Real-Time Data Analytics in Telecommunications: Improving Network Performance and Reliability

The telecommunications industry is undergoing a rapid transformation, driven by the increasing demand for seamless connectivity, low-latency communication, and high network reliability. As 5G networks continue to expand, real-time data analytics have emerged as a crucial tool for optimizing network performance, predicting potential failures, and ensuring service continuity. By leveraging advanced analytics, network operators can proactively identify and mitigate performance bottlenecks, enhance spectrum efficiency, and improve overall user experience.

The rapid evolution of 5G networks demands cutting-edge solutions to ensure seamless connectivity, efficiency, and reliability. At the forefront of this transformation is Aqsa Sayed, an RF Engineering Lead at Amdocs, who has played a critical role in enhancing LTE & 5G network performance for major Tier 1 operators in the United States. With extensive expertise in real-time data analytics and 5G optimization, Sayed has successfully deployed over 1000 sites with 5G (2.5GHz band) for the Tier 1 Operator, achieving over 85% efficiency across maintaining live databases.

Sayed has a significant contribution in real-time data integration and optimization of 5G networks for network reliability enhancement. In her work, she has improved network throughput and latency for better connection in complex deployments. On the Nokia Equipment & OSS, she has further led various teams & field operation teams to ensure the seamless integration of 5G networks with minimal downtime while maintaining excellent service delivery. She also streamlined CIQ and RFDS processes so that cost was reduced and deployment strategies became efficient. 

Beyond technical implementations, Sayed’s influence is evident in the real-world improvement of network reliability. Her insights and strategic contributions have supported Tier 1 operators in enhancing service quality by leveraging real-time informatics in DAS/Small Cell deployments. This approach has contributed to a 20% success rate improvement for network deployments in the Market.. “Furthermore, by automating CIQ creation template & validation through data-driven analytics tools, this improved time taken for pre-integration checks by 10%, ensuring faster and more efficient network rollouts” she stated. Through predictive strategies that analyze KPIs such as SINR, RSSI, and throughput, she has assisted in reducing customer complaints by 5% and ensuring better user experience.

The improvement of 5G NR optimization due to Sayed’s insights that make use of real-time analytics for dynamic adjustment of throughput, latency, and handover parameters has increased network reliability by 10%. Real-time network resource allocation toward maintaining uninterrupted connectivity in on-the-ground scenarios during large public gatherings has benefited from her work in event-specific analytics. In addition, a student project deep-dive into HetNet architecture has provided critical insights into mobility management and how multi-layered networks affect baseline KPIs. 

Reportedly, the contributions have had measurable results, including a 5% reduction in latency across Tier 1 operators’ urban 5G networks by optimizing signal paths. By addressing database synchronization issues, Sayed developed multi step protocols that seamlessly integrate new 5G deployments into T-Mobile’s live networks, while maintaining 90% reliability across all design databases. She also assisted in congestion challenges during large-scale events, using traffic flow data to ensure consistent network performance for thousands of users.

Sayed’s expertise is widely recognized in the telecommunications industry, with multiple published works highlighting her research in 5G deployment strategies, MIMO technology, interference management, and network latency reduction. Her publications, such as “5G Deployment Strategies and Interference Management in Dense Urban Areas”, “Effectiveness of Beamforming Techniques on 5G Network Performance”,” Strategies to Improve Network Latency in 5G Networks “ & “Adaptive RF Planning Strategies for 5G Networks Using Machine Learning Techniques”provide in-depth analysis and innovative approaches to overcoming network optimization challenges.

As stated by Sayed, it is toward real-time analytics that she envisages the future of predictive maintenance, network automation, and ensuring that the services are reliable in 5G and beyond. According to her, real-time data would be the basis for AI and machine learning integration as embedded into network management applications, developing automated interference detection and KPI monitoring. Sayed also looked forward to dynamic spectrum sharing and a new way of understanding data-driven resource allocation, both aligned towards higher user experience as well as operational efficiency.

Aqsa Sayed continues to push the boundaries of 5G optimization with new technological advancements that redefine the future of network performance. Her expertise in real-time analytics, predictive maintenance, and AI-driven solutions ensures that next-generation telecommunications networks have resilience, adaptation, and future readiness.