Embodied intelligent spectrum sensing techniques in dynamic electromagnetic environments

The performance degradation and decision rigidity of conventional spectrum sensing in dynamic electromagnetic environments, arising from reliance on fixed acquisition and offline training under distribution shifts, were addressed. An embodied spectrum sensing modeling and policy computation approach was proposed. An unified system model integrating environment state, observation, knowledge representation, and embodied actions was constructed, and spectrum sensing was formulated as a sequential decision process over knowledge states. An immediate reward was designed with recognition of correctness, latency, and resource cost as the core factors. The value function and the state action value function were defined, the Bellman optimality equations were derived, the existence form and structural characteristics of the optimal policy were characterized, and a policy computation method based on value function updates was developed. An embodied decision framework for modulation recognition was established, and adaptive matching to diverse environments was enabled through multi model selection. Simulation results show that, compared with a static single model baseline, the proposed scheme significantly improves overall recognition accuracy, environmental adaptability, and error recovery capability. The proposed scheme provides an effective approach for robust spectrum sensing in dynamic electromagnetic environments.