Adaptive Frequency Planning Model in Decentralized FMCW Radar Networks with Channel Conflict Awareness

Abstract

The article addresses the problem of interference-free frequency planning in multi-user FMCW radars, taking into account mutual interference between signals. Analytical conditions are proposed to prevent the overlapping of chirp signals in the time-frequency domain, and an optimization problem is formulated to minimize the occupied spectrum under constraints on allowable frequencies and transmission time slots. Particular attention is given to modeling the chirp frequency slope as a key parameter that determines the level of mutual interference. For the first time, a priority-weighted frequency function approach is proposed, allowing adaptive spectrum allocation based on current traffic conditions, signal types, or external constraints. A multi-objective optimization model for the cost function is developed, simultaneously considering frequency preference, conflict level, and spectral accuracy. The study justifies the use of spectral monitoring and local decision-making for decentralized systems. This approach paves the way for the development of more intelligent and self-configuring FMCW systems capable of adapting to environmental changes without centralized coordination. The obtained results can be applied to develop adaptive planning algorithms in autonomous transport systems, intelligent sensor networks, and next-generation cognitive radio systems.