Federated Transfer Learning Based Cooperative Wideband Spectrum Sensing with Model Pruning

TL;DR

This paper introduces a federated transfer learning framework with model pruning for wideband spectrum sensing, enabling efficient, robust, and scenario-adaptive spectrum detection in wireless communication systems.

Contribution

It proposes a novel federated transfer learning approach with model pruning for wideband spectrum sensing, improving robustness and adaptability across scenarios.

Findings

FTL-WSSNet achieves good performance without local adaptation samples.

Model pruning enables fast model adaptation and inference.

The approach reduces hardware and computation costs.

Abstract

For ultra-wideband and high-rate wireless communication systems, wideband spectrum sensing (WSS) is critical, since it empowers secondary users (SUs) to capture the spectrum holes for opportunistic transmission. However, WSS encounters challenges such as excessive costs of hardware and computation due to the high sampling rate, as well as robustness issues arising from scenario mismatch. In this paper, a WSS neural network (WSSNet) is proposed by exploiting multicoset preprocessing to enable the sub-Nyquist sampling, with the two dimensional convolution design specifically tailored to work with the preprocessed samples. A federated transfer learning (FTL) based framework mobilizing multiple SUs is further developed to achieve a robust model adaptable to various scenarios, which is paved by the selective weight pruning for the fast model adaptation and inference. Simulation results demonstrate that the proposed FTL-WSSNet achieves the fairly good performance in different target scenarios even without local adaptation samples.