In modern households, digital activities often occur in parallel: While parents are participating in video conferences from home, children are simultaneously using the network for gaming, YouTube, or TikTok. This leads to bandwidth conflicts, which have a particularly negative impact on real-time applications. Disruptions in data transmission impair the user experience, cause feelings of stress, and reduce the ability to concentrate in everyday work.

A modern home network should therefore aim to ensure a consistently high Quality of Experience (QoE) across all applications. Artificial intelligence (AI) offers a technological approach to manage network resources dynamically and in a user-specific manner. This article explores the potential role of AI in future home gateways – and investigates why commercial adoption remains limited.

The Router as Central Intelligence in a Home Network

As an interface between the internet and end devices, the router (home gateway) is ideally positioned to centrally manage network-related optimization tasks. This is where the mechanisms for improving QoE should start. To make this as effective and dynamically adaptable as possible, AI presents a promising option.

However, the integration of AI into these devices is still in its early stages. Current router models barely have the necessary computing power to run advanced Machine Learning (ML) algorithms locally.

Future generations of routers are expected to come equipped with dedicated AI hardware accelerators such as neural processing units (NPUs) – similar to those already found in smartphones and notebooks. This evolution will unlock new opportunities for real-time local analysis and decision-making, marking a crucial step toward autonomous, intelligent network control.

Application Areas for AI in Home Networks

An AI system for the home network aims to collect and analyze usage related data from the home network environment and derive optimization measures from it. The technological structure of such a system comprises several levels:

(1) Network Monitoring & Traffic Analysis

In this phase, the network status is continuously monitored and analyzed. Machine learning and deep learning (DL) techniques are applied for the following tasks:

  • Traffic classification (Detection of end devices[1], applications, and application types),
  • Traffic prediction (Predicting bandwidth requirements),
  • Fault management (Faults detection),
  • Network security (Anomaly detection).

Such models[2][3] have so far been tested primarily in high-performance infrastructures such as data centers and mobile networks. Their implementation in home gateways is still limited due to insufficient computing power and energy efficiency constraints.

A major challenge in this context is the discrepancy between real-world conditions and offline research environments. Studies often rely on large, static datasets, which tend to yield overly optimistic results that do not accurately reflect the dynamic and unpredictable nature of home networks.

In addition, the application of AI in residential settings raises open questions related to GDPR compliance, particularly regarding data collection, processing transparency, and user consent. These issues require further investigation before widespread adoption can be considered

(2) Smart decision-making and resource management

Based on real-time monitoring data, the system dynamically manages bandwidth allocation and service prioritization. This includes not only ensuring a fair distribution of available bandwidth, but also making adaptive adjustments to the transmission quality of individual applications.

For instance, it may be reasonable to reduce several active 4K video streams to 1080p resolution—provided the applications in question support appropriate adaptation mechanisms. In such cases, the router can simulate a bandwidth constraint in order to prompt the applications to adjust their streaming quality accordingly.

In practice, these decisions are typically made using rule-based approaches or simple machine learning models, such as decision trees. While effective in many scenarios, such methods are often limited in their ability to generalize and adapt to more complex or dynamic network conditions.

(3) Operational Implementation in the Network

Implementation is achieved through targeted control of router interfaces and traffic queues. Established techniques such as Active Queue Management (AQM), widely supported in Linux-based systems, can be leveraged for this purpose. In addition, new standards like Wi-Fi 6 and Wi-Fi 7 provide enhanced control features that facilitate AI-driven optimizations.

Vendors such as Cisco, Aruba, and Juniper are already applying similar concepts in their hotspot solutions. Chip manufacturers including Qualcomm, Broadcom, and MediaTek are likewise beginning to integrate AI capabilities directly into their system-on-a-chip (SoC) architectures.

(4) Making QoE measurable

Quality of Experience (QoE) refers to the user’s perceived quality of an application—for instance, the audiovisual stability during a video conference or the responsiveness when switching between video streams.

Collecting objective QoE data in home networks remains a complex challenge. Traffic from applications such as Microsoft Teams, YouTube, or VoIP services is typically encrypted. As a result, the once-effective approach of Deep Packet Inspection (DPI) is increasingly reaching its limits and is considered inefficient in terms of processing overhead.

An alternative method involves identifying application-specific data flows using AI-based traffic classification and passively measuring their Quality of Service (QoS) parameters—latency, jitter, packet loss, and bandwidth—as they pass through the router.

These measurements allow for an indirect estimation of QoE using scoring models. Target values defined by application providers—for example, acceptable latency thresholds for real-time communication—serve as benchmarks for comparison.

Standardization efforts are underway in organizations such as the Broadband Forum (e.g., TR-421, TR-452) and IETF working groups (e.g., IPPM, NMRG). However, a universally accepted QoE metric has yet to be established.

Economic Reality: Margin Pressure as an Obstacle to Innovation

Despite technological advancements, a major hurdle remains at the economic level: the cost acceptance of service providers and the current pricing and licensing models used by SoC manufacturers and integrators, particularly given the high volumes involved.

The home gateway market is highly competitive, with low margins and significant price pressure. Devices must be procured in large quantities, often at subsidized prices, and typically have long depreciation cycles. Integrating advanced AI hardware would substantially increase device costs. As long as the value for end customers is not immediately clear or monetizable, many providers lack the financial incentive to adopt this technology. Moreover, viable business models for AI-powered home networking technologies are still underdeveloped.

Widespread adoption is only realistic once new service models, such as QoE-based premium tariffs, managed home services, or automated fault detection with reduced support costs, become established.

Conclusion

Although the technological foundations for an intelligent home network with automatic QoE management are already in place, widespread adoption remains a distant prospect. From a technical standpoint, the current use of various AI models often resembles medieval alchemy more than structured product development. What is lacking is the consolidation of AI approaches, standardized measurement methodologies, and seamless hardware integration.

On the market side, viable business models and clarity regarding data protection remain unresolved.

Only when technological maturity aligns with economic feasibility will the vision of a self-optimizing home network become a reality.

[1] So called Device Fingerprinting

[2] Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey; Mahmoud Abbasi, Amin Shahraki, Amir Taherkordi Computer Communications 170 (2021) 19–41

[3] Unmasking the Internet: A Survey of Fine-Grained Network Traffic Analysis; Yebo Feng, Jun Li, Jelena Mirkovic, Cong Wu, Chong Wang, Hao Ren, Jiahua Xu, and Yang Liu; IEEE Communications Surveys & Tutorials · January 2025