Energy-Efficient Dynamic and Spatiotemporal Spectrum Access via Spiking Reservoir Computing
Nima Mohammadi, Lingjia Liu, Yifei Song, Yang Yi · IEEE Transactions on Wireless Communications · 2025
This work presents an energy-efficient reinforcement learning (RL) solution based on Neuromorphic Computing (NC) to enable opportunistic spectrum access in partially observable wireless environments. To improve the energy efficiency of the underlying spectrum access strategy, we explore Neuromorphic Computing and adopt spiking neural networks. Additionally, the time-dependent aspect of the problem and the necessity for sample efficiency drive us to liquid state machines, a variant of reservoir computing. Nevertheless, a priori hyperparameter optimization of the spiking reservoir is essential for handling state- and time-varying inputs in RL agents; yet, this can undermine model robustness and impede deployment. In response, we examine homeostatic regulation for self-modulating the small-world reservoir’s dynamics, thereby maintaining desired near-chaotic behavior throughout operation. The RL model for opportunistic spectrum access is evaluated under both dynamic spectrum access (DSA), where agents identify temporal spectrum holes for transmission, and spatiotemporal spectrum access (SSA), where agents also aim to minimize coverage overspill without coordination or sharing location data. Numerical analysis demonstrates that the proposed model outperforms existing learning models in the literature for both DSA and SSA, while significantly reducing power consumption.