO offers great opportunity with Multi user MIMO where multiple users can share the same frequency at the same time during peak hours as long as orthogonality is maintained. Beam forming is another avenue that is and will help the increased specturm efficiney going forward.

Protecting your network from unauthorized access and cyber threats begins with continuous monitoring and real-time analysis of network activities. A good first starting point for automating this task is implementing an AIOps platform that leverages machine learning to detect anomalies and potential threats. Such a platform can identify unusual patterns and behaviors indicative of cyber threats, such as abnormal data transfers or access attempts.

Today large CSPs relies on perfromance data not only from the RAN and Core but also crowd sourcing data (whether in house or via vendors like opensignal) which provide valuble insights on how the end user is experiencing the network. With the great leaps of data engineering, all these sources can be merged to conclude the overall user experience and act upon.

I think there are some best practices that can be always suggested. One way to optimize mobile communications networks is to identify and troubleshoot any issues that may be causing network performance problems. This can involve diagnosing issues with network infrastructure, such as antennas, base stations, and transmission lines. Additionally, you can analyze network traffic patterns and identify areas where additional capacity may be needed to improve network performance. Finally, problem-solving skills can be used to develop and implement new technologies and protocols that can help optimize network performance and improve the user experience.