Intelligent 6G Edge Connectivity: A Knowledge Driven Optimization Framework for Small Cell Selection

TL;DR

This paper introduces a knowledge-driven optimization framework for small cell selection in 6G networks, improving latency and packet loss through adaptive, data-driven user association.

Contribution

It proposes a novel KDN framework integrating optimization and machine learning for intelligent user association in dense 6G small-cell networks.

Findings

Latency reduced by 30-45% in high-mobility scenarios

Packet loss decreased by over 35% under congestion

Outperforms conventional association methods in simulations

Abstract

Sixth-generation (6G) wireless networks are expected to support immersive and mission-critical applications requiring ultra-reliable communication, sub-second responsiveness, and multi-Gbps data rates. Dense small-cell deployments are a key enabler of these capabilities; however, the large number of candidate cells available to mobile users makes efficient user-cell association increasingly complex. Conventional signal-strength-based or heuristic approaches often lead to load imbalance, increased latency, packet loss, and inefficient utilization of radio resources. To address these challenges, this paper proposes a Knowledge-Defined Networking (KDN) framework for intelligent user association in dense 6G small-cell environments. The proposed architecture integrates the knowledge, control, and data planes to enable adaptive, data-driven decision-making. Small-cell conditions are modeled using queueing-theoretic indicators that capture traffic load and waiting-time dynamics. Based on these indicators, a joint optimization objective reflecting latency and packet loss is formulated and solved via Lagrangian relaxation to obtain globally guided association policies. These optimization outcomes are then used to supervise a lightweight Learning Vector Quantization (LVQ) model, enabling fast and scalable inference at the network edge. Extensive NS-3 simulations under varying mobility, traffic load, packet size, and network density demonstrate that the proposed approach consistently outperforms conventional baselines. The framework reduces average latency by 30-45% in high-mobility and heavy-traffic scenarios and decreases packet loss by more than 35% under congestion. The results confirm that combining optimization-driven knowledge with lightweight learning enables scalable, QoS-aware user association for future dense 6G networks.