MASSFormer: Mobility-Aware Spectrum Sensing using Transformer-Driven Tiered Structure

• URL: http://arxiv.org/abs/2409.17546v1
• Date: Thu, 26 Sep 2024 05:25:25 GMT
• Title: MASSFormer: Mobility-Aware Spectrum Sensing using Transformer-Driven Tiered Structure
• Authors: Dimpal Janu, Sandeep Mandia, Kuldeep Singh and Sandeep Kumar
• Abstract summary: We develop a mobility-aware transformer-driven structure (MASSFormer) based cooperative sensing method. Our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs) The proposed method is tested under imperfect reporting channel scenarios to show robustness.
• Score: 3.6194127685460553
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based cooperative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unlike existing methods, our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs)and addresses the complexities introduced by user mobility. The transformer architecture utilizes an attention mechanism, enabling the proposed method to adeptly model the temporal dynamics of user mobility by effectively capturing long-range dependencies within the input data. The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU and processes them in parallel using the SUtransformer network to learn the spatio-temporal features at SUlevel. Subsequently, the collaborative transformer network learns the group-level PU state from all SU-level feature representations. The attention-based sequence pooling method followed by the transformer encoder adjusts the contributions of all tokens. The main goal of predicting the PU states at each SU-level and group-level is to improve detection performance even more. We conducted a sufficient amount of simulations and compared the detection performance of different SS methods. The proposed method is tested under imperfect reporting channel scenarios to show robustness. The efficacy of our method is validated with the simulation results demonstrating its higher performance compared with existing methods in terms of detection probability, sensing error, and classification accuracy.

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