Channel-Knowledge Maps for Proactive Base-Station Association Across Mobility and Blockage Regimes
Keywords:
Channel-Knowledge Maps, Base-Station Association, Proactive Handover, Millimeter-Wave Communications, Mobility ManagementAbstract
The advent of millimeter-wave and terahertz communication systems has introduced unprecedented opportunities for ultra-reliable and low-latency communications. However, these high-frequency bands are severely susceptible to environmental blockages and dynamic mobility, leading to frequent radio link failures and suboptimal base-station association. Traditional reactive handover mechanisms, which rely on instantaneous signal measurements, struggle to maintain seamless connectivity in such volatile environments. This paper proposes a comprehensive framework utilizing Channel-Knowledge Maps to enable proactive base-station association across diverse mobility and blockage regimes. By decoupling the channel state information from immediate user locations and mapping it to geographical coordinates, Channel-Knowledge Maps provide the network with predictive awareness of the radio environment. This predictive capability allows the network to anticipate blockages and execute handovers before link degradation occurs. Through extensive system-level analysis, we investigate the construction methodologies of these maps, including spatial interpolation and predictive modeling based on crowdsourced environmental data. The study evaluates the proposed proactive association strategy under varying user velocities and blockage densities. Results demonstrate that the Channel-Knowledge Map-assisted framework significantly reduces ping-pong handovers and radio link failures while maximizing the overall network throughput compared to conventional reactive approaches. This research highlights the critical role of environmental awareness in future wireless networks, paving the way for intelligent, location-aware resource management and robust mobility support in next-generation communication architectures.
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Copyright (c) 2026 Andrew Ko, Wei Lei
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