Huawei iMaster MAE, China Mobile Anhui Showcase New High-Efficiency 5G Networks

At the 2022 Mobile World Congress, which was held in Barcelona, Spain recently, China Mobile Anhui and Huawei iMaster MAE shared case studies of their MBB intelligent O&M solution. Carrier executives and industry analysts who visited Huawei’s Digital Operations Transformation exhibition booth were also shown how intelligent algorithms were used to reduce duplicate alarms, cell fault false alarms, invalid work orders, and O&M costs.

Routine O&M of wireless networks typically involves responding to a huge number of redundant alarms, resulting in invalid work orders. When there are so many alarm sources, many of which are interdependent, and when network configurations are this complex, identifying alarm correlations and locating faults is challenging. The resulting inefficiencies drive up network OPEX.

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To address these O&M challenges in wireless networks, China Mobile Anhui decided to work with Huawei iMaster MAE to explore wireless network fault management. Together, they have developed the MBB intelligent O&M solution that incorporates intelligent algorithms, multi-dimensional association analysis, expert experience, and online user feedback to provide precise alarm noise reduction and intelligent fault association, proactive fault prediction and prevention, and more effective root cause analysis. The solution helps carriers move away from traditional alarm-based work order dispatches towards a new incident-based mechanism. By mid-2021, this solution was already seeing commercial use on China Mobile Anhui’s networks. Today’s case studies highlighted the solution’s impressive results.

China Mobile Anhui has seen their number of invalid work orders decrease. When alarms are reported, the iMaster MAE now analyzes existing NE alarms and automatically extracts appropriate toggling rules to merge redundant alarms. Their invalid cell fault alarms have since been reduced by 90%. In addition, to the successful implementation ofincident-based work order dispatches, where incident information is reported to the fault management system over the northbound standard interface, has resulted in a significant reduction in invalid work orders and site visits.

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Network O&M costs have been reduced. The solution uses a fault knowledge base that applies iterative learning to troubleshooting experience. Knowledge graphs are now being used to improve troubleshooting efficiency by more quickly performing root cause analysis. Since being applied to the entire network — more than 20,000 sites in total — the solution is estimated to have saved China Mobile Anhui about CNY 7.8 million in O&M costs. In addition, the intelligent algorithms are continuously learning, identifying patterns to help predict faults before they occur. The solution has facilitated a more proactive and prevention-focused approach to O&M, enhancing network stability.

Wireless maintenance expert

Liu Jingjing, Wireless maintenance expert from China Mobile

“The innovative intelligent alarm optimization solution developed by China Mobile Anhui and Huawei is based on MBB intelligent O&M and helps aggregate alarms into incidents and quickly identify network problems. Now, one work order is dispatched per incident, achieving L3 autonomy of wireless networks.”

Liu Jingjing, a wireless maintenance expert in China Mobile Anhui’s network optimization center, summed up the advantages of this new system by saying,

MBB intelligent O&M provides networks with awareness, analysis, decision making and execution capabilities. This solution can be used to improve network O&M efficiency, ensure high network reliability, and increase overall network efficiency, significantly reducing the impact of network interruptions caused by network faults. China Mobile has announced it plans to achieve L4 autonomy by 2025 and China Mobile Anhui and Huawei iMaster MAE have stated they will continue working together on the automation of wireless networks, in the hopes of transforming and upgrading the entire telecommunications industry.

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