The global telecommunications landscape is characterized by unprecedented connectivity demands, fueled by a surge in mobile data traffic projected to reach staggering volumes in the coming years. Some reports indicate a threefold increase between 2025 and 2029.
This explosion in usage, driven by data-intensive applications, cloud services, and the proliferation of Internet of Things (IoT) devices, presents significant challenges for network operators. International roaming, a critical service for global travelers and businesses, is particularly complex.
Traditional approaches, often reliant on static rules and cumbersome bilateral agreements, struggle to cope, leading to high operational costs, intricate management overhead, and, crucially, inconsistent quality of experience (QoE) for subscribers. Issues like dropped calls, slow data speeds, and unexpected “bill shock” frequently degrade the user experience, directly impacting customer satisfaction and contributing significantly to churn in a highly competitive market.
Amidst these challenges, AI and ML have emerged as transformative forces within the telecom industry. Operators worldwide are increasingly adopting AI/ML to optimize network performance, automate complex processes, reduce substantial capital and operational expenditures, and deliver superior customer experiences, with market growth projections indicating a significant rise.
A key figure driving innovation in this domain is Shreyash Taywade, an AI/ML leader at AT&T with a background from the Georgia Institute of Technology. His work focuses on harnessing the predictive power of AI to fundamentally change how telecom operators manage roaming services, moving beyond the limitations of outdated methods.
The inadequacy of traditional strategies in the face of modern network dynamics and user expectations necessitates such advanced solutions. ML is no longer just an enhancement but a critical tool for effective roaming management.
The inspiration behind applying machine learning to roaming optimization
The impetus for applying sophisticated machine learning techniques to the realm of telecom roaming stems directly from the inherent weaknesses of conventional methods. Traditional roaming steering has long relied on static strategies, characterized by predefined rules and manual updates.
These approaches lack the agility needed to respond effectively to the constantly shifting dynamics of global networks and user behavior, often requiring time-consuming manual updates. “The application of machine learning to optimize roaming and predict subscriber behavior was driven by the limitations of static steering strategies, which required manual updates and lacked the flexibility to adapt to changing conditions,” Taywade explains.
“These traditional approaches often led to suboptimal user experiences and business outcomes.” The complexity is multifaceted, involving a delicate balance of carrier preferences, fluctuating network Quality of Service (QoS) across different regions, diverse device capabilities, and labyrinthine pricing structures embedded within roaming agreements.
Static rules simply cannot account for this level of dynamic variability, often resulting in poor network selection, leading to dropped connections or slow data for users, and inefficient routing, leading to higher wholesale costs for carriers. This environment demanded a paradigm shift towards a more intelligent, adaptive solution capable of processing vast amounts of information in real time.
Machine learning provides the necessary tools to navigate this complexity. Taywade’s approach leverages powerful predictive algorithms to transform roaming management from a reactive process to a proactive one.
“By leveraging models like PROPHET, GBM, XGBOOST, and AutoML techniques, the system can analyze vast amounts of data from roaming agreements, subscriber usage, and network QoS metrics in real time,” he notes. “This enables highly accurate predictions of subscriber behaviors and usage patterns, allowing for dynamic steering recommendations that minimize wholesale costs, enhance user experiences, and meet network quality targets.”
PROPHET, developed by Facebook, excels at forecasting time-series data with strong seasonality, ideal for predicting travel patterns or cyclical network load changes. Gradient Boosting Machines (GBM) and XGBoost are highly effective for classification and regression tasks on structured data, capable of modeling complex interactions between subscriber attributes and network metrics to predict behavior or optimal network choice.
AutoML techniques further streamline the process by automating the selection and tuning of the best ML models for the specific task and data, accelerating deployment and adaptation. This selection of tools is adept at managing the high dimensionality and non-linear relationships inherent in roaming data, a task where simpler static rules falter.
This strategic application of AI aligns perfectly with the broader digital transformation sweeping the telecommunications sector. In an industry where operational efficiency and sophisticated network management are paramount, data-driven decision-making enabled by AI and ML is no longer optional but a fundamental requirement for competitive success and service innovation.
Research indicates significant potential for cost reduction and customer satisfaction improvements through AI implementation in telecom.
Handling complexity in large-scale roaming data collection and processing
A fundamental challenge in developing sophisticated AI-driven roaming solutions lies in managing the sheer volume, velocity, and variety of data involved. Collecting and processing large-scale roaming data from numerous international partners and diverse subscriber segments presents significant technical hurdles, including integrating disparate data sources like roaming agreements, anonymized usage statistics, and real-time network quality metrics, often complicated by latency in data transmission between networks.
Taywade’s approach addresses this head-on by establishing a robust, unified data pipeline designed for real-time processing and analysis. This infrastructure is critical; the effectiveness of the AI models hinges entirely on the quality, timeliness, and accessibility of the data fed into them.
The architecture employs a suite of powerful, open-source big data technologies chosen for their scalability and reliability. “The complexity of collecting and processing large-scale roaming data from multiple partners and subscriber segments was addressed by integrating disparate data sources—roaming agreements, anonymized usage data, and network quality metrics—into a unified pipeline,” Taywade states.
“We employed Apache Kafka for reliable, real-time data ingestion at scale and stored raw data in an HDFS-based data lake to enable schema-on-read processing.” Kafka acts as the central nervous system, handling high-throughput, real-time data streams from various sources.
The Hadoop Distributed File System (HDFS) provides a scalable and cost-effective storage layer for raw data, allowing flexibility in how data is later processed (schema-on-read). This foundation supports the sophisticated AI applications built atop it.
Downstream processing leverages powerful frameworks capable of handling both historical analysis and live data streams. “Processing frameworks like Apache Spark and Flink support both batch and streaming analytics, allowing complex transformations, aggregations, and real-time event-driven processing,” Taywade elaborates.
Spark and Flink are essential for executing the complex computations required for feature engineering and model training/inference at scale. Furthermore, ensuring data quality and automating the flow is crucial.
“ETL tools such as Apache NiFi and Apache NiFi and Talend automated data extraction, cleaning, normalization, and validation, ensuring data quality and consistency before loading into the data lake and data warehouse.” These tools streamline the often-laborious process of preparing data for analysis.
The entire sequence is managed using Apache Airflow, which orchestrates these complex workflows, scheduling tasks, monitoring execution, and ensuring data is available when needed. This emphasis on automation and data quality management is vital for operational efficiency and the reliability of the subsequent AI predictions.
The processed, high-quality data then powers the ML models, developed using leading frameworks like TensorFlow and PyTorch, enabling accurate subscriber behavior prediction. Finally, the insights and steering recommendations generated by the models are made accessible through APIs and visualized via interactive dashboards using tools like Grafana and Tableau, providing operators with real-time operational intelligence.
Tailoring network steering with predictive models for enhanced user experience
A key innovation in Taywade’s approach is the ability to personalize network steering decisions, moving beyond generic rules to cater to individual subscriber needs and behaviors. This personalization is driven by predictive models that learn from past actions and adapt to current conditions, directly translating into a significantly improved user experience, a critical factor in customer retention.
The system achieves this by synthesizing historical data with real-time information. “The predictive models tailor network steering decisions to each subscriber’s location or usage habits by ingesting historical location and usage data alongside real-time inputs,” Taywade explains.
He adds that they apply supervised, unsupervised, and sequence-to-sequence learning to predict future needs. These ML techniques allow the system to understand diverse patterns, from typical travel routines (supervised learning from labeled historical data) to discovering new behavioral segments (unsupervised learning) and predicting future locations or actions based on past sequences (sequence-to-sequence learning), enabling capabilities like predicting customer purchases or segmenting users based on behavior.
The impact on the user experience is profound, addressing common roaming frustrations like dropped calls, slow data, and inconsistent service quality. By continuously updating its predictions with live data feeds, the system can react intelligently even when subscribers deviate from expected patterns, ensuring they are connected to the most suitable network available at their current location.
“This personalized approach directs streaming-heavy users to networks with high throughput and low latency, while business callers are steered to networks optimized for voice quality,” Taywade illustrates. Furthermore, the system’s real-time adaptability provides network-level benefits that enhance individual experiences.
“Real-time adjustments also mitigate congestion and outages by rerouting subscribers to underutilized networks, reducing dropped calls and slow data speeds.” This dynamic load balancing ensures smoother performance for everyone on the network.
The outcome for the subscriber is a seamless, reliable, and high-quality roaming experience that feels tailored to their specific needs, fostering greater satisfaction and loyalty. This focus on QoE as a primary driver, enabled by predictive personalization, represents a strategic shift towards using technology to directly enhance customer value in a competitive market.
Real-world impact: Cost savings and improved coverage through predictive steering
The theoretical benefits of AI-driven roaming optimization translate into tangible results in real-world deployments. Taywade shares a scenario where his dynamic steering solution was implemented by an international telecom carrier facing common industry challenges: escalating roaming costs and inconsistent service quality stemming from their reliance on outdated, static steering rules.
Static rules often prioritize the lowest wholesale cost, potentially sacrificing network quality, or apply blanket policies that don’t account for individual needs or real-time network conditions. The carrier implemented Taywade’s predictive system to overcome these limitations.
“The dynamic steering solution was implemented by an international carrier struggling with high roaming costs and inconsistent service quality under static steering rules,” he recounts. “By leveraging predictive models on historical travel patterns, usage habits, network performance metrics, and real-time data feeds, the system selected networks that optimally balanced cost and quality rather than defaulting to the lowest wholesale rate.”
This highlights a crucial capability: the system’s ability to perform multi-objective optimization in practice, making intelligent trade-offs. For instance, when a subscriber traveled between the US, UK, and Germany, the system didn’t just react upon arrival; it anticipated their movements.
Once in Germany, it assessed local network conditions in real-time to select the partner network offering the best performance characteristics for that specific user’s predicted needs, considering both cost and quality factors simultaneously. The results demonstrated a clear return on investment, validating the effectiveness of the predictive approach.
“This approach resulted in significant cost savings through more efficient use of roaming agreements and enhanced coverage with fewer dropped calls and faster data speeds,” Taywade confirms. These improvements directly address major pain points for both carriers and subscribers.
He further notes the cascading positive effects: “As a result, subscriber satisfaction improved, churn rates decreased, and the automation of steering decisions reduced the need for manual intervention, boosting operational efficiency.” Reducing churn is particularly impactful, as retaining existing customers is significantly more cost-effective than acquiring new ones.
This case study provides compelling evidence that the AI-driven system delivers not just technical sophistication but concrete business value, optimizing costs while simultaneously enhancing the critical aspects of network coverage and customer satisfaction.
Balancing carrier cost efficiencies with end-user roaming quality
At the heart of effective roaming optimization lies the challenge of reconciling two often competing objectives: minimizing the wholesale costs incurred by the carrier and ensuring a seamless, high-quality experience for the end-user. Achieving this balance requires moving beyond simple cost minimization or static quality rules towards a more holistic, dynamic system.
Taywade emphasizes that his approach achieves this equilibrium through careful integration of technology. “Maintaining a balance between maximizing cost efficiencies for carriers and ensuring a seamless, high-quality roaming experience for end-users involves integrating advanced data analytics, machine learning models, and real-time optimization into a cohesive system,” he states.
This highlights a systems-thinking methodology, where various components work together synergistically. The process begins with comprehensive data collection, encompassing historical usage patterns, real-time network performance indicators (like latency, jitter, packet loss), and the complex terms of roaming agreements.
This rich data feeds the predictive ML models, which are specifically designed to evaluate multiple criteria simultaneously—cost implications, service quality metrics, and even inferred individual subscriber preferences. The core of the balancing act lies in sophisticated algorithms.
“Multi-objective optimization algorithms then find the best trade-offs, sometimes favoring slightly higher-cost networks for significantly better user experiences and other times steering users to cost-effective networks when quality differences are minimal,” Taywade explains. This nuanced decision-making is constantly refined.
“Real-time feedback loops continuously monitor network performance and subscriber satisfaction, allowing the system to dynamically adjust steering recommendations to prevent service degradation.” This ensures the system adapts to changing conditions and maintains the desired balance over time.
Crucially, this complex optimization process is not a “black box.” The system provides operators with visibility and control through interactive dashboards, likely using platforms like Grafana or Tableau.
These interfaces allow carriers to monitor key performance indicators, track cost savings, understand the rationale behind steering decisions, and, importantly, “Adjust algorithm weightings based on strategic priorities,” he shared.
This ability to tune the system allows operators to strategically shift the balance—perhaps prioritizing QoE for premium customers or focusing on cost savings during specific periods—ensuring the technology remains aligned with evolving business objectives and end-user expectations.This strategic control elevates the system beyond mere operational automation.
Unique challenges in building ML models for dynamic telecom environments
Applying machine learning effectively in the telecommunications sector, particularly for roaming optimization, presents a unique set of challenges that go beyond standard modeling tasks. The environment is inherently dynamic and complex, demanding robust and adaptive solutions.
Taywade points out that the primary difficulties stem from the unpredictable nature of the core elements involved. “Building ML models for telecom roaming optimization involves unique challenges due to the variability in subscriber behavior and rapid changes in travel patterns,” he notes, adding this necessitates “continuous learning mechanisms like online learning to adapt model parameters in real time.”
Unlike static datasets, user behavior and travel plans can change abruptly, requiring models that can learn and adjust on the fly rather than relying solely on periodic retraining. Network conditions add another layer of complexity.
“Network conditions are highly heterogeneous and fluctuate frequently, so the models must integrate real-time data feeds from network elements for timely monitoring and prediction,” Taywade explains. Performance can vary significantly between carriers, locations, and even times of day.
Models must ingest and react to this real-time network state information to make relevant steering decisions. Furthermore, the intricate details of roaming agreements introduce constraints that require sophisticated handling.
“The complexity of roaming agreements demands multi-objective optimization algorithms capable of balancing cost and service quality,” he states. Other significant hurdles include data sparsity, where insufficient data exists for less-traveled destinations, potentially hindering model accuracy.
This is addressed through techniques like data augmentation, transfer learning, or generating synthetic data to improve model robustness. Privacy and security are paramount, requiring methods like anonymization and federated learning to comply with regulations (e.g., GDPR) and protect user data.
The need for rapid decision-making necessitates low-latency inference, potentially driving deployment towards edge computing architectures closer to the user. Finally, gaining trust from network operators requires transparency, making explainable AI (XAI) methods crucial for understanding why a model makes a particular steering recommendation.
Addressing these real-world ML hurdles demonstrates a mature understanding of the practicalities involved in deploying AI within critical telecom infrastructure, showcasing a proactive problem-solving approach where challenges are anticipated and mitigated through specific technical solutions.
Ensuring responsible AI in telecom roaming optimization
The deployment of AI in telecommunications, especially when dealing with subscriber data and network access, carries significant ethical responsibilities. Ensuring user privacy, preventing bias, and maintaining transparency are critical for building trust and complying with regulations.
Taywade’s approach incorporates responsible AI practices throughout the system’s design and operation, treating ethical considerations as integral, not optional. Data protection forms the foundation.
“Ensuring responsible AI in telecom roaming optimization begins with robust data protection measures such as anonymization to remove PII and encryption both in transit and at rest,” Taywade emphasizes. Beyond standard practices, advanced techniques are employed.
“We leverage federated learning to train models on decentralized data, preventing raw data transfer and reducing breach risks.” Federated learning allows models to learn from data distributed across different locations (potentially different carriers or edge nodes) without consolidating the raw, potentially sensitive, data in one place, significantly enhancing privacy.
Addressing potential bias is another key focus. Since steering decisions could inadvertently disadvantage certain user groups if models learn discriminatory patterns, proactive measures are taken.
“Fairness-aware ML techniques, including bias detection audits and fairness constraints during training, mitigate unintended discrimination by ensuring equitable network selection across user segments,” Taywade explains. This involves actively checking models for biased outcomes and building fairness objectives directly into the model training process.
Transparency is also crucial for operator and user trust. Explainable AI (XAI) methods are utilized to shed light on the decision-making process of the complex ML models.
“Explainable AI provides transparent rationale for steering decisions, enhancing trust among operators and subscribers,” he adds. The commitment to responsible AI extends beyond initial design, involving ongoing governance and operational diligence.
This includes continuous monitoring of model behavior and impact, establishing feedback mechanisms for users and operators, strict adherence to data privacy regulations like GDPR and CCPA, and maintaining internal ethical guidelines supported by staff training. This multi-faceted strategy, combining technical safeguards like federated learning and XAI with robust governance processes, demonstrates a comprehensive approach to ensuring the AI system operates ethically, fairly, and securely, building the necessary trust for deployment in critical infrastructure.
The future evolution of roaming optimization in the 5G era and beyond
The advent of 5G and the continuous evolution towards future generations like 6G promise to reshape the telecommunications landscape, introducing new capabilities and complexities. Roaming optimization strategies must adapt to leverage these advancements and address emerging use cases.
Taywade’s patented approach is designed with this future in mind, incorporating flexibility to integrate with next-generation network technologies. “Roaming optimization in the 5G era will leverage higher speeds, lower latency, and greater capacity to support diverse use cases, including massive IoT deployments with varying connectivity requirements,” he predicts.
The sheer scale and diverse needs of IoT—from low-power sensors to high-bandwidth smart vehicles—demand more sophisticated optimization. “Our patented approach will incorporate IoT-specific considerations into predictive models to ensure appropriate resource allocation and service quality for devices ranging from smart vehicles to industrial sensors.”
Emerging technologies play a crucial role in this evolution, working synergistically with AI. Edge computing, by processing data closer to the end-user, enables faster and more context-aware steering decisions based on real-time local network conditions, which is vital for latency-sensitive 5G applications.
Network slicing, a key 5G capability allowing the creation of multiple virtual networks on a single physical infrastructure, offers unprecedented flexibility. “Integration with network slicing will allow dynamic steering to the most suitable virtual networks—whether a low-latency slice for critical IoT or a high-bandwidth slice for video streaming,” Taywade explains.
This allows the optimization engine to select not just the best network partner, but the best type of virtual network for the specific service need. Furthermore, AI techniques themselves continue to advance.
“Advanced AI techniques such as deep learning and reinforcement learning will further enhance predictive accuracy and optimization efficiency, while federated learning and differential privacy will safeguard user data and ensure regulatory compliance.” Looking further ahead, the system’s architecture is designed for adaptability.
Taywade anticipates the demands of future networks, stating that the “adaptable architecture will scale to future technologies like 6G to handle ultra-reliable low-latency communications and massive machine-type communications”. This forward-looking design ensures that the core principles of predictive optimization can be applied to the evolving requirements of next-generation wireless communication, positioning the solution for long-term relevance in a rapidly changing technological landscape.
The synergy between AI, advanced network capabilities like slicing and edge computing, and the specific demands of IoT is central to this future vision.
Taywade’s work in applying artificial intelligence and machine learning to international roaming optimization represents a pivotal advancement for the telecommunications industry. By moving beyond the constraints of traditional static methods, his patented, data-driven approach directly confronts the complexities of modern global networks.
The integration of sophisticated predictive models, robust real-time data pipelines, and multi-objective optimization techniques delivers a powerful solution that demonstrably balances carrier cost efficiencies with a vastly improved and personalized QoE for subscribers. The real-world success in reducing costs, enhancing network coverage, boosting customer satisfaction, and increasing operational efficiency underscores the tangible value of this innovation.
Crucially, this system is not merely a solution for today’s challenges; it is architected with foresight, ready to integrate with and leverage the capabilities of 5G, massive IoT deployments, edge computing, and network slicing, while remaining adaptable for future standards like 6G. By embedding principles of responsible AI—prioritizing privacy, fairness, and transparency—this work sets a high standard for ethical technology deployment.
Ultimately, Taywade’s contributions exemplify how intelligent, predictive systems are paving the way for a more seamless, efficient, and user-centric future in global communications.
